International Journal on Science and Technology

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Financial Decision Intelligence System (FDIS): An AI-Driven Framework for Personal Finance Analytics and Risk Prediction

Author(s) Ms. Gayathri D, Mr. Kabilan M, Mr. Arshad C
Country India
Abstract A recurring challenge among young earners in India is the absence of tools that go beyond recording past transactions to offer forward-looking financial guidance. The Financial Decision Intelligence System (FDIS) addresses this gap by combining machine learning (ML) classification, rule-based financial analytics, and a multi-channel alert mechanism within a single web-based platform. Three classification models — Random Forest, Decision Tree, and Logistic Regression — are trained on engineered financial ratios derived from user-submitted income, expense, savings, and debt data. The trained Random Forest classifier achieved 92% accuracy in categorising users as Low, Medium, or High financial risk. The system also incorporates a Smart Savings Splitter that automatically allocates surplus funds between an emergency reserve and debt clearance, and a Twilio-powered alert subsystem that notifies users via SMS, voice call, and email when a 30% or greater fluctuation is detected in credit or debit activity. A user study involving fifteen participants yielded an overall satisfaction rating of 4.7 out of 5.0. This chapter documents the system architecture, ML methodology, implementation choices, and experimental outcomes.
Keywords Artificial Intelligence, FinTech, Financial Risk Prediction, Machine Learning, Random Forest, Personal Finance, Decision Support System, Business Intelligence, Predictive Analytics
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-05-31
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