AI-driven financial risk mitigation: A multi-model approach to credit scoring and default1 prediction

This paper focuses on the use of artificial intelligence to enhance the mitigation of financial risks in a multi-model credit scoring and default prediction. The central question is how AI-based models can assist lenders in their quest to make more accurate, timely and data-driven credit decisions. The research uses a qualitative and analytical strategy, reviewing AI methods of assessing credit risk, including machine learning, deep learning, decision trees, random forests, and neural networks. It also takes into account real-life cases of financial institutions employing AI to enhance risk management. The results indicate that AI models may enhance the accuracy of predictions, decrease human biasness, detect high-risk borrowers earlier, and facilitate quicker loan approval procedures. The issues of data privacy, model transparency, and ethical issues, however, are also significant. The paper finds that AI-based credit scoring has a high potential to lower the risk of default and enhance financial stability in case of adequate implementation and control.