A Cooperative Classification System for Credit Scoring

Credit scoring (CS) is an important process in both banking and finance. CS is a score measure that helps lenders and creditors to see how good the borrower is. It indicates how likely the borrower is to pay back the owed debt, based on its past borrowing behavior. For instance, this score can be used to avoid risk and provide an objective analysis of the applicant’s creditworthiness which reduces credit risk. Also CS allows the automation of the lending process which leads to increase the speed and consistency of the borrower’s application. In this paper, we propose to use agents’ technology to develop a cooperative classification system for credit scoring. Empirical studies are conducted on five well-known financial datasets. We distribute the complex classification problem on a set of intelligent and cooperative agents where each agent evaluates a given machine learning algorithm on the considered dataset. The use of agents in the classification process provides a high degree of flexibility and allows for a faster time scale of data analysis.

[1]  Robert C. Holte,et al.  Very Simple Classification Rules Perform Well on Most Commonly Used Datasets , 1993, Machine Learning.

[2]  J. Wiginton.  A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior , 1980, Journal of Financial and Quantitative Analysis.

[3]  David J. Hand,et al.  Statistical Classification Methods in Consumer Credit Scoring: a Review , 1997 .

[4]  J. Ross Quinlan,et al.  Simplifying Decision Trees , 1987, Int. J. Man Mach. Stud..

[5]  Jin-Kao Hao,et al.  A Hybrid GA/SVM Approach for Gene Selection and Classification of Microarray Data , 2006, EvoWorkshops.

[6]  Vijay S. Desai,et al.  A comparison of neural networks and linear scoring models in the credit union environment , 1996 .

[7]  João Gama,et al.  A new dynamic modeling framework for credit risk assessment , 2016, Expert Syst. Appl..

[8]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[9]  Dalila Boughaci,et al.  Design and implementation of a misused intrusion detection system using autonomous and mobile agents , 2007, EATIS '07.

[10]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[11]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[12]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[13]  Habiba Drias,et al.  Distributed Intrusion Detection Framework based on Autonomous and Mobile Agents , 2006, 2006 International Conference on Dependability of Computer Systems.

[14]  Jianping Li,et al.  An evolution strategy-based multiple kernels multi-criteria programming approach: The case of credit decision making , 2011, Decis. Support Syst..

[15]  Rich Caruana,et al.  Greedy Attribute Selection , 1994, ICML.

[16]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[17]  Hussein A. Abdou.  Genetic programming for credit scoring: The case of Egyptian public sector banks , 2009, Expert Syst. Appl..

[18]  Jonathan Crook,et al.  Support vector machines for credit scoring and discovery of significant features , 2009, Expert Syst. Appl..

[19]  D. Hand,et al.  A k-nearest-neighbour classifier for assessing consumer credit risk , 1996 .

[20]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[21]  Joaquín Abellán,et al.  Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring , 2014, Expert Syst. Appl..