ABSTRACT:
The rapid adoption of artificial intelligence (AI) in personal finance is changing the ways that consumers manage money, invest finances, and engage with financial services. AI-enabled solutions, some of which are called robo-advisors, intelligent budgeting apps, algorithmic credit scoring systems, fraud detection, and personalized finance assistants have completely changed the any decision-making process related to personal finance we had over the past decade. Although these technologies have been widely disseminated there remain legitimate questions about their effectiveness, what accessibility actually means, and the implications moving forward. Our research is a systematic review of the personal finance literature that examines and evaluates AI in personal finance using the PRISMA method. The researcher searched for articles published from 2015-2025, that the researcher identified through leading academic journal databases, global reports, and policy papers. After the researcher finalized their search, there were 120 studies, and through screening and eligibility assessment, the researcher identified 40 high-quality studies to synthesize. The central goal of the review was to systematically ascertain the current level of AI applications available for consumers in personal finance. The review illustrated that AI is now embedded in three areas: (i) transaction and operations tools like chat bots or fraud detection engines; (ii) advisory or decision support tools like robo-advisors, algorithmic trading, or personalized investment dashboards; and (iii) credit risk and inclusion tools that use machine learning and alternative digital footprints (social media, mobile device data) to make scoring and access to finance easier. The most common technologies identified were machine learning algorithms, natural language processing, and predictive analytics which are becoming more common in fintech systems and legacy banking systems. The second objective was to assess how effective were these tools and their impact? There is some evidence that AI financial applications may provide benefits to people, including promoting saving behaviour, creating more diversified investment portfolios, offering quicker and fairer credit decisions; however, benefits are not equally distributed across populations. For financially secure, tech-savvy and digitally literate consumers, more benefits include personalized recommendations (e.g., ‘smart savings’ or ‘smart savings account’), and lower costs. This leaves vulnerable populations to deal with more engagement issues and algorithmic bias. Furthermore, there are continued challenges to data privacy and transparency, and amiable reporting and trust from the end user, particularly concerning qualitative results in the studies. There are also some insights in quantitative results concerning AI enabled credit scoring, and robo-advisory services, while qualitative results include behaviour results of financial automation (e.g. dependence on algorithms; the shift in perceptions of financial literacy). The third objective was to uncover research gaps and identify future research directions. The review identified a gap in the research investigating the long-term consequences of relying on AI to make financial decisions. Although past research has explored various aspects of financial decision-making, particularly concerning delegating aspects of financial decision-making to automated finance-related tools, there is limited empirical research that has assessed the long-term consequences of sustained reliance on automated finance-related tools on a user's financial capacity and psychological well-being. A variety of demographic differences, e.g., old versus young adults, income profile differences, and a variety of contexts, e.g., low- and middle-income countries as opposed to developed countries; there are a few studies that debuted in low and middle countries, and even fewer that actually researched outcomes in those environments. Finally, while ethical issues that pertain to the sustainable use of AI methods, such as algorithmic accountability, fairness, and explainability are prominent in the literature, they are underrepresented in scholarship. Future research could benefit from taking a multi-disciplinary approach, incorporating contributing disciplines, like computer science, behavioral economics, and finance in their planned design of appropriate and inclusive AI. This review synthesizes 40 carefully organized studies, and gives a broad perspective on the academic landscape of AI in personal finance by assessing the actual impacts provided by AI and signposting urgently needed areas for future research. These insights will help foster academic debates and serve as guidance for decision makers, fintech developers, and financial institutions to innovate within safe boundaries.