Bio-inspired intelligence for credit scoring. (11th February 2011)

• Record Type: Journal Article
• Authors: Goletsis, Yorgos; Exarchos, Themis P.; Katsis, Christos D.
• Is Part Of: International journal of financial markets and derivatives. Volume 2:Number 1/2(2011)
• Page Start: 32
• Page End: 49
• Publication Date: 2011-02-11
• Subjects: credit scoring -- biologically inspired algorithms -- ant colony systems -- ACS -- artificial immune systems -- particle swarm optimisation -- PSO -- classification -- neural networks -- NNs -- support vector machines -- SVMs -- decision trees -- logit -- QDA; Derivative securities -- Mathematical models -- Periodicals; Capital market -- Periodicals; 332.605
• Languages: English
• ISSNs: 1756-7130

Abstract

The application of quantitative techniques for the determination of credit worthiness, i.e., the credit scoring, is a major research field for bankers and academics as it can bring about significant savings to finance institutions whilst minimising their exposure to risk. In the current work, the applicability of recent developments in machine learning techniques is examined; specifically biologically inspired techniques mimicking natural ants, bird flocking and immune system cells are applied. Experimental results are presented on three real world credit scoring datasets. Comparative results with commonly used artificial intelligence and statistical classifiers verify the suitability of the newly examined methods.