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Fraud Investigation and Data Analytics go hand in hand

Fraud investigations go beyond the images of private detectives sleuthing surveillance and suspect interviews. The majority of cases are solved through the interpretation of documents and business portfolios. Data analytics plays a critical role in the work of a fraud investigator.

Data analytics is the science of examining raw data with the objective of drawing conclusions about that information. Data analytics is used across many industries to enable corporations and business entities to make better business decisions. In academia, particularly the sciences, data analytics are conducted to verify or disprove existing models or theories.

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Conventional methods of data analysis have long been utilized to uncover fraudulent activity. Investigators for the Securities Exchange Commission (SEC), for instance, use data analytics to identify potentially fraudulent activity such as front running, insider trading, fraudulent investment performance reporting, and window dressing. They are essential elements to complex and time-consuming investigations that deal with different domains of knowledge; these can include financial, economic, business practices and legal aspects. Fraud often consists of many incidents involving repeated transgressions using the same method. Fraud instances can be similar in content and appearance but usually are not identical.

Two forms of data analytics are frequently utilized in fraud investigations; these are link analysis and aberrational performance detection. Link analysis is a process which looks for relationships between two disparate data sources; this is particularly important in investigations involving insider trading. Using link analysis software on thousands of lines of data, investigators are able to quickly detect all instances in which the two suspects had a phone call with someone in common. This is a great improvement on the older non-technical methods, where investigators would have to attempt to look at each record individually.

Aberrational performance detection is a form of data analytics which focuses on unexpected performance to both identify fraudulent activity candidates as well as to ensure compliance with certain regulations as part of an examination. An example of aberrational performance detection can be exemplified in an investigation into a suspicious hedge fund. In this hypothetical scenario, the hedge fund’s recorded results were significantly better than its peers throughout both good and bad markets. Investigators, through such detection, would be able to determine that the hedge fund’s actual performance was significantly worse than its peers. Aberrational performance reviews such as these have been critical in identifying intentional valuation misstatements, Ponzi schemes, and other alleged illicit activities before they otherwise would have been discovered.

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Fraudulent activity can take many shapes and sizes. However, data analytics is an investigative tool that can provide results in almost any examination. There is, after-all, no such thing as the perfect crime. Through methods such as data analytics, fraud investigators are able to seize the critical evidence that is inevitably left behind. Fraud investigation and data analytics certainly go hand in hand.

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