New tools and technologies are used fraud prevention organizations for harnessing data to explore fraud cases before they occur. Frauds are not diminishing in intensity and constantly changing shape. Real world is different than what we expect it to be. And fraud analytics can help solve this problem.
By adopting and developing advanced fraud detection and prevention systems, organizations do manage to reduce losses due to fraud as fraudsters have a tendency to look for and find new ways . Therefore , combating fraud by building powerful and complex early detection mechanisms is not a pointless effort.
Early detection is a key factor in lessening fraud cases, but it involves more high–tech methodologies than detecting fraud after it occurs.
Anomaly detection and rules-based methods have been in widespread use to combat fraud for years. They’re powerful tools, but they still have their limits. Adding big data analytics to this mix can significantly expand fraud detection capabilities. Big data analytics provide more advanced solutions compared to rules-based methods. It can also be converted to rules that can help fine tune controls for constant improvement. That’s a big deal for companies deep diving into data—data that could be put to better use.
We are experienced in fraud analytics that can be categorized as:
Credit Card Fraud: Has two main sub types as application fraud involving obtaining card by using false personal information and behavioral fraud where details of legitimate cards are obtained fraudulently and sales are made on it.
Insurance Fraud: Has two main sub types as issuer including selling policies from nonexistent companies and buyer including exaggerated claims, falsified medical history and faked damage.
Product Warranty Fraud: Intentionally wrongly claiming compensation is called product warranty fraud.
Fraud analytics combines analytic technology and techniques with human interaction to help detect potential improper transactions, such as those based on fraud and/or bribery, either before the transactions are completed or after they occur. The process of fraud analytics involves gathering and storing relevant data and mining it for patterns, discrepancies, and anomalies. The findings are then translated into insights that can allow a company to manage potential threats before they occur as well as develop a proactive fraud detection environment.
If you’re frustrated that a massive amount of fraud-related information is going unused, it’s worth giving fraud analytics another look.