1

Sustainable AI in Robo-Advisory Platforms: Opportunities and Challenges in the Digital
Finance Era

Manish Handa

University Canada West
MBAR 661 - Academic Research Project
Supervisor: Dr. Sherry Abdellatif
Aug 22nd, 2025

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Abstract
Financial services are experiencing revolutions of digital technologies, and FinTech-inspired
changes in the industry are challenging the traditional approaches to the management of finances
through automation, artificial intelligence, and data-driven decision-making. This has lowered
costs, brought the services to a greater number of people, and opened innovation to areas never
previously possible under the traditional banking models. In this paradigm shift, robo-advisory
platforms have become a major force of disruption since they provide affordable, scalable, and
accessible investment services, unlike an ordinary advisor. Sustainability provisions, such as
ESG portfolios, are also being added to such platforms as investor focus changes. However,
certain major hurdles remain because of dangers on cybersecurity, trust, inconsistent regulation,
and inaccurate ESG reporting, among others, making its adoption challenging.
In this study, a qualitative conceptual research design is adopted using secondary literature
referenced in peer-reviewed articles, regulatory reports, and industry publications. A scoping
review and a documentary analysis have been carried out to help determine what has been done
so far and identify gaps in existing studies. The evidence points to three key themes, including
risk (cybersecurity, legal, and privacy vulnerability), consumer protection (trust, transparency,
and stakeholder requirements), and governance (fiduciary role, algorithmic responsibility, and
RegTech/CSR). Comparative analysis highlights the possibilities of robo-advisory platforms in
terms of democratization of access, cost reduction, and the possibility to integrate ESG-specifics,
as well as discloses the weaknesses of robo-advisory platforms regarding building trust, ensuring
resilience, and protection against greenwashing.
To fill these gaps, this paper suggests the Trust-Cybersecurity-Sustainability (TCS) Framework,
which combines psychological (trust), technical (cybersecurity), and ethical (sustainability /ESG)
aspects of the robo-advisory platform adoption. The framework focuses on the mediating effect
of the investor behaviour and expounds on the previous models of adoption by introducing
concepts of governance and sustainability. It has contributions to theory, adding knowledge to
adoption and trust frameworks, and practice, helping to inform financial institutions, regulators,
and policymakers in designing robo-advisory platforms that are sustainable, resilient, and
transparent.

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Table of Contents
Abstract ......................................................................................................................................2
Acknowledgements .....................................................................................................................6
1.

Introduction: Background and Objectives ............................................................................7
1.1.

1.1.1.

Overview of the Chosen Research Area ...............................................................7

1.1.2.

Relevance to Business/ Management Context.......................................................8

1.1.3.

Research Questions ..............................................................................................8

1.1.4.

Theoretical Background of Research Area ...........................................................9

1.2.
2.

Background .................................................................................................................7

Research Aims and Objectives ................................................................................... 10

Literature Review .............................................................................................................. 11
2.1. Overview ........................................................................................................................ 11
2.2 Opportunities for Robo-Advisory Platforms .................................................................... 12
2.2.1 Democratization of Finance ...................................................................................... 12
2.2.2 Efficiency and Objectivity ........................................................................................ 12
2.2.3 Personalization through AI ....................................................................................... 13
2.2.4 Integration with ESG and Sustainability .................................................................... 13
2.2.5 Financial Inclusion, DEI, and Sustainable Development Goals (SDGs) ..................... 14
2.3 Challenges of Robo-Advisory Platforms .......................................................................... 15
2.3.1 Trust Deficits ............................................................................................................ 15
2.3.2 Cybersecurity Vulnerabilities .................................................................................... 15
2.3.3 Regulatory and Ethical Concerns .............................................................................. 16
2.4 Trust and Cybersecurity: An Interdependent Relationship ............................................ 16
2.5 Sustainability and ESG Integration .................................................................................. 17
2.5.1 Investor Demand for ESG ......................................................................................... 17
2.5.2 Robo-Advisory Platforms as Scalable ESG Enablers................................................. 17
2.5.3 Challenges of ESG Integration .................................................................................. 17
2.6 Theoretical Lenses ........................................................................................................... 18
2.6.1 Technology Acceptance Model (TAM) and UTAUT ................................................ 18
2.6.2 Trust Theory ............................................................................................................. 18
2.6.3. Stakeholder Theory .................................................................................................. 18
2.6.4. Sustainable Development Goals and ESG ................................................................ 19
2.6.6. Corporate Social Responsibility (CSR) Theories and Models ................................... 20
2.7 Summary ......................................................................................................................... 22

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3.

Methodology ..................................................................................................................... 23
3.1 Research methods ............................................................................................................ 23
3.2.1 Phase 1: Scoping Review of the Literature ................................................................ 24
3.2.2 Phase 2: Documentary and Policy Analysis .................................................................. 24
3.3 Analytical Strategy .......................................................................................................... 25
3.4 Scope and Limitations ..................................................................................................... 25

4.

Results .............................................................................................................................. 26
4.1.

Using the Theoretical Lens to Structure the Synthesis ................................................ 27

4.1.1.

Adoption theories (Applied) ............................................................................... 27

4.1.2.

Trust theory (Applied)........................................................................................ 28

4.1.3.

Stakeholder and Sustainability Perspectives (Applied) ....................................... 28

4.1.4.

Governance and Regulation Models (Applied) ................................................... 28

4.1.5.

Integrated Approach (TCS in Use) ..................................................................... 28

4.2.

Qualitative Data Analysis .......................................................................................... 29

4.2.1.

Theme 1: Risks ...................................................................................................... 30

Subtheme 1.1. Cybersecurity Risks .................................................................................... 31
Subtheme 1.2. Legal and Regulatory Risks ........................................................................ 31
Subtheme 1.3. Data Privacy Risks ..................................................................................... 31
4.2.2.

Theme 2: Consumer Protection .............................................................................. 32

Subtheme 2.1. Trust and Transparency .............................................................................. 32
Subtheme 2.2. Complaints and Redress ............................................................................. 32
Subtheme 2.3. Stakeholder Needs ...................................................................................... 32
4.2.3 Theme 3: Governance ................................................................................................... 33
Subtheme 3.1. Fiduciary Duty ........................................................................................... 33
Subtheme 3.2. Algorithmic Accountability (Explainability & Bias) ................................... 33
Subtheme 3.3. RegTech and CSR ...................................................................................... 34
4.2.4 Comparative Opportunities ........................................................................................... 34
4.2.5 Comparative Challenges ........................................................................................... 34
4.2.6 ESG and Sustainability ............................................................................................. 35
4.2.7 Synthesis and Emerging Themes ............................................................................... 36
Persistent challenges in Robo-advisory Platforms .............................................................. 36
5.

Discussion ......................................................................................................................... 37
5.1.

Gap Analysis of the Existing Literature...................................................................... 38

5.1.1 Limited Multi-Method Studies .................................................................................. 39

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5.1.2 Limited Empirical Validation of Trust and Cybersecurity Link ................................. 40
5.1.3 Insufficient Research on ESG and Sustainability Integration ..................................... 40
5.2 Contribution of This Study to Address the Gaps .............................................................. 41
5.2.1. Integration of Trust and Cybersecurity ..................................................................... 42
5.2.2. ESG and SDGs as a Value-Adding Lens .................................................................. 42
5.2.3.

A Holistic Comparative Framework ................................................................... 43

5.3 Proposed Framework ....................................................................................................... 44
5.3.1. Key Components of the Framework: ........................................................................ 44
5.4. The Conceptual Framework ............................................................................................ 46
The TCS Triad Framework for Robo-Advisory Platforms ..................................................... 48
6.

Limitations ........................................................................................................................ 51

7.

Recommendations and Future Research ............................................................................. 53
7.1 Recommendations ........................................................................................................... 53
7.2 Future Research ............................................................................................................... 54
7.2.1. Empirical Validation of the TCS Framework ........................................................... 54
7.2.2. Cross-Cultural Comparisons .................................................................................... 54
7.2.3. Behavioural Finance and Robo-Advisory Platforms ................................................. 55
7.2.4. AI Explainability and Trust ...................................................................................... 55
7.2.5. Blockchain and Decentralized Finance (DeFi) ......................................................... 55
7.2.6. Longitudinal Studies ................................................................................................ 55

8. Conclusion ............................................................................................................................ 55
References ................................................................................................................................ 58
Appendix A: Robo-Advisory Platforms in Canada .................................................................... 65

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Acknowledgements
First, I would like to appreciate all the people who have supported and encouraged me in
coming up with this research work. First, I would like to thank my academic supervisor, who
offered abundant support, positive feedback, as well as encouragement to develop this project.
I would also like to extend my gratitude to my colleagues, my classmates and peers, who
helped me with interesting discussions about the dynamic area of FinTech and robo-advisory
platforms provided. Their views helped me broaden my knowledge both practically and
theoretically regarding the topic of my study.
I also wish to thank my family, who exhibited patience, understanding, and constant
support during the period of undertaking research. This gave them the strength and determination
to work on this manuscript because they believed in me.
Finally, I would like to acknowledge the authors, practitioners, and institutions whose
scholars and professionals have developed the fragments of scholarly and professional work that
were the foundation of my literature review and analysis. This research would not have been a
reality without them.

