Journal of Advanced Statistics

Download PDF (1150.7 KB) PP. 35 - 49 Pub. Date: December 15, 2019

DOI: 10.22606/jas.2019.44002

• Jimbo Henri Claver^*
  Department of Applied Mathematics and Statistics, AUAF & Waseda University, Tokyo, Japan
• Ngongo Isidore Séraphin
  Department of Mathematics, ENS, University of Yaoundé I , Cameroon
• Dongmo Tsamo Arthur
  Department of Mathematics, University of Yaoundé 1, Cameroon
• Andjiga Gabriel Nicolas
  Department of Mathematics, University of Yaoundé 1, Cameroon
• Etoua Rémy Magloire
  Department of Mathematics, Higher National Polytechnic School. Yaoundé 1, Cameroon

The risk of non-repayment of bank credit is a variable that banks are seeking to master in order to save their profitability and to protect themselves against bankruptcy. In this article, we have shown how to model the risk using tools for the decision making purposes using mathematical techniques of the method of Scoring. We construct a score function capable of minimising the probability that a client may not repay the credit at the fixed date. The construction of such function is done through Fischer discriminant analysis and the logistic regression. The methodology used inthis work relies on statistical analysis techniques and the probability of Scoring. Finally we applied our approach to a given company and found that the risk of non-repayment of the bank credit depends mainly on the loans ratios, global cash flow, global indebtedness, capital funds and net result, capital funds.

Banks, risks of non-repayment, method of scoring, Fischer discriminant analysis, logistic regression, score function.

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