AN APPROACH OF ESTIMATING THE PROBABILITY OF BEING GOOD FOR NEW BORROWERS

Vesela Mihova and Velizar Pavlov

Economy & Business Journal, 2017, vol. 11, issue 1, 200-208

Abstract: Statistical models are commonly used in the banking industry in order to assess the credit risk associated with the approval of people applying for certain products (loans, credit cards, etc.). Based on data from the past, these models try to predict what will happen in the future. This work has studied the causal link between the conduct of an applicant upon payment of the loan and the data that he completed at the time of application. A linear regression is used to estimate the probability of being good for new borrowers, and a scorecard is obtained from the linear model to assess new customers in the time of application.

Keywords: credit risk; modelling; scorecards; data analysis (search for similar items in EconPapers)
JEL-codes: A (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:isp:journl:v:11:y:2017:i:1:p:200-208

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