Harnessing Artificial Intelligence and Predictive Data Analytics to Enhance Risk Assessment and Credit Scoring Mechanisms in Retail Banking

Abstract

Artificial intelligence (AI) and predictive data analytics have emerged as transformative forces in retail banking, offering unprecedented capabilities to refine risk assessment and credit scoring processes. This paper presents a comprehensive, technically advanced exploration of methodologies that harness machine learning, deep neural architectures, and probabilistic inference to enhance the precision, robustness, and adaptability of credit risk models. Key contributions include a unified theoretical framework for integrating heterogeneous data sources—ranging from traditional financial ratios to unstructured behavioral indicators—and a rigorous treatment of feature representation methods that maximize predictive information content while controlling for multicollinearity and overfitting. A dedicated section develops a novel mathematical modeling paradigm based on variational Bayesian inference combined with spatio-temporal attention mechanisms, yielding dynamic creditworthiness scores that evolve with borrower behavior in real time. Extensive discussion covers strategies for high-dimensional data preprocessing, feature embedding via autoencoder networks, and the calibration of loss functions to balance type I and type II error costs under regulatory constraints. The paper further addresses model validation protocols, including back-testing over stressed economic scenarios and the construction of custom performance metrics that capture tail-risk exposures. Finally, considerations for operational deployment—such as scalable microservice architectures, continuous learning pipelines, and explainability frameworks—are examined to facilitate integration into existing banking infrastructures. This work advances the state of the art in retail credit decisioning by providing a technically rigorous roadmap for AI-driven risk assessment.