A multi-objective approach for the prediction of loan defaults

  • Authors:

  • Oluwarotimi Odeh;Praveen Koduru;Allen Featherstone;Sanjoy Das;Stephen M. Welch

  • Affiliations:

  • Department of Agriculture and Human Ecology, 101C Owens Hall, Virginia State University, Petersburg, VA 23806, United States;Department of Agricultural Economics, 313 Waters Hall, Kansas State University, Manhattan, KS 66506, United States;Department of Electrical and Computer Engineering, 2063 Rathbone Hall, Kansas State University, Manhattan, KS 66506, United States;Department of Agricultural Economics, 313 Waters Hall, Kansas State University, Manhattan, KS 66506, United States;Department of Agronomy, 3728 Throckmorton Hall, Kansas State University, Manhattan, KS 66506, United States

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

Quantified Score

Hi-index 12.06

Visualization

Abstract

Credit institutions are seldom faced with problems dealing with single objectives. Often, decisions involving optimizing two or more competing goals simultaneously need to be made, and conventional optimization routines/models are incapable of handling the problems. This study applies the Fuzzy Simplex Generic Algorithm (a multi-objective optimization algorithm) in generating decision rules for predicting loan default in a typical credit institution. Empirical results show that the best indicators of default status are observed when repayment capacity and owners equity are low and the working capital is either low or high. Also, the two worst rule indicators are low repayment capacity, high owners' equity and medium working capital or medium repayment capacity, low owners' equity and high working capital.