Statistical Methods for Fighting Financial Crimes

Abstract

Financial crimes affect millions of people every year and financial institutions must employ methods to protect themselves and their customers. The use of statistical methods to address these problems faces many challenges. Financial crimes are rare events that lead to extreme class imbalances. Criminals deliberately attempt to conceal the nature of their actions and quickly change their strategies over time, resulting in class overlap and concept drift. In some cases, legal constraints and investigation delays make it impossible to actually verify suspected crimes in a timely manner, resulting in class mislabeling or unknown labels. In addition, the volume and complexity of financial data require algorithms to be not only effective, but also efficiently trained and executed. This article focuses on two important types of financial crimes: fraud and money laundering. It discusses some of the traditional statistical techniques that have been applied as well as more recent machine learning and data mining algorithms. The goal of the article is to introduce the subject and to provide a survey of broad classes of methodologies accompanied by selected illustrative examples.

Keywords:  :

• Anomaly detection
• Classification
• Fraud detection
• Machine learning
• Money laundering