A STUDY ON AI-BASED CREDIT SCORING MODELS

Abstract

The rapid growth of digital payments in India has transformed the financial landscape, especially with the rise of fintech platforms, NBFCs, and online banking systems. While this transformation has improved convenience and accessibility, it has also increased fraud risks, leading to financial losses and security challenges. This project focuses on the application of Artificial Intelligence (AI) and Machine Learning (ML) for real-time fraud detection, with a case study on Razorpay, a leading payment gateway used by banks and NBFCs for KYC verification and secure transactions. The study explores how payment gateways use machine learning models such as Random Forest, Logistic Regression, SVM, Neural Networks, and Deep Learning to identify anomalies, detect suspicious behaviour, and prevent fraud in real time. This project also examines the integration of AI-based KYC (Know Your Customer) systems that utilize OCR, facial recognition, document verification, and pattern matching to enhance security and regulatory compliance. The research uses a secondary data-based methodology, supported by industry reports, government regulations (RBI Guidelines), white papers, technical case studies, and existing fraud detection models. Razorpay’s AI-driven risk management system, “Third watch”, has been studied as a practical example of fraud mitigation strategies within banking and NBFC operations. The results show that the implementation of AI not only improves fraud detection accuracy but also reduces manual verification time and operational costs. The study also highlights ethical issues, challenges, limitations, and future scope of AI in financial security. This research concludes that AI-based fraud detection is not just a technological advancement but a necessity for the financial industry. With appropriate data models, regulatory alignment, and technological scalability, banks and NBFCs in India can build a more secure, customer-centric, and fraud-proof digital ecosystem.