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1. Introduction: Background and Objectives
1.1.Background
1.1.1. Overview of the Chosen Research Area
The financial market is beginning to undergo a gigantic digitalization procedure, in which
the convergence of artificial intelligence (AI) and amplified data analytics will initiate the same.
The wealth management is changing due to the robo-advisory platforms, especially the
computerization of investment advice and management of portfolios through websites. Roboadvisory platforms were initially meant to be an inexpensive alternative to a financial planner in
the post-2008 recession era and are a technology that employs algorithmic decision-making in
order to lessen the entry obstacle and democratize the financial service (Roh et al., 2023).
Robo-advisory platforms have evolved to be extremely dynamic, meaning that they are
now able to integrate the variables of ESG preferences, tax optimization, and risk profiling
(Abraham et al., 2019). And the shift to a global one is increasing, and AUM assets markets are
expected to have reached USD 2 trillion (and above) by 2026 (Abraham et al., 2019). This
innovation will reiterate their contribution to the evolution of the future of finance.
Nevertheless, despite their potential, there is some worrying news about robo-advisory
platforms. According to the critics, algorithm-based counselling is impersonal, unheard, and
unobserved morally (Yi et al., 2023). The absence of cybersecurity and the distrust of consumers
are also considered negative factors to increase their usage. These questions are not to be talked
about at a technical level but deeper research related to the theoretical and practical aspects of
digital financial advice.

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1.1.2. Relevance to Business/ Management Context
The case of robo-advisory platforms as disruptors and facilitators of financial services is
relevant to business and management because it can be used as a springboard into the future and
a means to prepare business and management to deal with financial service disruption. On one
hand, they interfere with the old model of business by providing scaled as well as small-cost
advisory services and, hence, competing with the traditional wealth managers. Conversely, they
help businesses to match the trends in the world, like digitalization and sustainability.
It seems that the robo-advisory platform is both an opportunity and a threat to
management. They present institutions with an avenue through which they can increase the
number of customers, cut expenses, and augment their competitiveness. Meanwhile, they also
depend on AI, which incurs management difficulties due to responsibility and regulatory and
ethical considerations when making decisions. Moreover, investors are placing growing
importance on financial institutions to such an extent that they incorporate ESG in their advice to
investors. Robo-advisory platforms, therefore, are instrumental in aligning the investment
strategies and sustainability agendas, if they can address the trust and cybersecurity issues
(Nguyen et al., 2023).
1.1.3. Research Questions
This study raises the following research questions:
Research question (RQ) 1: What are the opportunities and challenges when using roboadvisory platforms compared to human financial advisors and traditional investment platforms?
Research question (RQ) 2: How do robo-advisory platforms promote sustainability goals and
integrate ESG factors into their services?

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1.1.4. Theoretical Background of Research Area
The research has a theoretical basis based on various sources of literature, namely
behavioural finance, trust theory, cybersecurity risk frameworks, and sustainability.
Theories and Models
The use of the Technology Acceptance Model (TAM) and Unified Theory of Acceptance
and Use of Technology (UTAUT) provides an understanding of robo-advisory platform adoption
by the users, where trust and perceived security are the most important variables (Roh et al.,
2023). In addition to this, the agency theory also indicates how investors and advisors, whether
human or algorithmic, might be conflict-of-interest parties that fostered the focus on monitoring,
transparency, and accountability.
Opportunities: Sustainability, SDGs, and ESG
The support of the United Nations Sustainable Development Goals (SDGs) can be
achieved with the help of robo-advisory platforms, which have the potential to support the SDGs
by making portfolio standards more accessible and democratic and implementing ESG standards
in their portfolios. The ESG integration by a robo-advisory platform has the potential to redirect
capital to sustainable companies and industries, which will support a responsible investment
(Vasile & Manta, 2025). Robo-advisory platforms enable the smallest of investors to access
ESG-themed portfolios on a large scale, allowing personal investors to contribute their bit to
bigger sustainability changes.
Challenges: Trust and Cybersecurity Risks

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Despite these opportunities, challenges still exist. Trust forms a key adoption determinant
since there is a certain degree of risk and uncertainty in the nature of the financial decision
process. The research has shown that clients would be cautious about relying on AI-based
platforms, because they feel that they are mysterious and lack human touch (Yi et al., 2023).
Furthermore, the threat posed to robo-advisory platforms is characterized by extremely high
security risks, including information leakage, fraud through algorithms, and fraud (Adekoya et
al., 2025).
Trust-Cybersecurity Relationship
The trust and the cybersecurity risk are mutually dependent. Publicized attacks can erode
the user’s trust, which can be enhanced with effective security tools. The mediating factor
between risk perception and adoption intention, through which cybersecurity resilience is a state
of long-term acceptance of robo-advisory platforms, is mediated by trust. This interaction
typifies one of the most important theoretical perspectives of this paper through the linking of the
concept of digital security and the trend of behaviour adoption.
1.2. Research Aims and Objectives
The general purpose of the research under consideration is currently to critically discuss
the opportunities and challenges that the use of robo-advisory platforms is open to, paying
particular attention to the issues connected to sustainability, the risks of cybersecurity dangers,
and integrating ESG. The research proposal will be useful to provide a contribution to the
scholarly and management debate by bridging the gap in theoretical and practical knowledge.
The targeted objectives are as under:

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1. To examine the relative prospects and threats of robo-advisory platforms compared to
human financial advisors and the traditional investment platform.
2. To understand how robo-advisory platforms incorporate sustainability targets in their
operations, especially with ESG investment strategies.
3. To understand the current relationships between trust, cybersecurity, and the use of roboadvisory platforms.
4. To pinpoint gaps that are found in existing literature and suggest a conceptual framework
for future studies.
5. To make recommendations to policymakers, practitioners, and researchers on what can
be done to improve trust, resilience, and sustainability in robo-advisory platforms.
2. Literature Review
2.1. Overview
The increase of robo-advisory platform is one of the most revolutionary changes in the
field of financial technologies (FinTech) during the last decade. This new breed of technologyenabled platforms built on artificial intelligence (AI), machine learning, and algorithmic
decision-making has upended the wealth management sector in terms of value rather than
lowering costs (Roh et al., 2023). Being a disruptive innovation, robo-advisory platforms not
only undermine the purpose of typical human advisors but expand the investor-advisor
relationships as well. But on the one hand, opportunities in terms of democratizing finance and
sustainable investing accompany them, and on the other hand, trust and cybersecurity issues, as
well as regulatory sufficiency concerns, appear (Farahani & Ghasemi, 2024; Yi et al., 2023).
The synthesis of this prior research comprises four key themes: (1) robo-advisory
platform opportunities; (2) challenges, with emphasis on trust and cybersecurity; (3)

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sustainability and ESG integration; and (4) adoption and risk-perception theoretical lenses. The
review identifies these zones by mapping them and provides the basis on which the voids of
existing information can be filled.

2.2 Opportunities for Robo-Advisory Platforms
2.2.1 Democratization of Finance
Among the most-discussed opportunities is the democratization of financial advice.
Compared to human advisors, robo-advisory platforms can save users high expendable money
management fees by fully automating portfolio construction, rebalancing, and tax optimization,
thereby making financial advice affordable to more citizens who would otherwise not be able to
afford real-world human financial advisors. It has been demonstrated that millennial and digitally
native investors are especially attracted to robo-advisory platforms because they are cheap and
easily accessible (Vasile & Manta, 2025).
In addition to that, robo-advisory platforms may eliminate entry barriers: they may open
accounts with a minimum balance requirement or no requirement whatsoever. Early American
reporting mentioned that one year-younger and less financially educated investors gravitated
towards sites like Betterment and Wealthfront; later empirical research demonstrates that lower
financial literacy and younger age are strong predictors of robo-advisory platforms usage (De
Crescenzo, 2017; Yi et al., 2023; Piotrowski and Orzeszko, 2023; Roh et al., 2023). This will be
in tandem with the broader SDG 10 of minimizing inequalities in financial literacy and access.
2.2.2 Efficiency and Objectivity
The other good aspect is efficiency. The use of algorithm-based systems is capable of
handling large data streams, hence being more efficient in optimizing the allocation of assets as

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opposed to human advisors. Conversely, robo-advisory platforms generate advice that possesses
certain behavioural biases, such as overconfidence or herding, in which more consistent advice is
given with increasing power (Nain and Rajan, 2023). This objectivity enhances the performance
of the portfolio because it relies on quantitative models as opposed to subjective opinion.
2.2.3 Personalization through AI
Investment strategy personalization by user risk profile and time duration, and
preferences have also been made possible by the assistance of AI by a robo-advisory platform.
Machine learning models evolve all the time to the behaviour of the investor and provide more
personal advice (Nguyen et al., 2023). What is more, natural language processing (NLP)
interfaces enhance the experience of the users, making financial tips more interactive and userfriendly.
2.2.4 Integration with ESG and Sustainability
The most important emerging opportunity is the mission of robo-advisory platforms
concerning the promotion of sustainable investing. Several works demonstrate that more
investors are interested in ensuring that portfolios are aligned with ESG factors (Vasile & Manta,
2025). Robo-advisory platforms can scale up ESG integration due to the ability to provide predesigned sustainable portfolios based on big data analytics assessment of environmental and
social performance across businesses. It is associated with SDG 13 (Climate Action) and SDG
12 (Responsible Consumption and Production).
With the startups like Wealthsimple and Nutmeg already being able to show how roboadvisory platforms could be employed in a way that considers ESG products and enables them to
direct investments to sustainable businesses, it is clear that they will continue to play a role going

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forward. To get a comprehensive summary of the current robo-advisory platforms in Canada and
how they would fit in a comparative analysis of their offerings in terms of integrating ESG, refer
to Appendix A.
2.2.5 Financial Inclusion, DEI, and Sustainable Development Goals (SDGs)
One of the factors that drives financial inclusion also comes with the use of Roboadvisory platforms in lessening the entry barrier that may have previously locked out small and
underrepresented investors. In contrast to the conventional advisor that has a high minimum
requirement, robo-advisory platforms are affordable, low-cost and accessible services that help
to democratize finance (Abraham et al., 2019; Onabowale, 2025). This aligns with the principle
of Diversity, Equity, and Inclusion (DEI) in financial services because this enables women,
minorities and households in the lower income bracket to also get access to digital wealth
management tools.

