INTRODUCTION:

The various effects of Artificial Intelligence (AI) have changed the way people perform financial services, on how they manage, save, invest, and overall plan their financial lives. Within the last decade, rapid innovations in machine learning (ML), natural language processing (NLP), and predictive analytics allowed for a stream of creative personal finance products (e.g. robo-advisors, algorithmic credit scoring, AI chat bots, and fraud detection) that are producing increasingly data-driven, personalized, and automated forms of personal finance management^1,2,5. While fintech adoption globally has kept pace, there is still very little understanding of the ramifications of AI as it relates to an individual’s financial outcomes, behavioral change, and future dependence on financial systems as compared to its meanings for financial institutions or the macro economy^3,4.

The Role of AI in Changing the Landscape of Personal Finance:

Historically, personal finance has relied on limited regulatory and professional advice channels, limiting accessibility and cost. Fintech delivered services in a digital channel that reduced the costs of financial services while offering transparency⁵.

AI further enables a shift in the personal finance landscape by considering real-time data, enables predictive modeling with historic data, and allows for personalized, adaptive insights for users. For instance, robo-advisors provide investment advice previously available only to high-net-worth individuals at reduced costs and algorithmic credit scoring uses alternative digital footprints to expand access to credit^6,7.

There are three overall Domains related to the application of AI to personal finance. The first transactional and operational AI, including chatbots, fraud detection systems, and biometrics provides a smoother experience for everyday financial transactions⁸. The second Domain advisory and decision support AI, including robo-advisors and automated saving apps provides personalized investment or budgeting solutions^9,10. The third Domain includes AI and credit and inclusion systems. Credit scoring and inclusion systems are changing the financing landscape, and are using ML-driven models that assess non-traditional credit metrics that widen access to financing, and reduce information asymmetries promoting inclusion^11,12.

Effectiveness and Consequences for Users:

Despite these developments, the effectiveness of AI powered personal finance applications is still contested. Some studies have identified evidence of quantifiable improvement in financial outcomes. Studies show for example, that digital credit scoring using big data, has led to reductions in default rates and has provided a channel for excluded borrowers¹. Similar improvements have been seen with robo-advisors, which have led to more diversification and more cost-effective individual portfolio compared to traditional advisory methods⁶. AI-enabled budgeting and savings applications have enhanced desirable behavioral changes that support users in saving more frequently^13,14.

However, the benefits are not shared equally. There are significant demographic differences in trust in AI-enabled financial services, with younger, digitally amenable consumers adopting their use while older consumers or those lacking digital skills are much further behind¹⁵. The fear of algorithmic bias in credit scoring models², risks to data privacy¹⁶, and the black-box nature of machine decision-making¹⁷ continue to impede general use. The Financial Stability Board⁸ cautioned as well that if the use of AI is concentrated to only a few large firms, or in a few dominant sectors, that it could provoke systemic risks and raise questions around accountability and resilience.

There are Gaps and Challenges in Current Research:

Although studies are emerging that investigate operational and near-term impacts of AI used in personal finance, additional gaps and challenges remain. First, there is a lack of empirical research investigating the long-term behavioral and psychological impacts of reliance on automated their financial decisions. For example, does continued reliance on robo-advisors reduce their financial literacy and self-determination, or does it enhance their sense of confidence and self-efficacy by way of continuity? Very few longitudinal studies have made attempts to investigate these issues^3,18.

Second, there are few studies investigating the engagement of underrepresented demographics. Most empirical studies have been conducted on middle- and higher-income consumers in advanced economies^11,19. There is little evidence about how low-income households, older people, or communities in emerging markets engage with AI personal finance solutions. This raises significant issues relating to equity and potential for increased financial exclusion^12,19.

Third, there are few, if any, contributions to the ethical and governance aspects of using AI in personal finance, which is an underdeveloped approach in the literature. A “smart regulation” between innovation and consumer protection is called²⁰, but there has yet to be a comprehensive body of literature providing the frameworks for algorithmic decision-making to creating transparency, accountability, and explainability in contexts like credit scoring and investment advice where algorithms are opaque and users are oftentimes unaware of biases underpinning these systems which erode trust^17,21. Contribution of this Review It is necessary to rein in this fragmented literature on AI and personal finance due to its unevenness, not going to study of verified AI impacts, and promising future directions for research. This review addresses these issues through systematically reviewing with the PRISMA model (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) by synthesizing relevant studies, and reporting our study selection and assessment processes.

