Knowledge-Rich Data Mining in Financial Risk Detection

Authors:
Yi Peng;Gang Kou;Yong Shi
Affiliations:
School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, P.R. China 610054 and CAS Research Center on Fictitious Economy and Data Sciences, Beijing, C ...;School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, P.R. China 610054 and CAS Research Center on Fictitious Economy and Data Sciences, Beijing, C ...;CAS Research Center on Fictitious Economy and Data Sciences, Beijing, China 100080 and College of Information Science & Technology, University of Nebraska at Omaha, Omaha, USA NE 68182
Venue:
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Year:
2009

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Abstract

Financial risks refer to risks associated with financing, such as credit risk, business risk, debt risk and insurance risk, and these risks may put firms in distress. Early detection of financial risks can help credit grantors to reduce risk and losses, establish appropriate policies for different credit products and increase revenue. As the size of financial databases increases, large-scale data mining techniques that can process and analyze massive amounts of electronic data in a timely manner become a key component of many financial risk detection strategies and continue to be a subject of active research. However, the knowledge gap between the results data mining methods can provide and actions can be taken based on them remains large in financial risk detection. The goal of this research is to bring the concept of chance discovery into financial risk detection to build the knowledge-rich data mining process and therefore increase the usefulness of data mining results in financial risk detection. Using six financial risk related datasets, this research illustrates that the combination of data mining techniques and chance discovery can provide knowledge-rich data mining results to decision makers; promote the awareness of previously unnoticed chances; and increase the actionability of data mining results.