Improving inclusion is associated with the following Sustainable Development Goals
(SDGs), where the contribution of robo-advisory platforms is relevant. A case in point is that
SDG 10 (Reducing Inequalities) is advanced by their low prices and availability, and SDG 12
(Responsible Consumption and Production) and SDG 13 (Climate Action) by sustainable
investment portfolios (Faradynawati & Söderberg, 2022; Vasile & Manta, 2025). Moreover, the
robo-advisory platforms support Sustainable Development Goals (SDGs) 8 (Decent Work and
Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure) since they foster the use
of digital innovations and expand the range of access to capital markets.

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Such lens would not only see robo-advisory platforms as enhancing efficiency and
sustainability but also as a tool advancing two key aims such as channeling inclusive growth and
robo-advisory platforms become an important instrument toward supporting both financial equity
and global sustainable development.

2.3 Challenges of Robo-Advisory Platforms
2.3.1 Trust Deficits
Trust becomes a repeated motif in the literature and has always been named a point
preventing the adoption of robo-advisory platforms. According to Yi et al. (2023), skepticism has
resulted because the users tend to view algorithmic advice as non-transparent, or a black-box. In
contrast to human advisors, robo-advisory platforms are unable to express empathy or to instill
confidence in the client in times of turmoil in the market. Such a lack of emotional quotient
generates a lack of trust, which is especially true among older or less-tech-savvy investors.
In addition, trust is neither equal nor absolute as it is based on cultural, demographic, and
experience variables. Research indicates that younger and technology-savvy clients are more
willing to accept algorithmic recommendations, but older investors still want to interact with
another person (Piotrowski & Orzeszko, 2023).
2.3.2 Cybersecurity Vulnerabilities
Robo-advisory platforms are vulnerable to cybersecurity threats given the digital aspect
of their nature. The significant risks include data leakage, identity theft, and tampering with
algorithms and algorithms thus eroding investor trust. When financial platforms lose the trust of
users due to a single cyberattack, as Adekoya et al. (2025) observe, this may take years to
restore. Since the issues of money are a candid subject, there are great stakes involved.

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Furthermore, there are new kinds of systemic risk as a result of the AI-powered systems.
The financial instability may spread rapidly because of the links between platforms (algorithmic
errors or problems caused by malicious attacks). Robo-advisory platforms contrast with human
advisors in that they rely fully on the integrity and security of data.

2.3.3 Regulatory and Ethical Concerns
The fast development of robo-advisory platforms is difficult to keep up with by
regulators. The issue of fiduciary duty, liability when the computed solution introduces an error,
or how the decision-making process of a particular AI is transparent, is not established. Other
researchers suggest increased explainability of the algorithms to enable accountability (Schwarcz
et al., 2025).
There are also central issues of ethics involved. Unless exercised, robo-advisory
platforms will be stuck in algorithmic discrimination, where certain groups of investors are
discriminated against. Absence of human judgment in decision-making also clouds ethical
dilemma and can be applied in long-term judgment of finances.

2.4 Trust and Cybersecurity: An Interdependent Relationship
The security threats and cybersecurity mingle with trust. One of the greatest breaches of
data is a pure assault on trust, and a solid cybersecurity existence could be a foundation of a
greater level of trust. The literature indicated that trust is the mediating variable in the
relationship between the perceived risk and the intention to adopt (Yi et al., 2023). In other
words, investors will be willing to adopt robo-advisory platforms in case they feel that the
platforms are secure and trustworthy.

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A number of models include the Technology Acceptance Model (TAM) and Unified
Theory of Acceptance and Use of Technology (UTAUT) that emphasize the role of perceived
security in influencing behavioural intent (Roh et al., 2023). These models affirm that
development of trust depends not only on the technical capability to resist but also on
transparency, regulatory adherence and communication to the users.

2.5 Sustainability and ESG Integration
2.5.1 Investor Demand for ESG
One of the key trends in the last few years is the increase in demand for ESG-aligned
investments. Sustainability is important to the millennials and Gen Z investors, who represent the
major user segment of robo-advisory platforms. Research indicates that ESG integration is not
optional anymore, but rather it is becoming a necessity for those providers in the financial sector
(Faradynawati & Söderberg, 2022).
2.5.2 Robo-Advisory Platforms as Scalable ESG Enablers
Robo-advisory platforms can promote sustainable finance through the scale of multi-asset
portfolios focused on ESG. By using big data and AI, they can observe sustainability reports,
greenhouse gas emissions, and corporate governance of firms and construct ESG rankings.
Having this capability allows an agent in the mass market to align portfolios up to sustainability
objectives without extensive financial literacy (Faradynawati & Söderberg, 2022; Vasile &
Manta, 2025).
2.5.3 Challenges of ESG Integration
Nonetheless, ESG integration is not problem-free. Vice scholars point out the problems
of greenwashing, when labels added to ESG do not imply actual sustainability in the company

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(Nguyen et al., 2023). Moreover, ESG criteria can be oversimplified by robo-advisory platforms,
which do not reflect environmental and social performance complexity. It begs the question of
how credible and efficient the robo-advisory platform on ESG will be.

2.6 Theoretical Lenses
2.6.1 Technology Acceptance Model (TAM) and UTAUT
The TAM model focuses on perceived usefulness and perceived ease of use as a factor
that determines the adoption of technology (Roh et al., 2023). These dimensions are mediated by
the trust and perceptions of security in the robo-advisory context. UTAUT also adds on to the
adoption theory to include the factor of social influence and facilitating conditions (Roh et al.,
2023).
2.6.2 Trust Theory
The trust theory is used as a basis through which investors avoid making risky decisions
and embrace AI-powered financial services. The act of assuming vulnerability through positive
expectations of the actions of another individual is referred to as trust (Afroogh et al., 2024).
Applying it here, it illuminates the role of transparency, reputation and the strength of
cybersecurity in adoption.
2.6.3. Stakeholder Theory
According to the stakeholder theory, financial service providers have a responsibility
ahead of them to attend to both the shareholders' returns and other assurances to the wider
society (Faradynawati & Söderberg, 2022). The robot-advisory platforms incorporating the ESG
criteria can be considered as the practical implementation of the stakeholder theory, which aims
to make the investment portfolio correspond to the ethical and sustainability requirements.

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2.6.4. Sustainable Development Goals and ESG
Robo-advisory platforms have the potential to transform retail investors’ understanding
of ESG preferences into rules and tilts on specific themes with the sustainability goals of the
United Nations (SDGs) through AI-driven screening and portfolio construction at scale. In
practice, these platforms may map allocations to such goals as SDG 10 (Reduced Inequalities)
via low fees, low minimums, and digital onboarding to widen access; SDG 12 (Responsible
Consumption and Production) through controversy screen and resource efficiency; and SDG 13
(Climate Action) via low-carbon indices and exposure to green bonds, enabled by links to SDG
8/9 regarding innovation and infrastructure as the key to sustainable growth (Abraham,
Schmukler, & Tessada, 2019). Without clear governance and disclosures, including explainable
ESG logic, auditable data pipelines and safeguards against greenwashing, and credible digitalscale SDG alignment, other than a marketing label, will be elusive, on par with the evolving
FinTech expectations and trust requirements of AI-enabled advice services (Hsieh, Chang, & Suhan, 2024; Afroogh et al., 2024).
2.6.5 Modern Portfolio Theory
The theory is built on the foundations of Financial Economics discipline. The theory was
introduced by Harry Markowitz in 1952. It discussed the importance of investors' attention to
risk-return trade-off. Theoretically, the higher the risk the higher return or vice versa. Markowitz
highlighted the significant role of diversification in asset allocation to construct the "efficient
frontier" across asset classes (fixed income, equity, derivatives, cryptocurrencies, real estate,
etc...) as an investment strategy to reduce risk without reducing expected return. The aim is to
ensure portfolio optimization and construct the assumption that investors are rational and
markets are efficient (Donadelli, et al. 2025, Markowitz, H., 1952). When applying the theory to

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robo-advisory platforms, it is evident that most prominent North American platforms (such as
Wealthfront or Betterment) use robo-advisory platforms to conduct investor profile
questionnaires and feed it into the algorithm to run automatic rebalancing based on the riskreturn profile of the investor (Betterment, 2025; Wealthfront, 2025).

2.6.6. Corporate Social Responsibility (CSR) Theories and Models
Whereas Stakeholder Theory (Freeman, 1984) provides an explanation of who
organizations are answerable to, the varieties of CSR theories elaborate on what the
responsibilities of businesses are towards these stakeholders and how the same can be
undertaken through the adoption of practices. Therefore, CSR models are supplementary to
Stakeholder Theory: the former focuses on who firms should consider, whereas the latter
determines how they have to meet the various expectations of different sides. This greater
context of governance is applicable in robo-advisory platforms since responsibility is not limited
only to the investors; other stakeholders, such as regulators, society, and the environment, are
also involved.
Probably the most popular framework is the Pyramid of CSR put together by Carroll,
where four aspects of responsibility are defined: economic, legal, ethical, and philanthropic
(Carroll, 1991). Such categories, as mentioned by Carroll later, constitute an entirety, with the
ethics being present at all levels, trade-offs being unavoidable, and applicability in the
international scope evident (Carroll, 2016). When applied to robo-advisory platforms, economic
responsibility is the ability to offer a low-cost and broadly available digital investment service;
the legal responsibility rests on adherence to financial regulations and data-protection laws; the
ethical responsibility consists of transparency, fairness, and the prevention of greenwashing; and

