RISK CLASSIFICATION OF SME LOANS – IMPACT ANALYSIS FOR THE USE

OF DIFFERENT RATING MODELS BY BANKS

by Beat Bernet2 _{and Simone Westerfeld}2

Corresponding Author:

Simone Westerfeld Senior Lecturer

Swiss Institute of Banking and Finance University of St Gallen Rosenbergstr. 52 CH-9000 St.Gallen SWITZERLAND Tel.: +41 71 224 70 39 Fax: +41 71 224 70 88 E-mail: [email protected]

Abstract

Rating models used by banks to evaluate the creditworthiness of SME clients still differ substantially with regard to the underlying rating philosophy, system architecture and calibration. Looking at bank ratings from an SME perspective as a current or potential future borrower, the question arises as to whether the different designs of bank internal rating models lead to different rating results and subsequently to different credit conditions. This problem is the basis of the research question at the heart of this article. In our empirical study, we rate a representative sample of Swiss SME clients by different rating models. We first test whether different rating models belonging to different types of modeling architectures lead to different risk classifications (rating results), even though identical input data is used. This first hypothesis can be supported based

1 _{A later version of this conference paper was published in German in the "Zeitschrift für} Betriebswirtschaft (ZfB), 78. Jg. (2008), H.10" as "KMU-Ratingmodelle und Ratingqualität: Auswirkungen der Ratingarchitektur auf die ex-ante Risikoklassifikation von KMU- Kreditkontrakten"

102 Abstract

on our empirical findings and may implicate the risk of rating model arbitrage against banks. The second hypothesis tested is based on empirical findings from other studies, analyzing whether the inclusion of qualitative information leads to significantly higher rating marks compared to ratings solely based on quantitative information. Our findings do not support the second assumption.

1. Introduction

A bank’s decision to grant a loan and the according credit conditions (risk-adjusted prices, volume, tenor, collateral) to an SME are of significant importance for single debtors and also for the economy as a whole. The efficiency and quality of the credit rating and selection process at banks influence both the credit supply of the economy and long-term provisioning rates, thereby affecting the profitability of banks and the stability of the entire financial system.

With the implementation of the New Capital Accord in 2007, the discussion of bank internal rating models has increased both in academia and practice. The BIS paper specifies conceptual requirements for internal rating systems that need to be matched, so that an internal rating system can be accepted by supervisors (Basle Committee (2006)). However, there is a substantial degree of freedom left within these guidelines. Consequently, rating models used by banks still differ substantially with regard to the underlying rating philosophy, system architecture and calibration.

Looking at bank ratings from an SME perspective as a current or potential future borrower, the question arises as to whether the different designs of bank internal rating models lead to different rating results and therefore to different credit conditions, even though input data remains unchanged. Besides the impact for SMEs, “flawed” rating decisions might also lead to the mispricing of SME loans, which could result in adverse selection effects and a loss of market share for the respective banks.

The rating model’s design is determined by the underlying rating philosophy. The rating philosophy itself is again determined by the purpose of the rating, the rating object and time dimensions. In the context of SME loans, the purpose

103 Introduction

of the rating for the individual loan exposure is usually the determination of probabilities of default (PD). PDs can be used as a tool to control the risk exposure in a SME portfolio and as a basis for economic applications such as pricing and economic capital attribution (Nakamura/Roszbach (2005)). Usually, companies are defined as rating objects, i.e. a counterparty rating is applied (in contrast to transaction ratings). With regard to the time dimension, banks differentiate between “through-the-cycle” ratings and “point-in-time” ratings, whereas empirical studies show that the latter is more important in banking practice (Amato/Furfine (2004)). The analyses presented in this article therefore focus on “point-in-time” counterparty rating models for SMEs, which attribute a certain PD to a borrower.

Following the definition in the Basle paper, a rating system covers all methods, processes, controls, data and IT systems that are necessary to calibrate rating models and derive PD estimates (Basle Committee (2006)). In this article, we use the term rating model as being a part of the overall rating system. A rating model is used to perform risk classifications in terms of ratings. The architecture of a rating model describes the elements of the model and their relationships with each other.

Typically, a PD is attributed to a specific rating class. Therefore, a rating describes the ex-ante quantification of the risk, that the rating object does not perform with regard to predefined credit events, typically paying back the liabilities within a certain time frame. In practice, the attributed PDs are often used as a basis for loan decisions and the application of risk premiums and credit limits.

The article is organized as follows: Based on a literature overview, we develop two hypotheses as a framework for our empirical analysis. This is followed by the empirical analysis in section 3, where we rate a credit portfolio of 435 counterparties using three different real rating models with different architectures. We test these two hypotheses with regard to the impact of different rating model architectures on risk classification and discuss the results in section 4. In the two concluding sections, we interpret the results and develop a conclusion and potential impacts.

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