Credit risk management focuses on identifying, assessing, and mitigating the risk of borrower default. The book explores key principles, methodologies, and frameworks used by financial institutions to manage credit risk effectively. It covers credit scoring models, portfolio risk assessment, regulatory frameworks like Basel Accords, and stress testing techniques. The book emphasizes risk mitigation tools such as credit derivatives, collateralization, and diversification. It also discusses early warning signals, loan classifications, and risk-adjusted return models. Case studies highlight real-world applications, showing how institutions navigate market volatility and economic downturns. A key takeaway is the balance between risk appetite and profitability, ensuring sustainable lending practices. The book underscores the role of AI, machine learning, and big data in enhancing credit risk analytics. It provides a strategic perspective on integrating risk management into financial decision-making while complying with evolving global regulations.