21
the philanthropic responsibility of allowing financial literacy and democratizing sustainable
investing.
The Triple Bottom Line (TBL) model (Elkington, 1998) is an addition to the above
Carroll model in the sense that sustainability is being embedded in strategy with the formulation
of people, planet, profit. The latest research proves that the integration of TBL is not a matter of
choice anymore but a source of organizational resilience and sustainable competitive advantage
because when the economic viability and social equity and environmental sustainability are
achieved, long-term success is attained (Sapsanguanboon, Faijaidee, & Potasin, 2025). In the
robo-advisory setting, this implies a balancing between profitability and ESG portfolios that
contribute to climate targets (planet) and making wealth management accessible to a broader
range of people (people), in addition to monetary returns (profit).
Lastly, stakeholder engagement and stakeholder governance would mean something
within the scope of CSR. Stakeholder Theory has been growing in prominence as a framework in
the context of raising and resolving challenging ethical and trust-related questions in various
sectors, and bibliometric data show that Stakeholder Theory is an influential theory to guide
CSR-based decision-making (Azevedo Silva da Costa et al., 2025). With robo-advisory
platforms, this interrelation illuminates that, in addition to satisfying adoption and
trustworthiness conditions, platforms are also supposed to uphold social legitimacy by adopting
responsible AI, transparent integration of ESG and multiple stakeholder accountability. This
study is also based on the foundations of the AI-Ethics CSR Governance framework to address
the opportunities and compliance challenges and ethical concerns of the use of AI in the
banking sector (Abdellatif, 2025)

22
Overall, the theories of CSR and models contribute to the overall theoretical
underpinning of the research because of their widening of the stakeholder-centered models of
adoption towards a more comprehensive understanding of governance. The integration of the
CSR Pyramid as proposed by Carroll, the TBL, as well as stakeholder-based CSR governance in
this study places robo-advisory platforms in their place as socio-technical systems, the legality of
which is contingent on their profitability, regulation-compliant behaviour, ethical and sustainable
developmental actions.
2.7 Summary
There are two lines of thought that appear in the literature: On the one hand, the attractive
prospects of robo-advisory platforms are related to the value of cost-effectiveness,
democratization, individualization, and ESG integration. Conversely, they are confronted with a
serious problem of trust, cybersecurity, and adequacy of regulations.
Most importantly is the nature of the interplay between trust and cybersecurity proves to
be a deciding factor on adoption. Although sustainability and integration with ESG make roboadvisory platforms more attractive, some questions of transparency and greenwashing need to be
solved to increase their efficiency.
This review forms the rationale of future research to fill these gaps, especially by
conducting multi-method empirical research to validate the adoption patterns, measure the
cybersecurity resilience, and evaluate the adoption of ESG integration in practice.

23
3. Methodology
3.1 Research methods
The research design is qualitative and conceptual, based on pre-existing theoretical
frameworks and secondary sources, in accordance with critical-review and agenda-setting
research styles in areas of rapid change (Hsieh, Chang, & Su-han, 2024; Vukovic et al., 2025).
Conceptual designs are mostly suited when no empirical evidence is able to accumulate as fast as
technologies are developing (Ruben & Ortiz, 2025; Rjab, Mellouli, & Corbett, 2023). We also
make references to adoption constructs that were defined in empirical investigations of the use of
AI-powered robo-advisory platforms (Piotrowski & Orzeszko, 2023; Nguyen et al., 2023).
The theoretical background has several points of view that make up its principles. Roh,
Park, and Xiao (2023) use UTAUT to understand the aspects of adoption of robo-advisory
platforms, while Yi et al. (2023) utilize it to establish the role of trust, usability, and knowledge
in the establishment of the investor behaviour stage. Meanwhile, Faradynawati and Söderberg
(2022) discuss the concept of sustainability preference in online investment platforms, and
Schwarcz, Baker and Logue (2025) introduce the issue of governance and fiduciary duty. As it
can also be seen in the paper by Abdellatif (2025), the ideas of AI ethics and corporate social
responsibility (CSR) could be integrated into financial governance with the help of the
conceptual framework of RegTech. Collectively, these studies serve to inform the evolution of
Trust-Cybersecurity-Sustainability (TCS) as a framework that asserts adoption as an outcome of
trust, cybersecurity risk, and integration of sustainability.
3.2 Data Collection Methods and Data Sources
The research is solely based on secondary sources, and it combines research articles
published by academicians and regulatory agencies, policy reports, and industry studies.

24
3.2.1 Phase 1: Scoping Review of the Literature
Issues of power, trust, and control concerning AI-powered robo-advisory platforms were
captured through a scoping review. There were four broad themes that came out.


Confidence and trust in the informed decision on the investor trust that is foundation on
knowledge, usability, and transparency (Yi et al., 2023; Sidat and Matchaba-hove, 2021).



The threats of cybersecurity, vulnerabilities, and the effects of cyber attacks on investor
confidence (Adekoya, Atlam, & Lallie, 2025; Hasanah et al., 2024).



One of the opportunities of sustainable finance democratization, as well as the risk of
greenwashing, has become sustainability and ESG integration (Faradynawati &
Söderberg 2022; Vasile & Manta 2025).



Regulation and governance- fragmented structures and governance problems (Baker &
Dellaert, 2018; Schwarcz et al., 2025).

3.2.2 Phase 2: Documentary and Policy Analysis
The second step incorporated a documentary review of regulation structures, policy
reports, and legal reviews. The articles by Baker and Dellaert (2018), Jedlicbkova (2024) and
Ruben and Ortiz (2025) address the topic of fiduciary responsibility in robo-advice and focus on
the ethical and governance concerns of autonomous AI systems. The article by Vučinić and
Luburić (2024) provides comparative perspectives on FinTech regulation. The example provided
by Abdellatif (2025) is particularly applicable to Canada and can be applied when changing AI
ethics and CSR into financial governance. Notably, the Office of the Superintendent of Financial
Institutions (OSFI) has newly updated its Guideline E-23 to incorporate AI and machine learning
into the scope of its enterprise-wide model risk management policy framework with the final

25
guidance to be released in 2025, marking a significant change towards managing AI-related risks
in Canadian financial institutions
This step was completed using content and critical discourse analysis to understand how
the concepts of trust, cybersecurity-based risks, and sustainability are addressed in policy and
governance discourse.
3.3 Analytical Strategy
To be rigorous, this study relies on conceptual integration and triangulation. ESG was
mainly associated with academic literature on adoption (Roh et al., 2023). Policies and legal
documents provided an outlook on fiduciary responsibility, liability, and algorithmic liability
(Schwarcz et al., 2025; Shin-Ru Hsieh et al., 2024). Industry reports and investor studies
provided testimony of sustainable investment behaviour (Abraham et al., 2019; Faradynawati &
Söderberg, 2022).
Incorporating these views will help in building the TCS framework, which connects trust,
cybersecurity resilience, and sustainability as the pillars influencing the adoption of the roboadvisory platforms.
3.4 Scope and Limitations
The present research is conceptual and exploratory. Although it lacks primary data
collection, the rigor is maintained by relying on peer-reviewed sources of information, regulation
analysis and industry opinion. An example is the bibliometric analysis by Fahruri et al. (2023)
that allows mapping adoption research, or the power of conceptual integration as shown by
Abdellatif (2025), who proved its usefulness as a policy-relevant tool.

26
The integration of these approaches leads to the TCS framework that is theoretically
sound, policy-relevant, and practically useful to resolve the ethical and technical dilemmas of the
AI-powered robo-advisory platforms subject to cybersecurity threats.

4. Results
This section will sum up the findings of the material reviewed so as to show how it
answers the research questions to some level. In contrast to the literature review, which
summarized previous studies, Results are grouped around converging evidence themes and how
such patterns address: RQ1 (opportunities vs. challenges of robo-advisory platforms) and RQ2
(how robo-advisory platforms integrate ESG and promote sustainability). As there was no
original collection of primary data, the so-called results are provided in the form of thematic
findings synthesized based on existing theories, models, and sources of evidence. The findings
reveal the possibility and the limitations of AI-enabled robo-advisory platforms, especially
concerning adoption rate, trust, security threats following cyberattacks, consumer protection,
governance, and sustainability.
According to Grand View Research (2025) North America robo advisory market
highlights


The North American robo advisory market generated a revenue of USD 1,524.7 million
in 2022.



The market is expected to grow at a CAGR of 28.2% from 2023 to 2030.

27
Figure 1. Growth of North American Robo Advisory Market, from 2018 to 2030 (US$M)

Source: North America Robo Advisory Market Size & Outlook, 2030

4.1. Using the Theoretical Lens to Structure the Synthesis
This section applies the lenses introduced in Section 2.6 to organize findings, rather than
re-stating the theories. We use adoption models to group opportunities, trust/cybersecurity to
explain barriers, and stakeholder/CSR/governance to distinguish credible ESG integration from
performative claims. Each lens is tied explicitly to RQ1 and RQ2.
4.1.1. Adoption theories (Applied)
Rather than re-explaining TAM/UTAUT, we use them to cluster results: lower fees, ease
of onboarding, and automated rebalancing map to perceived usefulness/ease (opportunities),
while algorithm aversion and low financial literacy map to residual barriers.
Synthesis for RQ1: opportunities are conditional, materializing only when enablers (usability +
support) offset perceived risk.

28
4.1.2. Trust theory (Applied)
We use the mediating variable of trust to transform platform characteristics into
adoption/retention: apparent security measures and informative logicality - increased perceived
reliability - increased intention to adopt and remain.
Synthesis for RQ1: trust deficits erase cost advantages after breaches/opacity, explaining why
some platforms stall despite strong pricing.
4.1.3. Stakeholder and Sustainability Perspectives (Applied)
Stakeholder/CSR/TBL lenses are used to question ESG credentials in our results: where
screening is clear, pipelines of data are trackable and connections to SDGs definitive, ESG seems
substantive; where disclosures are limited, risk of greenwashing is higher.
Synthesis for RQ2: Robo-advisory platforms can scale ESG access, but credibility hinges on
process transparency, not labels.
4.1.4. Governance and Regulation Models (Applied)
We operationalize governance as levers to transform risk as perceived into risk as
managed: clarity of fiduciary duty, model-risk management, explainability/test-for-bias, and
redress avenues.
Synthesis for RQ1: These levers reduce frictions to adoption that are caused by cyber/privacy
and opacity. Connection to RQ2: Verifiable ESG integration is based on the same controls.

