Real-Time credit scoring and risk analysis: Integrating AI and data processing in loan platforms

Abstract

Real-time credit scoring and risk analysis play a crucial role in ensuring accurate lending decisions in modern financial platforms, particularly with the growing adoption of alternative financing models like Buy Now, Pay Later (BNPL). However, traditional credit scoring models often fall short because of their reliance on static historical financial data, which limits their effectiveness for individuals with little or no credit history and reduces responsiveness to evolving borrower behaviour. To address these limitations, this manuscript proposes a deep learning-based approach, Credit Scoring and Risk Analysis utilizing Deep Processing Loan Platform (CSRA-DPLP-BSCNN). Initially, input data is collected from BNPL datasets. Then, the input data is pre-processed using the Regularized Bias-Aware Ensemble Kalman Filter (RBEKF) to manage missing values, normalize inputs, and remove noise. The cleaned data is then processed using a Binarized Simplicial Convolutional Neural Network (BSCNN), which identifies patterns related to credit scores, repayment history, income levels, and financial behaviour to predict credit risk in real-time. The proposed CSRA-DPLP-BSCNN method achieves 98% accuracy, 97% precision, 96% recall, 98% F1-score, and 1.159 seconds of computational time, with a high ROC of 0.95, compared with existing methods. For example: Using Machine Learning, Alternative Data, and Predictive Analytics to Improve Financial Scoring via Advanced AI-Driven Credit Risk Assessment for Buy Now, Pay Later (BNPL) and E-Commerce Financing (CRA-ECF-PAEFS); Increasing Financial Stability through Real-Time Credit Risk Monitoring Using Machine Learning Techniques and Advanced Data Analytics (EFS-RTCRM-MLT-ADA); and Credit Risk Evaluation in the Financial Sector Using Deep Learning (CRE-FSDL).