Data Mining Applications in Accounting and Finance

Data Mining Applications in Accounting and Finance

Wikil Kwak (University of Nebraska at Omaha, USA), Susan Eldridge (University of Nebraska at Omaha, USA) and Yong Shi (University of Nebraska at Omaha, USA & Chinese Academy of Sciences, China)
Copyright: © 2014 |Pages: 9
DOI: 10.4018/978-1-4666-5202-6.ch056

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Data mining has been used in a variety of business applications, such as consumer buying pattern prediction and credit card default prediction, but recent research studies in accounting and finance have applied data mining techniques for classification and prediction of events such as firm bankruptcy and auditor changes. Data mining may be a useful tool for accounting and finance applications because of the huge volume of financial data that impacts business decisions. In this chapter, we summarize three published research studies in which we applied various data mining applications using accounting and other data for classification and prediction decisions, and we identify important issues to consider when applying current data mining tools.

The three research studies we summarize in this chapter are Kwak, Eldridge, Shi, and Kou (2009), Kwak, Eldridge, Shi, and Kou (2011), and Kwak, Shi, and Kou (2012). In these studies, we evaluate the relative usefulness of a variety of data mining approaches primarily by comparing overall accuracy rates for predicting material weaknesses in internal controls (Kwak et al., 2009), auditor changes (Kwak et al., 2011), and bankruptcy (Kwak et al., 2012). We compare the results of multiple criteria linear programming (MCLP) with those of linear discriminant analysis and Decision Tree based See5 (DT) in Kwak et al. (2009) and with those of classification and regression tree (CART), linear logistic regression (LLR), and Bayesian network in Kwak et al. (2011). The CART, LLR, and Bayesian network applications are three of the 13 data mining models in Witten and Frank’s (2005) WEKA Data Mining Workbench, and Kwak et al. (2012) apply all 13 data mining models.

Key Terms in this Chapter

Internal Controls: Processes or procedures in a firm’s financial reporting system that are designed to address reliability and quality of the data and the reports produced.

Auditor Change: An event characterized by a different auditor (independent accountant or accounting firm) providing the audit opinion on a firm’s annual financial statements compared to the auditor providing the opinion on the firm’s previous year statements.

Bankruptcy: An extreme form of financial distress in which a firm seeks legal protection from its creditors and usually ceases to exist.

Material Weakness: A condition in which one or more of a firm’s internal controls is ineffective and could lead to a material misstatement in the firm’s financial statements.

Financial Statement Variables: Data items (such as current assets, total assets, net income, and the like) that are commonly reported in financial statements or ratios created using such data items.

Data Mining: Finding out meaningful patterns from large amounts of data.

Multiple Criteria Linear Programming: Linear programming using multiple criteria and multiple constraints.

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