4.1.5. Integrated Approach (TCS in Use)
The TCS (Trust-Cybersecurity-Sustainability) framework integrates these lenses: robust
cybersecurity enables trust; credible ESG sustains trust; governance spans all three. We use TCS

29
to interpret the patterns in Section 4.2 and to motivate the recommendations, directly addressing
RQ1 (opportunities vs. challenges) and RQ2 (credible ESG integration).
4.2.Qualitative Data Analysis
The thematic analysis of the literature and regulatory documents revealed three themes of
risks and consumer protection, governance, and additional comparative prompts on
opportunities, challenges, and ESG sustainability. This study proposes a typology based on
thematic analysis, including themes and subthemes derived from the critical literature review
and policy analysis of academic, legal, and industry sources.

Table 1. Thematic Analysis (Designed by author)

1. Risks

1.1 Cybersecurity Risks - Data Leaks,

Adekoya et al. (2025);

Hacking, Identity Theft, and Platform

Hasanah et al. (2024)

vulnerabilities lower the trust.
1.2 Legal/Regulatory Risks -

Baker & Dellaert (2018);

Disorganized and vague liability systems

Schwarcz et al. (2025)

discourage adoption.
1.3 Data Privacy Risks : Fears of

Jedličková (2024)

consenting to and storing their data, as well
as tracking down information sensitivity
abuse.
2. Consumer

2.1 Trust & Transparency: Investors

Yi et al. (2023); Piotrowski &

Protection

want someone to explain to them the

Orzeszko (2023)

30
recommendations that AI powered systems
is providing.
2.2 Complaints & Redress: Limited
methods of dispute resolution and

Abraham et al. (2019)

accountabilities.
2.3 Stakeholder Needs: Expansion in the
Nguyen et al. (2023);
services provided along the line of
Faradynawati & Söderberg
personalization, green integration and
(2022)
inclusion.
3. Governance

3.1 Fiduciary Duty: It is uncertain that the

Baker & Dellaert (2018)

robo-advisory platforms can uphold their
fiduciary duty.
3.2 Algorithmic Accountability : Must

Schwarcz et al. (2025);

demand fairness, explainability and non-

Bianchi & Brière (2021)

biasness.
3.3 RegTech & CSR : Harnessing
Abdellatif (2025); Ruben &
RegTech and CSR to make ethical
Ortiz (2025)
adoption.

4.2.1. Theme 1: Risks
Cybersecurity is the risk that is rated as the highest to robo-advisory platforms. Adekoya
et al. (2025) assess the multidimensional costs of cyberattacks in financial services, whereas
Hasanah et al. (2024) identify the shortcomings in the risk-profiling systems that could expose

31
the users to mismanagement. Legal and regulatory risks exist because of the inconsistent
systems, and fiduciary obligations and liability remain a controversial topic (Baker & Dellaert,
2018; Schwarcz et al., 2025). Moreover, data security issues, especially related to consent,
storage, and algorithm abuse, restrict the trust of users (Jedličková, 2024).
Subtheme 1.1. Cybersecurity Risks
The risk of fraud, malicious hacks, and injections of Robo-advisory AI-based platforms is
high. Cyberattacks disrupt the stability of data integrity and availability and affect financial
operations negatively, and investor confidence (Adekoya et al., 2025).
Subtheme 1.2. Legal and Regulatory Risks
The limitations exist due to the lack of homogeneous regulations and unequal duty
enforcement of the fiduciary role. These jurisdiction-related disparities in the appropriateness
and reporting rules undermine investor trust and platform dispensation (Baker & Dellaert, 2018;
Schwarcz et al., 2025).
Subtheme 1.3. Data Privacy Risks
Lack of strong consent management and transparency in data practices is a drag on
adoption. In order to avoid misuse of sensitive financial information, researchers observe the
necessity to retain more control over the storage, retention and non-abusive utilization of data.
(Jedličková, 2024).
Synthesis/RQ link: Cyber, legal/liability, and data-privacy vulnerabilities are more of a systemic
than an isolated risk. The direct response to RQ1 (challenges) is this tendency (the absence of
credible security control and accountability, the advantage of cost and access is not being turned
into a sustainable adoption).

32
4.2.2. Theme 2: Consumer Protection
The problem of consumer protection is closely related to trust and transparency. Financial
participants are usually reluctant to trust black-box models with explanations (Piotrowski &
Orzeszko, 2023). Redress systems and complaint systems are not developed as the clients do not
know their path to seek dispute (Abraham, Schmukler, & Tessada, 2019). Moreover,
stakeholders are asking more personalized services and sustainable investment opportunities,
most notably millennials, who have a majority share in the robo-advisory adoption (Nguyen,
Chew, Muthaiyah, Teh, & Ong, 2023; Faradynawati & Söderberg, 2022).
Subtheme 2.1. Trust and Transparency
Based on studies, the key predictors of adoption are perceived usability, trustworthiness,
and knowledge. The other problem with black-box opacity is that it reduces the confidence of the
investors. (Piotrowski & Orzeszko, 2023; Yi, Rom, Hassan, Samsurijan, & Ebekozien, 2023).
Subtheme 2.2. Complaints and Redress
Robo-advisory platforms in general do not follow clear lines of accountability, and
complaint feedback is usually unavailable. This lack of consumer protection level decreases trust
in the governance of the platforms (Abraham et al., 2019; Baker & Dellaert, 2018).
Subtheme 2.3. Stakeholder Needs
Investors are demanding more custom portfolios that are aligned to ESG interests. It is
proven that the demand related to sustainable investment options is very strong among
millennials (Nguyen et al., 2023; Faradynawati & Söderberg, 2022).
Synthesis/RQ link: The two most important areas of concern, as discussed in the literature, are a
lack of transparency (opaque recommendations) and weak redress mechanisms. The rationale
behind RQ1 was that trust and transparency are never soft, but scale.

33
4.2.3 Theme 3: Governance
Governance issues revolve around fiduciary responsibilities, algorithmic responsibility
and instilling ethics into practice. There are still questions about whether the fiduciary standard
that is applied by robo-advisory platforms is equivalent to human advisors (Baker & Dellaert,
2018). There is growing concern with the idea of algorithmic accountability, and demand for
explainability and fairness of decision-making (Schwarcz et al., 2025). The principles of
RegTech and CSR are emerging as the trends of making financial creations complemented by
ethical principles (Abdellatif, 2025; Ruben & Ortiz, 2025). The prudential supervisor of Canada
recently highlighted the efficiency of regulations, without affecting resilience, which states that it
will be ready to step in as risks become apparent in the financial system environment directly
pertaining to the bank-affiliated robo-advisory platforms (Office of the Superintendent of
Financial Institutions [OSFI], 2025).
Subtheme 3.1. Fiduciary Duty
The automation of the platforms raises the question of their comprehensiveness with
fiduciary duties like suitability, disclosure, and acting in the best interest of the client (Baker &
Dellaert, 2018; Schwarcz et al., 2025).
Subtheme 3.2. Algorithmic Accountability (Explainability & Bias)
Regulators are demanding more answers to questions of explainability, fairness, and bias
testing of algorithms. The identified algorithms-related problems, namely, accountability and
algorithm aversion, are accessible through pathways proposed by the given XAI solutions
(Schwarcz et al., 2025; Bianchi & Brière, 2021).

34
Subtheme 3.3. RegTech and CSR
Embedding RegTech technologies with the principle of CSR enables the systems to be
extremely transparent and ethical in their governance. Compliance-automation enhances control,
and CSR-oriented oversight liberation can make the innovation of robo-advisory platforms
personified in adherence to social trust (Abdellatif, 2025; Ruben & Ortiz, 2025).
Synthesis/RQ link: Alignment technologies, such as embedded fiduciary principles,
explainability, and testing bias, translate perceived platform risk into actual risk. This indicates
the way limitations in RQ1 can be addressed.
4.2.4 Comparative Opportunities
Robo-advisory platforms are economically effective, and their fees range between 0.25%
and 0.50% of AUM per year as opposed to 1.0% of AUM in the case of human advisors
(Abraham et al., 2019). They open financial markets to the middle-income and younger investors
by lowering the entrance fee. The personalization powered by AI allows for the portfolio to be
optimized in real-time (Farahani & Ghasemi, 2024; Han & Ko, 2025). The integration of ESG
enhances their value creation even more (Vasile & Manta, 2025).
4.2.5 Comparative Challenges
Barriers of adoption still exist. A lack of trust hinders the degree of trust in algorithms
(Afroogh et al., 2024). The rate of vulnerability regarding cybersecurity is still high (Adekoya et
al., 2025). Uncertainty is the result of regulatory disintegration (Schwarcz et al., 2025; Hsieh et
al., 2024). Simplified risk-profiling is a distorted understanding of complex investor
requirements (Hasanah, Sudarso, & Koesrindartoto, 2024). Lastly, robo-advisory platforms do
not give the same empathy as human advisors do in turbulent markets (Tan, 2020; D’Acunto &
Rossi, 2020).

35
4.2.6 ESG and Sustainability
The ESG inputs and portfolios are applied in Robo-advisory platforms based on the UN
Sustainable Development Goals (Vasile & Manta, 2025; Faradynawati & Söderberg, 2022).
They also make sure that sustainable finance is made available to everyone, including retail
investors, as entry is not very difficult (Yi et al., 2023). However, it increases the chances of
inconsistent reporting of ESG and greenwashing, which reduces the credibility of ESG
(Ramazan & Ashraf, 2025).
Synthesis/RQ link (sets up RQ2): Robo-advisory platforms can make ESG more scale-friendly,
although the quality and disclosure of the provided data are biased, transparent ESG reasons and
auditing of inputs are impossible, and claims of sustainability are already falling under the
greenwashing umbrella. This nuance is central to RQ2.
Critical Synthesis: It is demonstrated that robo-advisory platforms can bring access to ESG
menus, but this has a varying impact. Most provide products that replicate wide indices with
light exclusions that create little actual tilt in the real world. To conduct this study, ESG
integration is only believed to be credible when four conditions are met: (1) the screening logic is
explicable (what is screened in/out and why); (2) look-through holdings reflect material portfolio
tilts versus a non-ESG benchmark (not de minimis); (3) data pipelines, and ratings sources are
not only auditable and consistent but reported with measures; and (4) stewardship outcomes
(votes, engagements, escalations, exits) are also reported and measured. Without them, the threat
becomes the democratization of sustainable investing, not re-labelling-i.e., greenwashing, which
then becomes detrimental to trust and uptake within the TCS frame.

