Framework for Credit Risk Management, by Brian Coyle, 2000, Chicago, Ill.: Fitzroy Dearborn.
Framework for Credit Risk Management, by Brian Coyle, deals with one of the most debated subjects in economic literature, especially in the field of banking: how to identify, appraise, and control credit risk. It is a most comprehensive manual that may serve as a guide to credit risk management, but it is a very brief one, for it has been structured as an introductory survey. The main topics of the book are credit risk-the focus being on a series of different configurations of it-and the "canons of lending" (good lending principles).
Despite a limited number of pages (137), which results, as mentioned above, in a small but easily readable book, the volume offers a telling outline of bank credit policies and risk-reduction strategies needed to contain the consequences of credit risk. It also investigates the methods used to face the ex post impact of risk-particularly security forms, covenants, and conditional lending (when the borrower is required to provide loan guarantees)-comparing how banks and nonfinancial companies behave in the case of debtor's default (nonpayment occurrences). The book deals with both trade credit and bank credit, in fact, and analyzes some of the contractual instruments available to cope with counterpart risk, including credit insurance.
Nevertheless, the author seems to follow a traditional style, also showing a preference for a very qualitative approach. As a consequence, he ignores most of the quantitative and recent literature concerned with the management of values at risk and credit risk, except for a few references, somewhere in Chapter 6, to credit scoring.
The first chapter simply defines credit risk and explains the risk-reward trade-off that lenders have to evaluate in order to decide the terms and conditions of a loan.
Chapters 2 and 3 deal with trade credit and bank credit, respectively. The former provides an overview of collective and individual risks that a trading company should consider when credit customers are grouped (misdiversification). …