Camouflaged fraud detection in domains with complex relationships

We describe a data mining system to detect frauds that are camouflaged to look like normal activities in domains with high number of known relationships. Examples include accounting fraud detection for rating and investment, insider attacks on corporate networks, and health care insurance fraud. Our goal is to help analysts who are overwhelmed with information about companies or on-line system access logs or insurance claims to focus their attentions on features that cause damage in the future. We focused on accounting fraud where the task is to detect the subset of companies that were potentially committing accounting fraud within the total population of public companies that file quarterly and annual filings with the Securities and Exchange Commission (SEC). Using (a) Representation of changes, (b) A mix of decision tree learning, locally weighted logistic regression, k-means clustering, and constant regression in a two phase pipe line, we developed models that rank companies based ...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Sankar Virdhagriswaran, Gordon Dakin
• Keywords: Accounting Fraud, Accounting Fraud Detection, Care Insurance Fraud, Data Mining, KDD 2006