Role of Data Analytics in Fraud Management
Fraud is one of the fastest growing challenges being faced by businesses today. There are fraudsters who ‘bend the truth’ relating to data or information required at the point of application, verification or claim. At the other end of the scale, there are organised fraud rings – the racketeers of today’s world - whose sole premise is to deceive businesses, public authorities, or consumers intentionally for either financial or commercial gain. Fallout from fraud hits business reputation, instils mistrust in genuine customers’ minds and in extreme cases, could lead to the businesses folding.
There is unanimity among financial institutions that fraud incidents are continuously increasing. Also, recovery is as low as 25 percent in more than half of fraud cases. It is seen that maximum losses are incurred by retail banking portfolios followed by corporate banking and priority lending portfolios. Some of the important contributing factors to fraud are lack of tools to identify red-flags, scientific investigations, and oversight by people responsible.
There is a lot of data with organisations today which is unstructured, and here data analytics plays a vital role. Proactively extracting, analyzing and interpreting data can aid in detection of potential fraud and thereby can support the implementation of effective fraud management programmes.
For instance, banks can process loan applications while referring to a past reject list rather than face the consequences of a fraudulent application after disbursing the loan. We are seeing organisations adopting automated/rule based methods of monitoring potentially fraudulent behaviour – this trend will ensure enhanced efficiency of processes within an organisation. Detecting fraud at the point of application is the way forward for organisations. There is a need for complete coverage of the population rather than a specific sample though closed user groups. This would lead to a wider scope for fraud detection and will help the industry as a whole. It is seen that most frauds happen at the time of application, hence early detection of fraud in most analytics solutions can help businesses quickly identify potentially fraudulent behaviour before the fraud materialises.
The use of analytics in fraud management enables businesses to conduct safe business with their good customers while denying ever-persistent fraudsters. Today, there are application inconsistency services available to tackle industry wide frauds.
For example, there are solutions available that enable banks to check internal inconsistencies and past records within the bank, across branches. There is also an option to check for inconsistencies and past records not just within a bank, but across banks which have signed up to be members of the closed user group. Use of analytics in fraud management has revealed that, out of the various fraud types, fraudulent contact information contributes to 18 percent, while use of fictitious identity and repeated attempts from already identified individuals contributes to 15 percent and 19 percent respectively.
Rule-based fraud detection techniques are critical as fraudsters are fairly capable of finding innovative ways of breaking through challenging defences. With businesses being more vulnerable than ever before, the significance of fraud management has never been more pronounced and decision analytics is a step in the right direction.