International Journal of Science, Engineering and TechnologyAuthors: Aditi Rajput, Avani Malviya, Dhruv Dekhane, Mahi Ishwe, Prof. Manish Vyas
Abstract: Artificial intelligence is reshaping how individuals access, receive, and act on financial advice. Personal finance— covering budgeting, credit management, investment planning, and retirement preparation—has long been gatekept by cost and expertise. AI-driven systems, from simple calculators to adaptive robo-planners, are changing that. But expanded access does not automatically mean better outcomes. Many platforms reproduce the same incentive misalignments and opacity prob- lems that made traditional human advisors unreliable in the first place, only now at scale. This paper examines the current state of AI in personal finance advice across three angles: (1) a review of machine learning techniques used in credit risk assessment and portfolio optimization, (2) an analysis of trust, personalization, and behavioral risks in deployed platforms, and (3) a proposed five-principle framework—fiduciary duty, adaptive personalization, technical robustness, ethical fairness, and auditability—to evaluate whether these systems genuinely serve users. Experimental results from a supervised ML classifier trained on a Kaggle credit risk dataset show Random Forest achieving 89.25% accuracy and AUC of 0.77. The broader argument is that technological sophistication, while necessary, does not resolve the governance gaps that undermine user trust in AI financial advice.
DOI: http://doi.org/10.5281/zenodo.20573690