Out of 120 records discovered in academic and policy databases (2015–2025), we examined 75 unique studies. After eliminating any studies that did not meet relevance or quality criteria, we ended up with 40 high-quality works (e.g., peer-reviewed journal articles, policy documents, and industry reports) that we retained for our qualitative synthesis. This review synthesizes findings across disciplines (finance, economics, computer science, public policy) to give a broad overview of AI adoption in personal finance, the confirmed and expected implications, and the ethical and social considerations for its widespread adoption.

Research Objectives and Conceptual Framework of the Study:

This review is structured around three primary objectives:

1. Synthesize the existing applications of artificial intelligence in personal finance by understanding their primary functions, the enabling technologies used to develop them, and the platforms and systems into which they are incorporated.

2. Assess the efficacy and impact of AI finance tools on how they affect users' financial performance, behaviors, and education, while taking into account their limitations and restrictions.

3. Identify future research viability and gaps related to poorly researched populations, behavior over a sustained period, and ethical frameworks concerning personal responsibility of adoption of AI.

Figure 1 Conceptual Framework of the Review Study

METHODOLOGY:

Research Design:

This research represents systematic literature review (SLR) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. In this systematic literature review, PRISMA guidelines were used to ensure procedural transparency, reproducibility, and a systematic synthesis and analysis of the vast amount research evidence available. The review was organized by the three objectives of the research: (1) synthesize the current state of AI and personal finance; (2) analyze efficacy and impact; and (3) define gaps and future research opportunities.

Data Sources and Search Procedures:

The authors implemented an inclusive search in the primary academic databases and organization repositories from January-March, 2025. The following databases were used:

· Scopus

· Web of Science (WoS)

· IEEE Xplore

· ScienceDirect (Elsevier)

· SpringerLink

· Emerald Insight

· SSRN for working papers

· OECD iLibrary, World Bank Open Knowledge Repository, and IMF Publications for policy reports

The search included materials published from 2015-2025 to capture coverage of the most recent decade of AI innovations in personal finance. Search terms were formed using Boolean logic and included iterations of keywords like:

• "Artificial Intelligence" or "Machine learning" or "Natural language processing" or "Predictive analytics"

• "Personal finance" or "household finance" OR "budgeting" OR "financial planning" OR "investment management" or "robo-advisors" or "credit scoring" OR "financial inclusion"

Literaure was also included from consulting firms (e.g. Deloitte, Accenture, McKinsey) and regulatory bodies (e.g., IOSCO, FSB) in order to ensure that actionable outcomes were obtained.

Inclusion and Exclusion Criteria:

The following inclusion criteria were developed to maintain relevance and quality:

1. Empirical or conceptual studies that discuss AI applications involving personal finance (individual-level management as opposed to institutional or corporate finance).

2. Peer-reviewed journal articles, conference proceedings, policy reports, and white papers from industry sources.

3. Publications published in English between 2015 - 2025.

The exclusion criteria were:

1. Studies that only addressed AI applications within macroeconomic implications or institutional uses of AI (i.e. algorithmic trading for hedge funds).

2. Publications that do not explicitly reference personal financial outcomes dialectically considered, evaluated, or analyzed.

3. No reference to academic commentary, blog, or unassisted source.

Screening and Selection Process:

The original search produced 120 records. After removal of duplicates (n = 20), we had 100 unique records. Title and abstract screening resulted in the exclusion of 25 records that did not explicitly address AI in personal finance. This left us with 75 full studies that were assessed for eligibility using the inclusion/exclusion criteria for the review. Forty high-quality studies were retained for final synthesis after going through quality appraisal.

The selection process is presented in a PRISMA flow diagram (Figure 2).

Figure 2 PRISMA flow diagram for AI in Personal Finance

Quality Assessment:

Quality appraisal was completed using an adapted version of the Critical Appraisal Skills Programme (CASP) checklist designed for systematic reviews and empirical studies. The assessment criteria included:

• Clarity of research objectives

• Methodological robustness (sample size, AI techniques employed, data quality)

• Usage of the studies as related to personal finance

• Clarity of results and limitations

Each study was rated by two independent reviewers. Any disagreements related to quality appraisal were resolved between the reviewers.

Data extraction and synthesis:

We extracted data about each of the 40 studies included in this review related to:

• Year and source of publication, and type of publication (academic, policy, industry)

• AI technique used (ML, NLP, predictive analytics, hybrid, etc.)

