Fraud is a billion-dollar business and it is increasing every year. Data analysis methods are used to detect and prevent fraud. This requires detailed domain knowledge of financial, economic, business practices, and law to do these kinds of analysis.
Techniques used for fraud activities include:
Supervised learning (e.g. neural networks, support vector machines, decision trees etc.) to model fraudulent activities.
Unsupervised learning techniques such as clustering techniques could be used to model fraud such as
Duplicate transaction analysis
Data quality analysis