USING THE LISP-MINER SYSTEM FOR CREDIT RISK ASSESSMENT

Abstract

Credit risk assessment, credit scoring or loan applications approval are one of the typical tasks that can be solved using machine learning or data mining techniques. From this perspective, loan applications evaluation is a classification task, in which the final decision can be either a crisp yes/no decision about the loan or a numeric score expressing the financial standing of the applicant. The knowledge to be used is inferred from data about past decisions. These data usually consists off socio-demographic characteristics, economic characteristics (e.g. income, deposit), the characteristics of the loan, and, the loan approval decision. A number of machine learning algorithms can be used for this purpose. In this paper we show how this task can be solved using the LISp-Miner system, a tool that is under development at the University of Economics, Prague. LISp-Miner is primary oriented on mining for various types of association rules, but unlike classical association rules proposed by Agrawal, LISp-Miner introduces a greater variety of different types of relations between left-hand and right-hand side of a rule. Beside this, two other procedures that can be used for classification task are implemented in LISp-Miner as well. We describe the 4FT-Miner and KEX procedures and show how they can be used to analyze data about loan applications. We also compare the results obtained using the presented algorithms with results from standard rule learning methods.