Multiple criteria linear programming data mining approach: an application for bankruptcy prediction

Authors:
Wikil Kwak;Yong Shi;John J. Cheh;Heeseok Lee
Affiliations:
Department of Accounting, College of Business Administration, University of Nebraska at Omaha, Omaha, Nebraska;College of Information Science and Technology, University of Nebraska at Omaha, Omaha, Nebraska;Accounting and Information Systems, The University of Akron, College of Business Administration, Akron, OH;Department of Management and Information System, Korea Advanced Institute of Science and Technology, Seoul, Korea
Venue:
CASDMKM'04 Proceedings of the 2004 Chinese academy of sciences conference on Data Mining and Knowledge Management
Year:
2004

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Abstract

Data mining is widely used in today’s dynamic business environment as a manager’s decision making tool, however, not many applications have been used in accounting areas where accountants deal with large amounts of operational as well as financial data. The purpose of this research is to propose a multiple criteria linear programming (MCLP) approach to data mining for bankruptcy prediction. A multiple criteria linear programming data mining approach has recently been applied to credit card portfolio management. This approach has proven to be robust and powerful even for a large sample size using a huge financial database. The results of the MCLP approach in a bankruptcy prediction study are promising as this approach performs better than traditional multiple discriminant analysis or logit analysis using financial data. Similar approaches can be applied to other accounting areas such as fraud detection, detection of tax evasion, and an audit-planning tool for financially distressed firms.