• Domain of application (budgeting, investment, credit, fraud detection, financial literacy)

• Reported impacts on financial outcomes and behavior

• Challenges, limitations, and ethical considerations

Narrative synthesis was employed, accompanied by organizing the evidence thematically by the three research objectives. Where possible, quantitative evidence (e.g., improvements in portfolio performance, decreases in the default rate) were emphasized, while qualitative evidence (e.g., trust in behavior, attitudes to financial literacy) were grouped into the broad themes.

RESULTS AND DISCUSSION:

Review identified an array of AI applications in personal finance, which fit into three clusters (dominant domains): transactional/operational tools; advisory/ decision-support systems; and credit/inclusion systems.

Transactional and operational tools. Multiple studies point to chatbots, biometric authentication, and fraud prevention engines that are often AI-based^8,16. A few researcher documents the emergence of chatbots driven by natural language processing (NLP) to enable seamless interaction between customers and providers in service contexts¹⁶. Studies like The financial stability board highlighted online fraud detection systems that leverage anomaly detection and predictive algorithms to improve transaction security and efficacy in real time. Collectively, these provide two functions: convenience and security from providers that cut operational costs while giving consumers trust in their purchases.

Advisory and decision-support systems. Robo-advisors are the premier area of study of AI innovation in personal finance. According to the studies of Baker and Dellaert⁹, and Tapia and Yermo⁶, robo-advisors represent a democratization of access to financial advice and affordable portfolio management using algorithmic allocation of assets. Researchers reported findings from a sample of millennials that Gen Z had used machine learning that imposes identification on clients and provides investment strategy more responsive than the traditional advisory offering⁵. AI-based budgeting and saving apps leverage behavioural nudges and predictive analytics to effectively create intentional decisions around household cash flow and facilitate healthier saving behaviours¹⁴.

Credit and inclusion mechanisms. Credit scoring based on machine learning is now now a viable alternative to traditional credit scores, which often rely on an individual's limited history of financial behaviour. The variables in one's digital footprint that capture their online behaviour and payment patterns predict the likelihood of default with as great or greater prediction and efficiency compared to conventional credit scores¹. The another example when they find that fintech lenders using ML-based credit models, prove to be able to extend credit to thin-file customers better than traditional banks². In these cases, the use of digital footprints improves operational efficiency, while also expanding access to finance that align with global poverty alleviation goals^12,19. In sum, the findings presented in this work suggest that AI applications in personal finance represent a range of functional variations and has reached a level of technological sophistication; integrating ML, NLP, and predictive analytics into their operational services. The natures of these features are evident in the proliferation of both fintech start-ups and established institutions into the mainstream, signifying that AI has permeated the fabric of our financial ecosystem.

The second aim was to consider the direct effects of AI applications on outcomes for individuals. In summary, the identified studies show positive but variable effects on financial outcomes and behaviours.

Improvements in financial outcomes. AI-driven investment platforms consistently show performance advantage. The robo-advisors provided portfolios that were more diversified and lower cost than traditional human advisors, especially for retail investors with lower amounts of capital⁶. Similarly, reported statistically significant improvements in savings rates for users of AI based budgeting apps because of predictive reminders and automatic transfers¹³.

Access to credit as well as more effective risk management. The two studies considered significant efficiency gains from use of ML-based credit scoring. Studies discovered problems with defaults when alternative data is used¹; and fintech lenders were offering credit to riskier segments without a corresponding increase in the default rate². These represented both efficiency and inclusionary aspects of the study.

Behavioral and literacy implications. In addition to the financial implications, we can understand how these technologies influence user behaviors. Literature notes that the nudges and prompts present in budgetary tools¹⁵, for example, help users to behave better and to develop greater discipline but also introduce a possibility that users lean on the app's recommendations rather than developing reliability on their own. Earlier studies describes the implications of reduced ability - focusing on reduced financial literacy when people rely too heavily on robot advice¹⁸. Another study provides a counter example, suggesting that simply using a transparent robo-advisor exposes users to clear and constant³, simple explanations of what they were thinking when they constructed the portfolio and can improve financial literacy.

Challenges and limitations:

The literature also reflects challenges and limitations. Issues persist across the literature around privacy¹⁶, algorithm bias¹⁷, and decision and recommendation opacity²⁰. Trust was an important issue in the studies, with significant gaps still seen - primarily among older, less literate population segments¹⁹. Additionally, the Financial Stability Board highlighted risks stemming from the concentration of AI capabilities surrounding a handful of large providers. Overall, the literature tends to depict a complicated picture that consistently states: AI tools can undoubtedly support better outcomes for individual users, but the uptake and success inherent in those improvements differs according to a number of demographic, socio-economic and technological factors.

