The primary objective of the study is to classify the defaulters and non-defaulters of auto loans based on their specific personality traits, viz., `money attitude' and `income dimensions'. However, the aim is not only to classify, but also to understand the root of the defaulter behavior. Therefore, the study probes deeper into the attitude and perception variables of the consumers who avail loan facilities. The study, based on the customers of an MNC bank, using a survey in two metropolitan cities in India, suggests that the constructs of personality traits, such as money attitudes, power-prestige and anxiety, actually enhance the intention and actual usage of loan facility, and that the same can be the predictors of default behavior at a significant level. The results and the model developed can be used as the basis for decision making while processing loans.

The consumer culture has been growing fast in India. Easy access to credit and loan facility is one of the causes of the increase in the pace of the durable market growth. With retail customers being the focus, banks and financial institutions are eager to provide housing finance, credit cards, auto financing and so on. The list of players in auto financing is ever growing, which includes Tata Finance, Ashok Leyland Finance, GE Countrywide, and Sundaram Finance. Now banks, both public and private sector, have jumped into the auto loan segment (see Appendix 1 for a brief commentary on auto financing market in India).

One concern, which is inherent, imminent, and perhaps the most important in financing business, is how to handle the defaulting customers. Since it is always better if one can identify a potential defaulter in advance, there exists a need for a mechanism to identify the potential defaulters, i.e., a predicting model. The existing literature suggests various quantitative and qualitative models to assess the default behavior; however, almost all of them are built upon some broad financial indicators. These indicators include variables based on the credit history, income and other tangible assets owned by the customer availing of a loan. Little emphasis is laid on classification of the defaulters and non-defaulters based on their personality traits. The present study is an attempt to fill this gap. This is done by developing a model, based on the attitude and perception variables of the consumers, to help an MNC bank take decisions while dispersing loans.

Research concerning the prediction of default behavior has been based on two broad approaches: (1) Qualitative assessment of practical approaches used by managers; and (2) Quantitative (statistical and mathematical) modeling (Ohlson, 1980). The greatest development in recent years has been the use of quantitative models of default risk. American banks have been aggressively using business analytics to ascertain default behavior (Carrol and Zeltkevic, 2007). There have been studies which have used loan level data (loan level data includes the amount of loan, duration and amount of EMI, credit history and income levels) for the assessment of default behavior of the customers (Kalbfleisch and Prentice, 1980; and Cox and Oakes, 1984).

Bank Management Journal, MNC Banks, Decision Making Process, Qualitative Assessments, Regression Modeling, Economic Factors, Mortgage Policies, Loan Repayments, Loan Applications, Quantitative Modeling, Social Factors.