Artificial Intelligence and Credit Risk: The Use of Alternative Data and Methods in Internal Credit Rating

This book discusses the use of alternative techniques and data for credit risk, leveraging artificial intelligence to enhance predictive power and address interpretability and ethical dilemmas in the financial industry. Contributors include researchers, risk managers, and consultants.

Format: Hardback
Length: 104 pages
Publication date: 14 September 2022
Publisher: Springer International Publishing AG

This comprehensive book delves into innovative techniques and data sources used for credit risk assessment, providing a detailed exploration of the diverse methodological approaches employed for incorporating techniques and/or alternative data into regulatory and managerial rating models. Over the past decade, significant advancements in computational power, the integration of novel methodologies for data analysis, and the availability of vast new information about individuals and organizations, facilitated by the widespread adoption of the internet, have paved the way for the development and implementation of artificial intelligence (AI) techniques across various industries, including financial institutions. In the realm of banking, the relevance of AI is even more pronounced, particularly due to the growing utilization of larger and more comprehensive data sets for credit risk modeling. Traditionally, credit risk evaluation has relied heavily on client data modeling, employing techniques such as linear regression, logistic regression, decision trees, and others, along with data sets encompassing financial, behavioral, sociological, geographic, and sectoral aspects. However, the emerging challenge for credit risk managers is to harness the power of the new AI toolbox to enhance the predictive capabilities of models while addressing concerns related to interpretability and ethical dilemmas.

Contributors to this book include esteemed university researchers, experienced risk managers working in banks and other financial intermediaries, and renowned consultants. The topic of credit risk is of paramount importance in the financial industry, and this book stands as one of the pioneering works in this field, offering valuable case studies alongside practical problems and solutions. By examining the latest advancements in AI and their application in credit risk management, this book provides a comprehensive framework for financial professionals and researchers to navigate the complex landscape of risk assessment and decision-making.

Weight: 303g
Dimension: 210 x 148 (mm)
ISBN-13: 9783031102356
Edition number: 1st ed. 2022