36
4.2.7 Synthesis and Emerging Themes
Comprehensively, robo-advisory platforms are affordable, scalable, personalized, and
environmentally conscious, yet have weaknesses regarding trust, cybersecurity risks, and
regulation. Human financial advisors also maintain advantages in empathy and reassurance
during a crisis, which also implies a complementary relationship. This literature mapping
indicates well-researched areas of adoption and efficiency and little work on ESG and regulation,
with new interest in XAI, RegTech, fraud detection, and governance of sustainable AI (Fahruri,
Rusmanto, Dezie, & Warganegara, 2023; Abdellatif, 2025).
Persistent challenges in Robo-advisory Platforms
Both literature and practitioners highlighted challenges in developing trust, remedying
vulnerabilities in cybersecurity, and managing fragmented regulatory landscapes. Other issues
like the risk of data privacy breaches, lack of transparency in algorithms, and non-uniformity in
ESG reporting, among others, only erode trust of the users and decrease adoption (Adekoya et
al., 2025; Baker & Dellaert, 2018; Schwarcz et al., 2025; Jedličková, 2024).

Cross-theme synthesis: There are always conditional dependencies between the opportunities
(cost, access, personalization, ESG menus) and: (i) plausible cybersecurity/oversight to maintain
a trust relationship, and (ii) articulate, transparent ESG pipelines to maintain claims of
sustainability. It is a place where the TCS framework can be used to structure both the RQs
(opportunities vs. challenges) and RQ2 (credible ESG integration).

37
5. Discussion
To present a systematic review of the current state of research, this paper elicits a
typology of the robo-advisory literature along 11 key dimensions, such as adoption, cost, trust,
cybersecurity, governance, ESG integration and fraud detection. The typology categorizes the
research outputs into three: dense (well-researched), underexplored, and fragmented/missing, all
of which are noticed in Table 2. This organized mapping indicates that although much work has
been done in the field of adoption, trust, cost accessibility (Roh et al., 2023; Piotrowski &
Orzeszko, 2023; Abraham et al., 2019), there are still huge gaps in the field of ESG validation,
cybersecurity resilience, and harmonization of global governance (Nguyen et al., 2023; Schwarcz
et al., 2025). More precisely, the so-called fraud detection is fundamentally nonexistent in the
robo-advisory literature, which also reflects the disordered character of the latter (Fahruri et al.,
2023).
Based on this typology, published articles are categorized as dense, underexplored and
missing/fragmented, and the result shows that most of the research includes the works of
adoption, trust, and cost dimensions and not much is done to investigate ESG validation,
cybersecurity resilience, and fraud detection studies.

Table 2. Typology of Robo-Advisory Platforms Literature Across Research Dimensions

38

The typology developed by it not only confirms the fragmented environment of the field,
but also interdependence among trust, cybersecurity and sustainability. The identified gaps serve
as the parts of the component of the TCS Triad Framework that unite these three dimensions into
a model of the adoption and the legitimacy of robo-advisory platforms (see Figure 2).

5.1.Gap Analysis of the Existing Literature
A review of the existing literature on robo-advisory platforms implies the existence of the
extant literature that is abundant in terms of magnitude but has a fractured focus. A lot of the
literature has followed two apparently unrelated directions, one being the technical side of
algorithms, cybersecurity and regulatory compliance, and the other being the uptake of
consumers in terms of the constructs of trust, ease-of-use and perceived value. The notable
aspect concerns the lack of a common framework that captures the interaction that happens
among the fields to define the legitimacy and sustainability of the robo-advisory platforms.
Trust, though widely recognized as the key to adoption, is hardly ever analyzed alongside the

39
concrete, cybersecurity practices and governance mechanisms through which it could be
maintained in the long-term (Baker & Dellaert, 2018; Roh et al., 2023; Yi et al., 2023; Adekoya
et al., 2025). Likewise, sustainability, in this case through the combination of ESG portfolios, is
underrepresented in the retail robo-advisory market where the issues of greenwashing and
authenticity have been overlooked and have an impact on sustainable investing democratization
(Faradynawati & Söderberg, 2022; Vasile & Manta, 2025). Another requirement limiting the
literature is methodological issues, since most of the studies rely on cross-sectional surveys and
lack longitudinal or multimethod studies that could provide an insight into the way trust
develops, decays, or is re-built after the occurrence of cyber incidents (Piotrowski & Orzeszko,
2023). To this is the absence of a serious approach to fiduciary duties, the separation of powers
with respect to regulation, and the materialization of algorithmic responsibility, which are the
pillars of investor protection and platform accountability (Schwarcz et al., 2025; Hsieh et al.,
2024). Even solutions that might appear to aid in alleviating algorithm aversion, such as
explainable AI (XAI), are hardly implemented in the context of robo-advisory (Bianchi & Brière,
2021; Schwarcz et al., 2025). Taken together, these gaps also point to the need to develop a more
holistic, interdisciplinary framework not only highlighting trust, cybersecurity resilience, and
sustainability as key enterprises, but also acknowledging them as mutually defining areas of
robo-advisory platform uptake and ongoing viability. Several gaps can be identified as follows:
5.1.1 Limited Multi-Method Studies
A preliminary critical literature review revealed that a large majority of empirical studies
into the area of robo-advisory platforms are based on single methods of investigating them, i.e.
surveys or case studies (Piotrowski & Orzeszko, 2023; Yi et al., 2023). There are a few studies
that mix quantitative (e.g. adoption rates, portfolio results) with qualitative (e.g. investor trust

40
stories) data. This limits the wealth of knowledge on how investors make decisions and how trust
is perceived against technological reliability.
As an example, risk-profiling questionnaires are extensively used to segment customers
(see Risk Profiling Questionnaires in Investment, uploaded source), but there is little evidence
about whether these instruments account for nuances of behavioural finance or whether this kind
of instrument is perceived as reliable in digital-only environments.

5.1.2 Limited Empirical Validation of Trust and Cybersecurity Link
Even though various authors consider trust to be the foundation of the robo-advisory
platform adoption (Yi et al., 2023; Nguyen et al., 2023), they do not study the connection
between trust and cybersecurity risks. Trust is usually conceptualized as a fixed entity, when in
fact it is dynamic: a concept that can be greatly affected by the perception of data security,
system resilience, and regulatory protection (Baker & Dellaert, 2018).
The existing literature tends to ignore the importance of cyber incidents, regardless of the
connection to the loss in investments, in reducing investor confidence beyond the level justified
by the incident. This gives an unfilled research gap at the nexus of cybersecurity practices, trustbuilding systems and the practice of adoption.
5.1.3 Insufficient Research on ESG and Sustainability Integration
Most of the research focuses on institutional sustainable finance, but not retail-oriented
robo-advisory platforms, where sustainability integration is an emerging topic in robo-advisory
platforms (Faradynawati & Söderberg, 2022). There is poor empirical evidence on:

41


The perception of retail investors on the ESG-aligned robo-portfolios.



Whether the potential to democratize sustainable finance that robo-advisory platforms
have arises naturally or is more a matter of rebranding existing ESG products.



How the algorithmic design can contribute to preventing greenwashing in the long term
and allowing the attainment of the actual sustainability alignment.
This break prevents the creation of a multifaceted picture of the potential of robo-

advisory platforms in the context of the SDGs application to individual finance settings.
5.2 Contribution of This Study to Address the Gaps
This research has solved the already identified gaps in the literature by using a multimethod approach that will help identify and use a combination of a scoping review, policy
analysis, and developing a conceptual framework. Based on a systematic assembly of research
results of scholarly examinations, regulatory reports, and industry surveys, the research achieved
a thorough summary of the opportunities and issues of robo-advisory platforms.
This study could be able to compare the literature with typology of adoption, risks and
governance sustainability based on the secondary data collected. This has demonstrated areas of
interest-both models of adoption and efficiency studies, but also unserved topics such as
cybersecurity risks, fiduciary duties and integration with ESG.
The research also added to the body of knowledge, in that there was an introduction to
the holistical perspective of the technology adoption theories (e.g., TAM, UTAUT), trust
frameworks, and the sustainability perspective as introduced within the framework of a
regulatory governance. It demonstrates that a lack of trust, regulatory fracturing and risk of data

42
privacy intersect the element of sustainability to pose threats and opportunities in the transition to
the robo-advisory platforms.
It is anticipated that these contributions of the study will add value to the literature
through actual practices in the financial services industry through regulation of regulations and
sustainable investments. In the instance of institutions, the findings are useful in identifying how
to integrate the mechanisms of trust-building and ESG strategies in the digital finance models.
The data offer greater compliance framework common grounds to regulators across jurisdictions.
The implication of the study to the policymakers is on the way to democratize sustainable
finance without the threat of instability. The paper will contribute to the existing body of
knowledge regarding the robo-advisory platforms by providing a three-dimensional evaluation of
the features in the light of the assumptions of trust, cybersecurity, and sustainability
(ESG/SDGs).
5.2.1. Integration of Trust and Cybersecurity
Through examining the overlaps of distrust and cybersecurity risk, this paper
demonstrates that trust is a psychological and technical phenomenon. The fact that robo-advisory
platforms appear to be less affected by the misuse of information is pertinent to the adoption
views and much more important than the strictly anthropocentric idea of being a human versus a
robot in terms of the specific use of individual knowledge being more prone to hacking attempts.
5.2.2. ESG and SDGs as a Value-Adding Lens
The future of this study is the ability of robo-advisory platforms to democratize
sustainable finance by including ESG filters at scale. Robo-advisory platforms have the potential

