Summary
Risk assessment of credit portfolios is of pivotal importance in the banking industry. The bank that has the most accurate view of its credit risk will be the most profitable. One of the main pillars in the assessing credit risk is the estimated probability of default of each counterparty, i.e., the probability that a counterparty cannot meet its payment obligations in the horizon of one year. A credit rating system takes several characteristics of a counterparty as inputs and assigns this counterparty to a rating class. In essence, this system is a classifier whose classes lie on an ordinal scale. In this paper we apply linear regression ordinal logistic regression, and support vector machine techniques to the credit rating problem. The latter technique is a relatively new machine learning technique that was originally designed for the two-class problem. We propose two new techniques that incorporate the ordinal character of the credit rating problem into support vector machines. The results of our newly introduced techniques are promising.
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Extract
Support Vector Machines in Ordinal Classification: An Application to Corporate Credit Scoring
1. Introduction
A large corporation that wants to apply for a loan, obviously shops around to negotiate the best possible price and terms. The provider of such a loan, often a bank, wants to make a decent profit from this loan. Basically, this profit consists of two parts. Interest payments form the basis of the profit. Even more important, however, is whether the corporation will possibly be able to pay off the loan itself. Not surprisingly, these two aspects are tightly coupled. The larger the risk that a corporation might not be able to meet its payment obligations in the future, the more interest it needs to pay.A bank therefore assesses the risk it takes when providing a loan to a corporation (or any other customer). If the bank overestimates this risk, the price of the loan will be too high compared to competitor banks, and the corporation will apply for a loan elsewhere. On the other hand, if the bank underestimates the risk, the bank will issue loans to dubious debtors for a price that is too low.Careful risk assessment of credit portfolios is therefore of pivotal importance in the banking industry. The bank that has the most accurate view of its credit risk will be the most profitable. Moreover, since banks are the cornerstones of a country's economy, inaccurate credit risk assessment can have a tremendou...See the full content of this document
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