An Intelligent Framework for Predictive Risk Assessment and Loan Eligibility Classification

Bommisetti Reddi Lakshmi, Dr. S. Usharani

Artificial Intelligence; Credit Scoring; Django; Financial Risk Assessment; Fraud Detection.

The financial services sector is increasingly pivoting toward intelligent, data-driven technologies to enhance decision-making accuracy and mitigate loan default risks. Traditional manual evaluation processes are frequently constrained by time-intensive workflows and subjective biases. This paper presents an AI-Driven Loan Eligibility and Risk Assessment System designed to modernize credit evaluation through a rule-based artificial intelligence engine.Developed using the Django framework, the system automates the assessment of six critical financial dimensions: credit score, income-to-loan ratio, employment stability, debt burden, collateral, and asset holdings. The system generates a composite risk score, fraud anomaly alerts, and personalized loan recommendations, providing a transparent and explainable alternative to “black-box” machine learning models. By integrating role-specific dashboards and real-time notifications, the proposed solution optimizes the lending lifecycle while maintaining the auditable logic required for regulatory compliance.

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