Improving Credit Risk Management with Predictive Analytics

Abstract

In the context of credit risk management within the banking industry, this in-depth investigation explores the landscape of predictive analytics, which is constantly evolving. For the purpose of providing a holistic understanding of the role that predictive analytics plays in modern banking, the study is based on a qualitative research approach and combines current literature with case studies from the actual world. Among the key developments highlighted by the evaluation are the following: the increasing importance of ethical and regulatory compliance; the democratisation of predictive analytics tools; the integration of predictive analytics across diverse banking activities; and the transition to advanced machine learning algorithms. It proves the usefulness of predictive analytics by showing that it can facilitate more accurate risk assessments, quicker decision-making, and better overall banking performance. Analyses that compare different contexts demonstrate that predictive models perform differently in each of those contexts, highlighting the significance of selecting models that are customised to the specific context. Nevertheless, there are considerable obstacles to overcome, including the quality of the data, the interpretability of the model, the shortage of personnel, ethical problems, and the fees associated with implementation. Predictive analytics has the potential to become a vital instrument for managing credit risk in the banking industry. It will provide more accurate risk assessments, more intelligent judgements, and more resilience.