43
to operationalize SDGs at the retail level as compared to traditional advisors; hence, the paper is
one of the first to systematically interconnect the algorithmic finance business model with SDGs.
Are robo-advisory platforms promoting sustainability or just changing their name? Low
fees and automated onboarding make it easier for more people to get involved, but there is still a
gap in impact when ESG portfolios (a) rely on unclear vendor ratings, (b) only show small
carbon/governance deviations compared to the underlying benchmark, and (c) don't give much
information about sustainability. In TCS terms, weak S (sustainability credibility) makes T
(trust) weaker, no matter how strong cybersecurity is. The strategy is to move from "ESG as a
label" to "ESG as a legitimate process," which means having clear rules, data that can be
checked, and results that can be measured. This changes robo-advisory ESG from being about
marketing to being about sustainability backed by good governance.
5.2.3. A Holistic Comparative Framework
In contrast to the disjointed prior works, the present paper compares robo-advisory
platforms, human advisors, and traditional platforms to each other and shows that robo-advisory
platforms are complements and not substitutes. This analysis is of use to both academics and
practitioners.
In the process, this study contributes to a nexus between the trust theoretical model
(Afroogh et al., 2024), the current trend in technology adoption (TAM/UTAUT), and
sustainability (ESG/SDGs).

44
5.3 Proposed Framework
Using the analysis, this research proposes the Trust-Cybersecurity-Sustainability (TCS)
framework of Robo-Advisory platform adoption. This framework combines a psychological,
technical and ethical aspect into one.
5.3.1. Key Components of the Framework:
5.3.1.1. Trust Dimension
Borrowing the model of trust elaborated by Afroogh et al. (2024), it is possible to include
foundations of ability (algorithmic competence), benevolence (alignment with client interests),
and integrity (transparency in recommendations).
5.3.1.2. Cybersecurity Dimension
The cybersecurity dimension encompasses the perception of risk by the investors, the
resilience of the platform, regulatory conformity, and the security of the information relating to
clients. One of the biggest threats to robo-advisory platforms is associated with cybersecurity
risks that include data breaches, identity thefts, manipulation of algorithms, and others, which are
seen to compromise the investor confidence (Adekoya, Atlam, & Lallie, 2025). Cybersecurity
and trust are mutually amplifying: the more resilient digital environment is perceived, the higher
is the probability of adoption (Afroogh et al., 2024). The significance of uniformity in the
fiduciary treatment, alongside the liability regimes and data security, are that the perceived
credibility reduces in the cases when the jurisdictions enact inconsistent fiduciary treatment
(Schwarcz et al., 2025; Jedličkova, 2024). Such profound treatment of cybersecurity, in its turn,

45
not only ensures security to financial operations, but it also acts as the producer of long-term
trust towards AI-driven finances.
5.3.1.3. Sustainability Dimension (ESG/SDGs)
The sustainability aspect is committed to the connection between ESG portfolios and
each other, and their compliance with the United Nations Sustainable Development Goals
(SDGs) and the protection against greenwashing. The sustainable democratization of finance
became possible due to robot-adviser systems because it is ethically affordable to scale an ESG
portfolio, and it is widely accessible (Vasile and Manta, 2025; Abraham, Schmukler, and
Tessada, 2019). Still, all is not that sunny in ESG integration; simplified guidelines and
inefficient disclosures can create an illusion of sustainability and misdirect the whole process
(Nguyen et al., 2023). According to the researchers, the main driving force behind a plausible
adoption of ESG is the need to possess transparent reporting, explainable AI models, and good
data governance (Faradynawati & Söderberg, 2022; Hsieh, Chang, & Su-han, 2024). By turning
ESG into a rigorous process, robo-advisory platforms will be more ethical and will result in the
growth of the level of trust of the investors, which will put financial innovation in the same
category as sustainability issues.
5.3.1.4. Mediating Role of Investor Behaviour
The behaviour of investors is a key mediating variable in the adoption of robo-advisory
platforms. The aspect of cybersecurity guarantees, integration of sustainability, and
trustworthiness of the platform are those issues that differ based on risk appetite, financial
literacy, and behavioural biases. Literature shows that the rate of adoption is higher among
younger and more digitally literate investors than among older generations because they do not

46
trust the recommendations provided by algorithms and do not know them well (Yi et al., 2023;
Piotrowski & Orzeszko, 2023). Preliminary data support the idea that millennial and Gen Z
investors are more likely to demand ESG integration than other investors because of their valueoriented investing preferences (De Crescenzo, 2017). Therefore, trust, cybersecurity, and
sustainability, as glorified by any investment behaviour are only worth as much as they lead to
practical adoption by their investors.
5.3.1.5. Outcome: Adoption and Retention
Trust and cybersecurity, together with sustainability, directly influence both adoption and
customer retention in robo-advisory platforms. Technical competence, ethical and felt security
influence adoption and retention, which is orchestrated by providing the technical competence,
ethical and felt security on a longitudinal basis (Afroogh et al., 2024). Robo-advisory platforms
have opened the door to financial advice to a broader scope of people, yet maintaining this kind
of clientele implies constant reminding of fairness, transparency, and resilience (Abraham,
Schmukler, & Tessada, 2019). According to literature, this area of retention has been
underdeveloped, and researchers discussed that human advisors still have advantages in empathy
and reassurances during a crisis (Tan, 2020; D’Acunto and Rossi, 2020). Therefore, embedded
trust (i.e., explainable AI, cybersecurity soundness, and ESG integrity) processes are critical
towards their transition to committed engagements and client retention.
5.4. The Conceptual Framework
This study introduced a conceptual framework, the Trust-Cybersecurity-Sustainability
(TCS) Framework, combining dimensions of adoption, trust, governance, and sustainability. The
conceptual framework addresses a critical gap in the existing literature by realizing that trust,

47
digital resilience, and sustainability integration are all mutually interdependent elements that are
key to the successful operation of robo-advisory platforms, especially within the emerging digital
finance landscape of Canada.
Validation was achieved through triangulation of the scoping review insights with
empirical themes extracted from industry whitepapers and regulatory reports, enhancing the
framework’s credibility and relevance. This framework contributes to both theory and practice
by offering a stakeholder-rich and complex setting.
This framework for ethical and sustainable adoption contributes to theory by expanding
current adoption models to ethical and sustainability factors and to practice through a modelling
framework applied by institutions, regulators, and policymakers. It is made to be stakeholderrich, flexible, and interdisciplinary, and thus, such technological innovation becomes compatible
with a fiduciary responsibility, cybersecurity precautions, and ESG responsibility.
Financial institutions and policymakers are encouraged to adopt the framework as a
guiding cross-disciplinary model for robo-advisory platforms that are trustworthy, resilient and
sustainability-focused. Its application is not restricted to Canada but can eventually be used all
over the world to construct global standards in balancing efficiency with responsibility in digital
finance.

48
Figure 2. The proposed TCS Triad Framework for Robo-Advisory Platforms

Note. The conceptual framework presented in this study was developed by the author as part of
their MBA research project, under the academic guidance and industry-informed expertise of
Dr. Shahinaz H. Abdellatif, who served as the subject matter expert (SME)
The TCS Triad Framework for Robo-Advisory Platforms
The TCS Triad Framework complements the preceding study by bringing together three
interdependent dimensions, Challenges/Risks, Trust, and Sustainability Opportunities to
understand how the usage of robo-advisory platforms can be adopted, legitimized, and retained
over time. Research in this area is still loose and, in some ways, dichotomous, with one body of
work dedicated to the technicalities of cybersecurity and compliance (Adekoya, Atlam, & Lallie,
2025; Afroogh et al., 2024) the other in terms of user endorsement as well as confidence (Yi et

49
al., 2023; Piotrowski & Orzeszko, 2023), with a third currently making its mark on ESG
integration and sustainable finances (Faradynawati & Söderberg, 2022). However, these three
views are seldom combined, and it remains unanswered how the three interrelate with each other
in practice. The TCS Triad Framework fills this gap by conceptualizing robo-advisory platforms
as socio-technical systems where their viability must be related to robo-advisory platforms
managing risks, building trust, and the sustainability of robo-advisory platforms must be
embedded in their design and governance.

The first dimension, Challenges/Risks, recognizes that threats to security extend past
cybersecurity violations to data privacy threats, algorithmic bias, disproportional enforcement of
the law, bad governance, and legal risks, including cross-border liability and fiduciary duty
enforcement. Also, it is linked to risks connected to sustainability, such as SDGs 12 and 13 being
threatened by greenwashing or by energy-intensive and water-provisioning algorithms. This
problem is the so-called carbon footprint of AI, where the energy being spent and the amount of
water utilized to train large models are enormous and can take away the climate goals the roboadvisory platforms are supposed to be propagating (Bolón-Canedo et al., 2024). Data breaches,
identity theft, and algorithm manipulation have also been listed as some of the most necessary
cautions to digital advice, shaking investor confidence in the process (Adekoya, Atlam, & Lallie,
2025). The literature helps us understand that strong cybersecurity resilience positively correlates
with adoption by minimizing the perceived risk and demonstrating the reliability in digital space
(Afroogh et al., 2024). Meanwhile, the disparity in the enforcement of fiduciary duty on a crossjurisdictional level undermines trust, so there is an importance of a strong regulatory structure
and enforcement authority (Schwarcz et al., 2025; Jedličková, 2024).

