Credit Risk Scorecards


Book Description

Praise for Credit Risk Scorecards "Scorecard development is important to retail financial services in terms of credit risk management, Basel II compliance, and marketing of credit products. Credit Risk Scorecards provides insight into professional practices in different stages of credit scorecard development, such as model building, validation, and implementation. The book should be compulsory reading for modern credit risk managers." —Michael C. S. Wong Associate Professor of Finance, City University of Hong Kong Hong Kong Regional Director, Global Association of Risk Professionals "Siddiqi offers a practical, step-by-step guide for developing and implementing successful credit scorecards. He relays the key steps in an ordered and simple-to-follow fashion. A 'must read' for anyone managing the development of a scorecard." —Jonathan G. Baum Chief Risk Officer, GE Consumer Finance, Europe "A comprehensive guide, not only for scorecard specialists but for all consumer credit professionals. The book provides the A-to-Z of scorecard development, implementation, and monitoring processes. This is an important read for all consumer-lending practitioners." —Satinder Ahluwalia Vice President and Head-Retail Credit, Mashreqbank, UAE "This practical text provides a strong foundation in the technical issues involved in building credit scoring models. This book will become required reading for all those working in this area." —J. Michael Hardin, PhD Professor of StatisticsDepartment of Information Systems, Statistics, and Management ScienceDirector, Institute of Business Intelligence "Mr. Siddiqi has captured the true essence of the credit risk practitioner's primary tool, the predictive scorecard. He has combined both art and science in demonstrating the critical advantages that scorecards achieve when employed in marketing, acquisition, account management, and recoveries. This text should be part of every risk manager's library." —Stephen D. Morris Director, Credit Risk, ING Bank of Canada




NCUA Examiner's Guide


Book Description




The Federal Reserve System Purposes and Functions


Book Description

Provides an in-depth overview of the Federal Reserve System, including information about monetary policy and the economy, the Federal Reserve in the international sphere, supervision and regulation, consumer and community affairs and services offered by Reserve Banks. Contains several appendixes, including a brief explanation of Federal Reserve regulations, a glossary of terms, and a list of additional publications.







Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance


Book Description

This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.




Prudential Supervision


Book Description

Since banking systems play a crucial role in maintaining the overall health of the economy, the adverse effects of poorly supervised systems may be quite severe. Without some form of vigilant external oversight, banking systems could fall prey to excessive risk taking, moral hazard, and corruption. Prudential supervision provides that oversight, using government regulation and monitoring to ensure the soundness of the banking system and, by extension, the economy at large. The contributors to this thoughtful volume examine the current state of prudential supervision, focusing on fundamental issues and key pragmatic concerns. Why is prudential supervision so important? What kinds of excess must it guard against? What particular forms does it take? Which of these are the most effective deterrents against mismanagement and system overload in today's rapidly shifting financial climate? The contributors foresee a continued movement beyond simple regulatory rules in banking and toward a more active evaluation and supervision of a bank's risk management practices.




Estimating the Costs of Financial Regulation


Book Description

Staff Discussion Notes showcase the latest policy-related analysis and research being developed by individual IMF staff and are published to elicit comment and to further debate. These papers are generally brief and written in nontechnical language, and so are aimed at a broad audience interested in economic policy issues. This Web-only series replaced Staff Position Notes in January 2011.




Bank Failure


Book Description




FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk


Book Description

Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.