Application of Data Mining Techniques for Financial Accounting Fraud Detection Scheme

Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. The implementation of data mining techniques for fraud detection follows the traditional information flow of data mining, which begins with feature selection followed by representation, data collection and management, pre processing, data mining, post-processing, and performance evaluation. Data mining methods have the capability of detecting fraud because these techniques can use past cases of fraud to build models, which identify and detect the risk of fraud. Financial statement fraud, one of the financial frauds, has reached the epidemic proportion globally. Collapses of high profile companies have left a dirty smear on the effectiveness of corporate governance, quality of financial reports, and credibility of audit functions. Financial statement fraud has become a critical issue in the businesses around the world. The aim of this contribution is to show some data mining techniques for fraud detection and prevention with applications in credit card and telecommunications, within a business of mining the data to achieve higher cost savings, and also in the interests of determining potential legal evidence.