Artificial Intelligence and Bank credit Risk: Understanding, predicting optimizing decisions in the age of Machine Learning

In the digital age, bank lending no longer relies solely on human intuition or traditional statistical methods. Faced with rising risks, increasingly complex financial behavior and an explosion of data, banks are entering a new era: that of artificial intelligence. Artificial Intelligence and Bank Credit Risk: Methods, Models and Applications explores in depth the transformation of banking decision-making systems thanks to Machine Learning. The book shows how financial institutions can use algorithms to better assess borrowers, reduce defaults and grant credit faster, more accurately and more fairly.The book begins by explaining the fundamentals of bank credit: why some applications are accepted, why others are rejected, and how notions of creditworthiness, risk and profitability structure financial decisions. It also highlights the real cost of bad credit: bank losses, bad debts, business failures and economic slowdown.