50

The second dimension, Sustainability Opportunities, demonstrates that ESG integration
and the United Nations 17 Sustainable Development Goals (SDGs) can be used to gain higher
legitimacy in robo-advisory platforms. It seems that robo-advisory platforms have a unique
opportunity to democratize sustainable finance by scaling to offer low-cost/accessible ESG
portfolios (Vasile & Manta, 2025; Abraham, Schmukler, & Tessada, 2019). The most
problematic, however, is weak disclosures and simplified ESG standards that can be used as an
attempt at greenwashing and kill the credibility and resulting trust in investors (Nguyen et al.,
2023). According to the researchers, in order to achieve credible ESG adoption, there must be
transparent reporting, explainable AI models, and strong data governance (Faradynawati &
Söderberg, 2022; Hsieh, Chang, & Su-han, 2024). To expand on this, Bolón-Canedo et al. (2024)
advocate the implementation of green AI, the models of machine learning and greener algorithms
that will incur lower costs of computation and will still be accurate. These practices can make
robo-advisory platforms consistent with SDG 13 by lessening their impact on the environment.
Strict implementation of ESG is likely to empower more ethically amenable robo-advisory
platforms, allowing financial innovation to play a role in meeting sustainability objectives, such
as SDGs 7, 8, 9, & 10, and EDII.

The third dimension, Trust, based on Ability (competence), Benevolence (alignment with
client interest), and Integrity (transparency in recommendations), and influenced by the vendor,
underlying systems, and the type of robo-advisory platform, examines the competence of the
platform as a factor of adoption and retention, investor interests and efficient communication.
This is mediated by investor behaviour: the younger crypto-savvy generations are more easily

51
accessed by robo-advisory platforms, and older clients are less comfortable through their relative
lack of familiarity and general lack of confidence with algorithmic systems (Yi et al., 2023;
Piotrowski and Orzeszko, 2023). At the same time, millennials and Gen Z investors are
particularly perturbed about the integration of ESG and are ready to invest in the assets that
would allow them to contribute to your financial performance with their own values (De
Crescenzo, 2017). This can leave such behavioural conditions clear, whereby trust is not in a
vacuum but instead platform and investor dependent.

The operation space of the robo-advisory platform is at the intersection of these three
dimensions, where validation measures, risk management, and the EDGE principles (Ethics,
Data Governance, and Explainability) decrease the risk of non-compliance audit, and clientcentred portfolio construction (TORI: time horizon, objectives, risk tolerance, investment
knowledge) and ESG integration are developed. Adoption levels are caused by perceptions of
competence, resilience, and sustainability fit, and retention is determined by the endurance of
fairness, transparency, and reliability (Afroogh et al., 2024; Tan, 2020; D’Acunto and Rossi,
2020). The TCS Triad Framework, in such a manner, brings together diverse literatures into a
new, interdisciplinary model, showing how the three components, trust, cybersecurity, and
sustainability, not only operate independently but are mutually reinforcing pillars of roboadvisory adoption and long-term legitimacy.

6.

Limitations

This study presented several limitations that must be acknowledged when interpreting
and informing future research.

52
6.1 Absence of Primary Data Collection
This study is founded on a literature-based analysis mainly. Because the secondary
sources deliver a very strong theoretical background, the absence of survey, interview or
experimental data, however, results in some conclusions being more conceptual than empirical.

6.2 Generalizability Across Markets
Regulatory and cultural factors, where a robo-advisory platform is adopted, can widely
differ by region (e.g., North America, Europe, Asia). The scholarship used in this paper is global
and therefore it can lack accuracy with a single jurisdiction.

6.3 Technological Evolution
FinTech is a very fast-moving discipline. New trends in AI (such as explainable AI,
generative models) and blockchain-based advisory may become outdated very soon and render
existing frameworks obsolete. Therefore, as much as the Trust-Cybersecurity-Sustainability
(TCS) framework is applicable in modern times, it may require improvements in the future.

6.4 ESG Data Reliability
There is a likelihood of greenwashing and inconsistencies in reporting the ESG data and
its metrics. The reliance on the literature available in this study presupposes a certain level of
data soundness, which is disputable in practice.

6.5 Behavioural Assumptions

53
The model is based on rational behavioural assumptions of investors in relation to trust,
cybersecurity, and ESG levels. Behavioural finance studies, however, reveal that predictions tend
to be subjected to biases, emotions, and heuristics, which tend to influence decisions and hence
introduce uncertainty in the prediction.
7.

Recommendations and Future Research

7.1 Recommendations
Following the investigation, there are a few recommendations to be offered to various
stakeholders:

For Regulators


Establish universally accepted global regulatory frameworks, especially on cybersecurity
controls and environmental, social, and governance (ESG) disclosures on robo-advisory
platforms.



Implement stress-testing of algorithms like stress tests in banking so that algorithms can
hold up against cyberattacks.

For Robo-Advisory Platforms


Increase the transparency of algorithms to give customers understandable and
interpretable explanations of portfolio recommendations.



Add assurances around cybersecurity to marketing and onboarding, and present trust as a
guarantee that goes beyond a service feature but is a means of mitigating risk.



Enhance ESG products with the use of AI-based ESG screening tools that reduce
greenwashing and make their portfolios consistent with the SDGs. In this respect,

54
emerging AI principles can be applied to develop energy-efficient machine learning
models not only to reduce the cost of computation, but also to democratize access to
sustainable fintech solutions (Bolón-Canedo et al., 2024).

For Investors


Increase the financial literacy on the subject of how robo-advisory platforms operate,
namely the aspects of data privacy, ESG portfolios, and risk management
instrumentation.



Induce investors to insist on multi-layered warranty signs (certifications, third-party
audits, and security certifications) with the providers.

7.2 Future Research
The next research direction should be based on the study and cover its weaknesses, and
continue the discussion about the robo-advisory platform adoption:
7.2.1. Empirical Validation of the TCS Framework
Conduct surveys and experiments to investigate the relationship between trust and
perception of cybersecurity trust and sustainability with adoption behaviour.
7.2.2. Cross-Cultural Comparisons
Determine whether different cultures (e.g., collectivism and individualism) result in
different perceptions of the credibility and ESG appeal of robo-advisory platforms.

55
7.2.3. Behavioural Finance and Robo-Advisory Platforms
Explore how cognitive biases (overconfidence, loss aversion) are presented to determine
adoption decisions beyond rational factors of trust/cybersecurity.
7.2.4. AI Explainability and Trust
Explore how explainable AI (XAI) can be used in robo-advisory platforms to achieve
higher adoption of the technology by users, and reduce regulatory risk.
7.2.5. Blockchain and Decentralized Finance (DeFi)
Consider how this could disrupt current models of robo-advisory platforms because of the
potential of blockchain-based robo-advisors, which would enhance cybersecurity and
transparency, along with ESG monitoring.
7.2.6. Longitudinal Studies
Monitor robo-advisory platform long-term client retention, as adopting is just the
beginning - the trust has to be sustained over the years (this is another issue that is less explored).

8. Conclusion
This study provides a significant contribution to both theory and practice by aiming to
examine the promising potential and impending dilemmas of AI-driven robo-advisory platforms
critically and give specific attention to how concerns related to trust, cybersecurity risks, and
integration of sustainability into the robo-advisory context will influence the current adoption
processes in the digital finance age. A scoping review of academic, regulatory, and industry
references identified 3 key themes: risks (Cybersecurity, legal and data privacy exposures),
consumer protection (trust, transparency, redress and stakeholder requirements), and governance
(fiduciary duty, algorithmic accountability and RegTech/CSR). Such thematic implications prove

56
that even though robo-advisory platforms make finance more approachable and more affordable
and customized (in particular, the introduction of ESG-appropriate portfolios), democratizing the
access to such services, the barriers, which already exist, such as the lack of well-consistency in
regulations, data misappropriation, and the lack of transparency in the algorithms, limit the
offerings of robo-advisory platforms, not to mention the lack of trust among the investors.
In solving these challenges, the present study suggests the Trust-CybersecuritySustainability (TCS) Framework that balances all three models, namely, psychological,
technical, and ethical and develops a consistent model of adoption. The framework illuminates
that the role of trust on digital resiliency and performance of robo-advisory platforms is mediated
by investor behaviour, and efficiencies translate into adoption and retention in cases of perceived
fairness, transparency, and resilience of robo-advisory platforms. By suggesting this model, the
work contributes to theory and practice by elaborating adoption/trust scholarship within the lens
of sustainability and governance and by providing regulators, policymakers, and financial
institutions with a framework that facilitates the development of sustainable and trust-driven,
resilient, and trustworthy robo-advisory platforms.
The research also suggested that there were certain long gaps, such as limited application
of empirical method, underdeveloped validation of the nexus between trust and cybersecurity
and insufficient evidence of the threat of the ESG integration and greenwashing. These indicate
the need for future research that combines the longitudinal information with the behavioural
finance experiments, explainable AI (XAI) demonstration and cross-cultural studies to verify and
refine the TCS model.

57
In conclusion, Robo-advisory platforms are not aimed to replace human financial
advisors but rather become an inseparable addition that must be adopted by the majority. On
their part, they will succeed by bridging the prevalent trust, cybersecurity resilience and
sustainability integrity gaps. With the Trust-Cybersecurity-Sustainability (TCS) Framework in
place, the institutions and the regulators will have a chance to make sure that they are not only
efficient, but also ethical and resistant simultaneously. Finally, green financial democratization
can be supported by robo-advisory platforms, since, besides uniting technological
experimentation, fiduciary diligence, and international sustainability goals, it can also turn into a
tool of inclusive and responsible digital finance.

58
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Appendix A: Robo-Advisory Platforms in Canada
The diagram below shows how the Big 5 robo-advisory platforms in Canada can be ranked based on
three factors, namely Low Fees, Bank-Backed Trust and Good ESG Offerings.

Figure A1. Placing the Big 5 Canadian Robo-Advisory Platforms along Low Fees, Bank-Backed
Trust, and Good ESG criteria.

Note. Self-created using Python in Google Colab.
This positioning idea assists in accentuating the comparative advantages of the major robo-advisory
platforms in Canada without involving the exact figures on AUM.