Demographic Inclusivity:

A notable gap exists in the representation of marginalized groups. The majority of studies tend to locate in developed markets and focus on middle and high-income consumers¹¹. There is a paucity of empirical work focused on low-income households, the elderly, or populations in emerging economies--despite the fact that marginalized populations may disproportionately benefit from low-cost AI solutions^12,19.

Long-term behavior outcomes:

Several studies document short-term behavioral impacts, but few examine the longer-term impacts toward psychological and financial readiness for an ongoing reliance on AI systems. Studies note concerns related to dependency and knowledge erosion¹⁸, although few other literature examine this using longitudinal data. More research is needed to examine whether reliance on AI increases resilience via improved decision-making, or erodes autonomy via over-reliance.

Guidance, governance, and transparency:

Ethical issues are often acknowledged but rarely explored in detail. Studies create potential frameworks for algorithmic accountability and explainability^20,21; however, there are currently few empirical frameworks for the evaluation of fairness, transparency, and accountability related to personal finance. In light of current regulatory accountability for AI decision-making¹⁷, this will pose an urgent priority.

Moving forward, researchers should pursue three directions: (1) develop empirical studies that include vulnerable groups, (2) longitudinal studies related to behavioral and psychological outcomes, and (3) interdisciplinary ethical frameworks that include finance, computer science , and behavioral economics.

Deconstructing the State of AI in Personal Finance:

The results of this review show that AI has gone from a possible new wave influence to a shared organizational reality in personal finance. Its uses include routine encounters (fraud detection, chatbots), strategic decision-making (robo-advisors, algorithmic investing), and access (credit scoring, inclusive lending). This accumulation is indicative of the rapidly maturing technology and the level of engagement with it by consumers and providers^1,2 yet, despite the technical capabilities and possibilities being cut and dry, the evidence synthesized in this review presents a more varied evaluation with regards to efficacy, equity and sustainability.

Benefits and Uneven Distribution of Impacts:

A key point worth noting however in this review is the uneven distribution of positive benefits. AI-based budget apps and robo-advisors are clearly creating better savings rates and improved portfolio diversification^6,13. But these benefits are largely confined to digitally savvy higher-income consumers. Marginalized groups such as low-income households, older people, and consumers in developing economies are missing from both the user base and the context that makes it to the academic literature^11,12. This is a troubling sign for the potential for AI to reproduce financial inequalities under the guise of technical advancement. That is to say that without active engagement during both the user design and implementation of AI, technology will have the tendency to amplify existing financial exclusions rather than mitigate them.

Paradox of Autonomy and Dependence:

Another-apparent contradiction is that of autonomy and dependence. On the one hand, AI enables individuals who might otherwise not have access to high-quality financial advice, which democratizes decision-making power^3,9. On the other hand, an empirical body of literature follows with the suggestion AI tools will over time affect financial literacy and weather critical judgment^15,18. This tension reveals a broader discussion in behavioral economics, recognizing that “nudges” can enhance decision-making certainty, while also detracting from the active decision-making process. Future research should inquire whether AI performance tools should support or take over thinking in individual financial decision-making and how to engage in these debates in a responsible manner.

Trust, Transparency and Ethical Accountability.

Trust becomes a critical determinant of adoption. Concerns about data privacy, algorithmic bias, and opaque decision-making have been documented across many human activity contexts^16,17. Some research has shown that the lack of an explainability element diminishes user confidence¹⁹, particularly for older demographics or those with reduced digital based confidence. Nonetheless, while widespread attention is given to these risks, empirical frameworks for measuring and evaluating ethical performance have been underdeveloped^20,21. This reflects tensions in how fast these instruments enter the market and the slower pace of governance frameworks. If these matters are not addressed, it poses a challenge to not only individual adoption but also the stability of the system⁸.

Implications for Research, Policy, and Practice:

From the synthesis, we draw three promising implications:

1. The area of research should examine the long-term behavioral and psychological impact of AI reliance, and not just the short-term financial implications. Additionally, while longitudinal studies can examine whether AI reliance strengthens resilience or promotes dependence, there should be increased advocacy and engagement with vulnerable groups, who might otherwise fall victim to continual inequity through limited research focus on their understanding and experience of AI in personal finance.

2. Practice and policy makers need a dual obligation: to support innovation and protect the consumer. This includes encouraging algorithmic transparency and mandatory fairness audits, as well as accountability standards and levels of cross-border collaboration. Given that a significant portion of AI-enabled personal finance is cross-border in nature²⁰, the challenge for policy and practice is to anticipate the interjurisdictional overlap that will arise with AI innovation.

