AI-Based Risk Assessment Models in Banking
TL;DR
Performance evaluation using metrics such as accuracy, precision, recall, F1-score, and AUC shows that AI models outperform traditional approaches by enhancing prediction capability and reducing manual intervention.
Abstract
The banking sector requires accurate risk assessment to maintain financial stability and reduce losses. Traditional risk assessment methods rely on historical data, credit scores, and statistical techniques but often struggle with large-scale data, complex patterns, and real-time decision-making. Advances in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have introduced intelligent solutions for evaluating credit, fraud, operational, market, and liquidity risks. AI-based models analyze vast amounts of structured and unstructured financial data to identify hidden patterns and generate predictive insights. Techniques such as Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, Gradient Boosting, Neural Networks, and Deep Learning models improve risk prediction accuracy and fraud detection. This study proposes an AI-driven risk assessment framework comprising data collection, preprocessing, feature extraction, model training, risk prediction, and decision support. Performance evaluation using metrics such as accuracy, precision, recall, F1-score, and AUC shows that AI models outperform traditional approaches by enhancing prediction capability and reducing manual intervention. Despite challenges related to data privacy, interpretability, regulatory compliance, and ethics, AI-based risk assessment significantly strengthens modern banking risk management and supports sustainable financial operations.