Data analysis techniques for fraud detection

Fraud is a billion-dollar business and it is increasing every year. The PwC global economic crime survey of 2016 suggests that more than one in three (36%) of organizations experienced economic crime.

Fraud involves one or more persons who intentionally act secretly to deprive another of something of value, for their own benefit. Fraud is as old as humanity itself and can take an unlimited variety of different forms. However, in recent years, the development of new technologies has also provided further ways in which criminals may commit fraud. In addition to that, business reengineering, reorganization or downsizing may weaken or eliminate control, while new information systems may present additional opportunities to commit fraud.

Traditional methods of data analysis have long been used to detect fraud. They require complex and time-consuming investigations that deal with different domains of knowledge like financial, economics, business practices and law. Fraud often consists of many instances or incidents involving repeated transgressions using the same method. Fraud instances can be similar in content and appearance but usually are not identical.

The first industries to use data analysis techniques to prevent fraud were the telephony companies, the insurance companies and the banks (Decker 1998). One early example of successful implementation of data analysis techniques in the banking industry is the FICO Falcon fraud assessment system, which is based on a neural network shell.

Retail industries also suffer from fraud at POS. Some supermarkets have started to make use of digitized closed-circuit television (CCTV) together with POS data of most susceptible transactions to fraud.

Internet transactions have recently raised big concerns, with some research showing that internet transaction fraud is 12 times higher than in-store fraud.

Fraud that involves cell phones, insurance claims, tax return claims, credit card transactions etc. represent significant problems for governments and businesses, but yet detecting and preventing fraud is not a simple task. Fraud is an adaptive crime, so it needs special methods of intelligent data analysis to detect and prevent it. These methods exist in the areas of Knowledge Discovery in Databases (KDD), Data Mining, Machine Learning and Statistics. They offer applicable and successful solutions in different areas of fraud crimes.

Techniques used for fraud detection fall into two primary classes: statistical techniques and artificial intelligence. Examples of statistical data analysis techniques are:

Some forensic accountants specialize in forensic analytics which is the procurement and analysis of electronic data to reconstruct, detect, or otherwise support a claim of financial fraud. The main steps in forensic analytics are (a) data collection, (b) data preparation, (c) data analysis, and (d) reporting. For example, forensic analytics may be used to review an employee's purchasing card activity to assess whether any of the purchases were diverted or divertible for personal use. Forensic analytics might be used to review the invoicing activity for a vendor to identify fictitious vendors, and these techniques might also be used by a franchisor to detect fraudulent or erroneous sales reports by the franchisee in a franchising environment.

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