FinSim: A Deterministic Financial Shock and Decision Simulation System for Personal Finance Decision Support

Abstract

FinSim is a personal finance decision support system designed to help individuals examine how financial shocks and major financial decisions affect liquidity, debt burden, and overall resilience. FinSim is designed as a deterministic, rule-based platform with no artificial intelligence, no machine learning, and no probabilistic recommendation logic. Instead, it combines structured user financial profiles, financial formulas, threshold-based classification, and visual analytics to support what-if exploration. The proposed platform integrates three main functional modules: shock simulation, decision impact simulation, and resilience analysis. These modules compute metrics such as savings rate, debt-to-income ratio, emergency fund coverage, net worth, loan affordability, and a composite financial health score. The literature reviewed for this work shows that many existing personal finance tools focus on tracking past spending, while prior decision support research highlights the value of model-driven and knowledge-driven systems for complex personal finance decisions [1]-[4]. FinSim contributes a transparent architecture that connects these ideas to an Indian personal finance context through INR-based modeling, EMI analysis, and explainable rule-driven recommendations. Using the system’s implementation and validation results, this paper shows that the platform can identify high-risk cases such as prolonged job loss, weak liquidity buffers, and overextended loan decisions, while also providing interpretable outputs through charts, health scoring, and typed recommendations..

Introduction

It begins by explaining that personal finance has become increasingly complex due to multiple factors such as income management, debt, savings, investments, and unexpected financial shocks. Existing tools like budgeting apps or investment platforms are mostly retrospective or isolated, meaning they show past spending or handle single calculations but do not simulate real-life financial scenarios such as job loss, medical emergencies, or inflation.

To address this gap, FinSim is proposed as a Financial Shock and Decision Simulator that evaluates a user’s financial condition under different scenarios. It uses transparent, rule-based mathematical formulas (not black-box AI models) to ensure results are explainable, reproducible, and auditable.

The system is built around four main modules:

• Shock Simulator: evaluates impact of crises like job loss or medical expenses
• Decision Simulator: analyzes loans or major purchases using EMI and debt calculations
• Resilience Analyzer: measures overall financial health and stability
• Recommendation Engine: provides explainable financial advice based on computed metrics

The literature review highlights that while existing research supports financial decision systems, vulnerability analysis, and literacy improvement, current tools still lack integrated scenario simulation, transparency, and end-to-end decision support. FinSim addresses this gap by combining these capabilities into one system.

Conclusion

This paper presents FinSim as a deterministic personal finance decision support system that combines simulation, rule-based interpretation, and visualization. The reviewed literature establishes the need for such a system: existing studies show the importance of model-driven financial support, the prevalence of household fragility, and the role of literacy and debt structure in financial decision quality [1]-[10]. FinSim operationalizes those concerns through a structured financial profile, simulation modules, weighted health scoring, threshold-based rules, and persistent analysis records for its API-backed modules. The main value of FinSim lies in transparency. Rather than predicting behavior through opaque models, it allows users to inspect how formulas and thresholds produce outputs such as EMI, DTI, emergency months, decision labels, and health categories. The implementation and testing results further show that the system can distinguish between stable and high-risk cases in ways that are financially meaningful and visually interpretable. As a prototype, it still has clear limitations, but the implemented system provides a credible foundation for future work in personal finance simulation, explainable financial guidance, and consumer-facing decision support.

References

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Copyright

Copyright © 2026 Rahil Majithia, Krishi Oza, Janish Dave, Dakshesh Khadtare, Prof. Kashif Sheikh. 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.