Commercial Loan Risk Management, Credit-Scoring, and Pricing: The Need for a New Shared Database

By McAllister, Patrick H.; Mingo, John J. | Journal of Commercial Lending, May 1994 | Go to article overview

Commercial Loan Risk Management, Credit-Scoring, and Pricing: The Need for a New Shared Database


McAllister, Patrick H., Mingo, John J., Journal of Commercial Lending


The commercial loan risk management process, as currently practiced, may be divided into four parts: risk rating, risk pricing, risk monitoring, and portfolio composition management. There is general agreement among the bankers interviewed for this study that each stage in the current process could be improved if better data were available to support the analytical techniques used. It is in this incremental improvement of current processes that the first benefits from an improved database would be seen. The most sophisticated managers in the industry also realize that an analytical approach to portfolio diversification would offer important additional risk-mitigating benefits. However, widespread adoption of analytical tools for portfolio diversification will not be possible unless the industry has access to an adequate database on loan performance.

The first part of this article briefly reviews typical techniques used for implementing each of the four components of the commercial loan risk management process, with an emphasis on the following two areas of inquiry:

1. How the current process could be improved through the availability of better data.

2. How the process itself could be changed to embody a more analytically sound, portfolio-oriented view of risk management.

The second part of this article describes the database that would be needed to implement more reliable, empirically based risk management procedures, either by making incremental improvements in the present basic process or by moving to a portfolio-based approach. A brief summary of the benefits of the proposed database is also included.

Risk Rating

Most larger institutions rate potential credit risks via a process that utilizes complex loan officer analyses as well as computerized risk-rating or credit-scoring models, which are, in theory, similar to the credit-scoring models widely used for consumer loans. Some institutions do not use a credit-scoring model for rating commercial loans because of the perception that such models are not sufficiently accurate. Indeed, the most widely used credit-scoring model was not derived from experience with commercial loan defaults but, rather, from experience with defaults in the public bond markets.

An alternative, recently developed credit-scoring model uses option-pricing theory to relate movements in the price of a borrowing firm's equity to an estimate of the distribution of the market value of the firm's assets. This asset value distribution, in turn, is used to estimate the probability of the firm becoming insolvent. The firm is considered insolvent when its asset value falls below the cumulative value of debt payments due.

The model relies on two assumptions that have not yet been empirically verified, although these assumptions underlie much of the theoretical framework of risk management:

1. Short-run movements in the price of equity accurately reflect changes in the underlying economic value of the borrowing firm.

2. The volatility of the stock price is related to the volatility of the underlying asset value of the issuing firm in a predictable manner.

A common feature of all currently used credit-scoring models is that the coefficients of the models were not developed using data on the performance of actual bank loans. In addition, no model, in our view, has been subjected to a sufficiently rigorous attempt to measure its accuracy in distinguishing between high-risk and low-risk commercial bank loans. Some individual banks have tested the commercially available scoring models against the judgments of their loan officers; however, neither the loan officers' subjective ratings nor the ratings generated from the credit-scoring models have been tested adequately against actual loan performance.

There is a good reason for banks' failure to validate empirically their credit-scoring approaches: the lack of an appropriate database. …

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