Fraud is inevitable and, as technology evolves and corporations continue to expand globally, fraud schemes also evolve and become more sophisticated. Fraudsters are using technology to their advantage, from manipulating electronic data to concealing fraudulent transactions in large volumes of data in the hope that those transactions are not detected. Data analytics is a critical tool in helping to detect potentially fraudulent transactions and prevent fraudsters from using technology to advance their schemes even further.
Forensic data analytics examines data with regard to incidents of financial crime. The aim is to discover and analyze patterns of fraudulent activities. Forensic data analytics can be used to identify trends, patterns or anomalies in primarily two types of data sets: structured data and unstructured data.
Structured data includes data from vendors, employees, financial transactions and payroll records. Unstructured data is taken from communication and office applications or from mobile devices. This data has no overarching structure and analysis thereof means applying keywords or mapping communication patterns. Forensic data analytics combines information from all these multiple sources, helping investigators quickly identify big-picture discrepancies and connect the dots in a fraud case.
While forensic data tools are powerful digital assets, the ultimate success of any fraud case still relies on the experience and judgment of the investigative team. Our RSM fraud professionals have successfully worked cases across a wide variety of industries, and they are highly skilled in tailoring the use of analytical tools for each specific situation. Additionally, experienced third-party providers, like RSM, should have a high degree of investigative objectivity as well as working relationships with specialized forensic accounting and research providers that are not usually available to a company’s internal team members.