A Critical Investigation in Assessing the Main Metrics of Using Machine Learning Approaches for Bank Risk Management in the Current Era

Abstract

In a variety of application fields, including image classification, recognition of speech, and machine interpretation, machine learning (ML) and artificial intelligence (AI) have attained human-level performance. Nonetheless, master based credit risk models keep on administering in the monetary business. To continuously present new strategies, it is important to lay out significant benchmarks and correlations on AI approaches and human master based models. The progressions in banking and chance administration, as well as the present and future issues, have been the subject of much exploration in both scholarly community and business. Through an examination of the current writing, this paper expects to distinguish regions or issues in risk the board that poor person been adequately investigated and might be great contender for additional exploration. It additionally examinations and assesses AI methods that have been explored with regards to banking risk the executives. The comparison's main results showed that machine-learning models outperformed traditional methods. The neural networks also demonstrated excellent results when compared to other methods of machine-learning (ML) in relations of AUC and precission/ accuracy.