AI-Based Credit Scoring System

Abstract

This paper presents an AI-based Credit Score System that evaluates the creditworthiness of individuals and businesses using PAN-based validation, verified credit bureau data, and machine learning. The system ensures strict data integrity by generating credit scores only when all required financialandbureauinformationispresent,preventingdummy orhardcodedvalues.Thefrontend,builtwithReact,providesa secureanduser-friendlyinterface,whilethebackend,developed using Python with Flask or Django, manages authentication, data storage, and ML-based prediction. Machine learning models such as Random Forest and Gradient Boosting are trained on historical datasets, with explainable AI techniques like SHAP employed for transparency. Designed for cloud deployment with encryption, role-based access control, and auditlogging,thesystemdeliversascalable,secure,andreliable solution for modern credit assessment.

Introduction

The AI-Based Credit Score System (CSS) is a secure and intelligent credit evaluation platform that combines PAN-based verification, verified credit bureau data, and machine learning models to generate accurate and explainable credit scores. Unlike traditional credit scoring methods, the system ensures strict data integrity by requiring complete and validated financial information before generating scores.

Built using React, Python (Flask/Django), and MySQL/MongoDB, the platform provides secure authentication, data storage, and a user-friendly dashboard. Machine learning models such as Random Forest and Gradient Boosting predict credit scores, while SHAP and LIME enhance transparency by explaining prediction results.

The system incorporates strong security measures, including JWT authentication, role-based access control, data encryption, and audit logging, making it suitable for cloud deployment. Experimental results demonstrate accurate credit assessment, reduced default risk, and near real-time decision-making. Future improvements include integration with multiple credit bureaus, advanced AI models, alternative data sources, mobile applications, continuous learning, and enhanced regulatory compliance. Overall, CSS provides a reliable, transparent, scalable, and secure solution for modern credit risk evaluation.

Conclusion

TheAI-BasedCreditScoreSystempresentedinthisstudy successfullyintegratesPAN-basedvalidation,secure database storage, and explainable machine learning models toprovideaccurate,reliable,andfaircreditassessments.The system enforces strict validation rules, ensuring that credit scores are generated only with complete and verified financial and bureau data. Random or hardcoded scores are prevented, and sensitive information such as PAN is encrypted and protected through JWT authentication and role-based access control. The full-stack implementation using React and Flask demonstrates a professional, user- friendlyfrontendcombinedwitharobustbackendcapableof real-time data processing and ML-based prediction. The credit score dashboard provides transparent and explainable results using SHAP/LIME, and the admin panel allows effective monitoring of system operations and ML model performance. Overall, the system ensures improved trust, accountability, and reliability in credit evaluation processes, addressing major limitations of conventional static scoring methods.

References

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Copyright

Copyright © 2026 Harshada Santosh Jagtap, Mugdha Vinod Sarode, Vaishnavi Popat Bodke , Dhruti Nilesh Lohar, Sivaram Ponnusamy, Umesh Pawar. 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.