Data Analysis Techniques for Fraud Detection

Definition

Data analysis techniques for fraud detection refer to the techniques that make use of statistical techniques and artificial intelligence to detect fraud in any company. Fraud is defined as an intentional act of an individual or more persons to deny another person or organization of something that is of value for their own gain. Every year, the number of fraud cases is increasing and the reason for this may be attributed to technological development.

Overview

For detecting fraud, traditional data analysis techniques have been used for a long time. These techniques needed time and complex investigations and they dealt with various areas of knowledge like economics, law, financial and business practices.

The data analysis techniques used for fraud detection were first employed by banks, telephony companies and insurance companies. The Falcon fraud assessment system in banking industry is one of the early examples of successful data analysis techniques implementation. In retail industries, digitalized CCTV and POS data of most vulnerable transactions to fraud are used.

Detection and prevention of fraud is not an easy task, so special techniques like data analysis techniques are used for detecting and preventing fraud. Some of these techniques exist in the domain of data mining, Discovery in Databases, machine learning and statistics. These techniques provide proper solutions in different types of fraud crimes.

Types of data analysis techniques for fraud detection

Data analysis techniques for fraud detection mainly fall into two main classes – artificial intelligence and statistical techniques. Some of the examples of statistical data analysis techniques are as follows:

• Techniques of data preprocessing for detecting, validating, correcting error and filling up of incorrect and missing data.
• Computation of user profile
• Matching algorithms for detecting incongruities in the behavior of users or transactions when compared to earlier known profiles or models.

Data analysis techniques for fraud detection in the area of accounting

There are some forensic accountants who specialize in forensic analytics that involve procuring and analyzing of electronic data for reconstructing, detecting or for supporting claims of financial frauds. The main steps followed in this data analysis technique are – collecting data, preparation of data, analyzing data and reporting.