AI-Powered Spending Habits Coach

Abstract

The increasing adoption of digital payment systems has significantly reduced transactional friction, often leading to diminished financial awareness and impulsive spending behavior. Existing personal finance applications primarily provide expense tracking and retrospective visualization, offering limited real-time behavioral intervention. This paper presents the design and implementation of an explainable artificial intelligence-based financial nudging system that transforms passive budgeting tools into proactive behavioral coaching platforms. The proposed framework integrates structured rule-based decision modelling with context-aware conversational feedback to deliver categorized, timely, and personalized nudges aligned with behavioral economic principles. A full-stack web architecture is implemented to support transaction processing, budget evaluation, and dynamic nudge generation while preserving data privacy through secure authentication and controlled storage mechanisms. The study focuses on system design, decision formulation, and functional validation rather than longitudinal behavioral experimentation. The results demonstrate the feasibility of operationalizing explainable AI techniques for financial habit intervention within a deployable application framework, establishing a foundation for future empirical evaluation and adaptive optimization.

Introduction

The passage describes an AI-based explainable financial nudging system designed to address a key limitation of modern digital finance tools: while apps like mobile banking and UPI platforms make spending easier, they often reduce user awareness of cumulative expenses and fail to influence behavior in real time.

Most existing financial apps focus on passive tracking (dashboards, categorization, budgets, and static alerts), which do not adapt to user behavior or context and often lead to notification fatigue. The work argues that effective financial guidance should instead provide real-time, context-aware interventions grounded in behavioral economics and explainable AI principles.

To address this, the study proposes a Context-Aware Nudge Framework that:

• Calculates spending risk using a ratio of spending to budget (S/B)
• Incorporates behavioral factors like spending frequency and historical trends
• Generates structured nudges rather than generic alerts

Nudges are categorized into:

• Preventive (stop overspending early)
• Corrective (address ongoing overspending)
• Reinforcement (reward good behavior)
• Educational (increase financial awareness)
• Motivational (support financial goals)

The system uses a hybrid decision engine combined with a conversational personalization layer to tailor message tone and content, improving engagement while reducing alert fatigue.

The architecture is a client-server web system with frontend dashboards, a backend decision engine, and a secure database, using encrypted communication and token-based authentication.

For evaluation, a synthetic dataset of 150 user spending patterns was used and compared against a traditional static threshold alert system. The proposed model focuses on early detection of overspending risk using metrics like Early Intervention Rate and False Positive Rate, aiming to deliver more timely and context-aware financial guidance.

Conclusion

This paper presents the design and implementation of an explainable AI-based financial nudging system positioned as an applied artificial intelligence study. By integrating structured behavioral modelling, categorized intervention strategies, and conversational personalization within a secure web architecture, the proposed system advances the transformation of financial management tools from passive reporting interfaces to proactive behavioral coaching platforms. Although empirical validation remains future work, the implementation establishes a practical and scalable foundation for AI-driven financial habit intervention systems

References

[1] S. K. Gorai and Amar Maurya, “Artificial Intelligence in Personal Finance Management: Opportunities and Challenges” IOSR Journal of Computer Engineering Volume 27, Issue 1, Ser. 1, pp.07–17, Jan- Feb 2025. [2] Mohit Jain and Arjun Shrihari,) “AI DRIVEN PERSONAL FINANCE MANAGEMENT TOOLS” IJNRD, Volume 9, Issue 12, December 2024. [3] Neha Tyagi et al., “Smart Spend-AI Driven personal finance manager” IJCRT, Volume 13, Issue 5, May 2025. [4] Samarjeet Singh Kheda et al., “Research Paper for Personal Finance Tracker” Acropolis Institute of Technology and Research. [5] Megha Suvarna et al., “Financely: Personal Finance Tracker Revolutionizing your Financial Journey” International Journal of Scientific Research & Engineering Trends, Volume 10, Issue 5, Sept-Oct-2024. [6] Sai Deepak Talasila, \"AI-Driven Personal Finance Management: Revolutionizing Budgeting and Financial Planning\" IRJRET, Volume:11, Issue:07, July 2024. [7] Somit Aggarwal and Prince Modanwal, \"AI-Driven Personal Finance Assistants: Enhancing Customization Through Behavioural Insights\" IJEDR, Volume 13, Issue 2 June 2025. [8] Prabin Adhikari et al., \"The impact of AI on personal finance and wealth management in the US\" IJSRA, Dec 2024. [9] Shyam Kaushik B M et al., \"AI-Driven Personal Finance Management System - A Survey\" TJER, Volume 12, Issue 7, July 2025. [10] Venkata Raj Kiran Kollimarla, \"THE ROLE OF ARTIFICIAL INTELLIGENCE IN DEMOCRATIZING FINANCE AND TRANSFORMING MARKET PARTICIPATION” IJARET, Volume 15, Issue 3, pp 128-142, May-Jun 2024. [11] Arun Rai, 2020. \"Explainable AI: from black box to glass box,\" Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 137-141, January 2020. [12] Shneiderman, B. (2020). “Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy.” International Journal of Human–Computer Interaction, 36(6), 495–504. [13] W. Samek, G. Montavon, S. Lapuschkin, C. J. Anders, and K. Müller, “Explaining deep neural networks and beyond: A review of methods and applications,” Proc. IEEE, vol. 109, no. 3, pp. 247–278, 2021 [14] Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019). “Machine Learning Interpretability: A Survey on Methods and Metrics.” Electronics, 8(8), 832 [15] Caraban, A.K., Karapanos, E., Gonçalves, D., & Campos, P.F. (2019). “23 Ways to Nudge: A Review of Technology-Mediated Nudging in Human-Computer Interaction.” Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems.

Copyright

Copyright © 2026 Ayush Saraswat, Shaikh Athar Ahmed, Sreeja G, Shalini Kumari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.