Censored Regression Techniques for Credit Scoring: A Case Study for the Commercial Bank of Zimbabwe (Bulawayo)

Credit creation is the main income generating activity for banks. However this activity involves huge risks to both the lender and the borrower. The risk of a trading partner not fulfilling his or her obligation as per the contract on due date or any time thereafter can greatly jeopardise the smooth functioning of a bank’s business. Credit risk therefore is one of the greatest concerns to most banking authorities and banking regulators. This paper is aimed at coming up with a model that can be used by the Commercial Bank of Zimbabwe in calculating the risk associated with credit scoring. The data set used covered personal loans from January 2010 to January 2012. Linear and Buckley James regression tests were employed to find the explanatory variables influencing time to default and repayment. In investigating customer classification, linear discriminant analysis was applied. Age, marital status, loan purpose and time at current job were found to be linearly related to time to default. Time to repayment was found to be linearly related to age, marital status and loan purpose. 67.5% of the original cases were found to be correctly classified. Buckley James regression out performed linear regression hence it was found to be the most suitable method in determining variables affecting risks in loan lending.

• Publication year

  2014

• Venue

  International journal of economics and finance

• Publication date

  2014-09-26

• Fields of study

  Business, Economics

• Identifiers
  DOI 10.5539/IJEF.V6N10P227
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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