3. For the practice of and development of financial services, financial institutions and fintech should prioritize ethics by design when developing their services. Principles of transparency, inclusivity, and explainability should not simply be an afterthought if they are included at all, but integrated throughout the design process as core concepts. Examples of practical interventions which could do this include “explainable AI dashboards” and hybrid advisory models where AI technology and financial advice can exist together. This may allow the designer to build trust and acceptance, and ultimately broaden use in practice.

Situating Within Larger Discussions:

As noted above, we have situated AI in personal finance within larger discussions of digital inclusion, responsible innovation, and algorithmic governance. When mobile money allowed individuals to engage with finance in developing economies, the chances for very basic financial empowerment changed, if not the direction of growth. AI has the possibility of changing how financial empowerment develops across the world without removing finance from a user's autonomy; the introduction of AI increased risks of (1) removing access above all else (2) questions of transparency, and (3) increased concentration of systemic risk^3,4. Clearly, it is not just about expanding access; it is also about equitable and sustainable access with an increase in transparency.

LIMITATIONS OF THE REVIEW:

There are limitations to this systematic review as there are to all systematic reviews. First, while the present review covers extensive databases, there may be grey literature or regionally-based studies that may not have been included. Secondly, included studies had great methodological variety and therefore the ability to conduct meta-analysis was restricted and, instead, we used narrative synthesis. Thirdly, this review did not include English Lite publications and therefore ignore other perspectives in the non-English sphere where fintech adoption is expanding. These limitations point to important areas for future research in multilingual and multidisciplinary areas of research.

CONCLUSION:

This systematic review has collated the changing nature of artificial intelligence, AI, in personal finance over the last ten years, using a PRISMA-guided search process of 40 robust studies. Overall, we find that AI has arrived as a transformative enabler in three areas: (i) transactional and operational tools (e.g., chatbots, fraud detection), (ii) advisory and decision-support systems (e.g. robo-advisors, savings apps), and (iii) credit and inclusion mechanisms (e.g. machine-learning algorithms using alternative data).

Overall, evidence suggests these tools can optimize savings behaviour, improve portfolio diversity, and expand access to credit. However, these improvements are neither universal nor equitable. The benefits of AI tradings and tools are generally centralised amongst digitally literate, higher-income users, while most at risk populations are still excluded. Furthermore, the challenges of algorithmic bias, opacity, and data privacy loom large and troublingly affect public trust and accountability.

We also identify key research gaps: longitudinal studies on behavioral and psychological impacts, limited inclusion of marginalized demographics, and specifying clear ethical evaluation frameworks. Our findings provide a call for an inter-disciplinary and inclusive research agenda recognizing the value of finance, behavioral science, and computer science.

PRACTICAL RECOMMENDATIONS:

Based on our review of the evidence, we provide three sets of recommendations for researchers, policymakers, and practitioners:

1. Researchers:

• Conduct longitudinal studies examining how blind trust in AI shapes financial literacy, autonomy of decision-making, and well-being.

• Broaden empirical studies with underrepresented populations, especially inequitably served populations that are low income, older adults, and developing countries, where AI might have a disproportionate impact.

• Investigate measurement mechanisms of ethical AI practice with an emphasis on fairness, explainability, and accountability, in personal finance models.

2. Policymakers and regulators:

• Provide regulatory sandboxes that permit the experimentation of AI-enabled financial services but preserve consumer protection.

• Develop regulatory requirements around transparency and fairness in AI model audits especially related to providing credit scores or providing investment advice.

• Encourage global collaboration for policymakers and regulators, as AI-enabled fintech services generally conduct work globally, and regulatory environments benefit from harmonization.

3. Developers (fintech or financial institutions):

• Provide explanation features to an AI tool, for example, as part of AI services, users can see why a credit decision was reached or why a portfolio was suggested.

• Consider inclusivity and accessibility to digital tools by consumers with varying degrees of digital literacy and access to products. Hybrid AI ideas that combine human and AI advice may be beneficial with the democratization of adoption.

• Actively tackle data privacy and security issues, since violations or inappropriate use of financial data can greatly diminish trust and acceptance.

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Received on 13.09.2025 Revised on 05.11.2025

Accepted on 08.12.2025 Published on 11.05.2026

Available online from May 14, 2026

Asian Journal of Management. 2026;17(2):127-134.

DOI: 10.52711/2321-5763.2026.00019

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.