Really good

Ghose and Soullelis’s Model Risk and Uncertainty in the Financial World is a joy to read. In plain language, interspersed with real and often humorous examples, these authors take the reader on a journey through the past, present and future of financial models. They focus on the challenges and nuances of accounting for risk and uncertainty when predicting the behaviour of individuals, institutions, and markets. Experts and novices alike will come away with a new appreciation for the art and science of financial modelling.

A timely and essential contribution to the study and practice of financial risk, this book offers the most comprehensive treatment of risk and uncertainty I have seen. Its insights on AI show how machine learning is reshaping financial decision-making while confronting interpretability and risk governance challenges. The authors’ blend of research and practical experience delivers a clear framework for managing model risk, making this an invaluable guide for academics, students, and practitioners alike. By combining cutting-edge insights with actionable frameworks for managing risk and uncertainty this book is an indispensable guide to the finance profession.

Model Risk and Uncertainty in the Financial World tackles the complex topic of model risk with impressive clarity and rigour. The authors trace an entertaining account of models, risk and uncertainty from Babylonian numerical methods to modern day gradient boosting techniques. Replete with pragmatic advice, the book should be very accessible to all risk professionals and financial regulators.

This book shows us how the state-of-the-art financial models in use today came from ideas and techniques developed and refined over centuries. It is an engaging and entertaining romp that begins with the differences between risk and uncertainty and then moves us through the arcane world of financial models and current AI techniques. It is a welcome and surprisingly fun departure from standard econometrics textbooks. If you’ve ever wondered about possibilities and probabilities in your life, this is for you.

Models drive decisions in finance: when they fail, the consequences are real. This book shows why, with clear examples and practical tools. It connects history, practice, and today’s AI-driven risks. At Risk Span, I see these challenges every day. Practitioners will find this book invaluable.

Model Risk and Uncertainty in the Financial World is a thoughtful and rigorous examination of how models both illuminate and complicate our understanding of finance. Ghose and Soulellis bring clarity to the taxonomy of uncertainty, highlight the challenges of specification and operational risks, and offer valuable insights into the role of AI and machine learning. This book is both intellectually rich and highly relevant, and it is an important resource for anyone engaging with the complexities of financial modelling today.

A delightful read that drives home the importance of understanding the difference between risk and uncertainty when modelling financial markets. It combines both theoretical insights with a practical understanding of the banking (Basel) oversight framework. A great read for undergraduate business majors, MBAs and those just starting out in finance.

An essential read for anyone working in IRRBB!

Great read