Combating unemployment insurance fraud with big data analytics
The last few years have seen many data and information breaches from both public and private organizations. One breach of particular concern involves personally identifiable information (PII). The Department of Homeland Security defines PII as any information that permits the identity of individuals to be directly or indirectly inferred, including any information that is linked or linkable to that individual. This includes first and last name, Social Security number, date and place of birth, biometric records and more.
One of the ways criminals are using PII is in unemployment insurance scams. According to an article by Chad Barnes, identity protection services do next to nothing to stop the improper use of government benefits, including unemployment benefits. Unemployment benefits (also known as unemployment insurance or unemployment compensation) are made by the government to individuals who register as unemployed, are out of work and are actively seeking work. Each country is different in the percentage of the individual’s previous salary that is provided; however, in the United States, individuals generally receive 40 percent to 50 percent of their previous pay for an average of six months.
Unemployment insurance fraud scams generally consist of individuals collecting benefits when they are technically no longer eligible. This type of fraud is often conducted by individuals on their own account. However, the amount of information being stolen in breaches and then sold in underground markets is opening new avenues for criminals to commit fraud. In the past 10 years, organized crime groups have begun to work smarter. It is safer, more economically viable and involves much less risk for these groups to purchase stolen PII and use them for financial gain. The worst part of this scam is that victims often do not know their information is being exploited in this way until they are approached by a government agency for filing unemployment claims while still employed, or when they themselves require unemployment benefits and are informed that they are not illegible.
How can this type of fraud be stopped? The key to stopping this kind of fraud is data. While it is a challenge to move through multiple data types and platforms, analytics can help by sifting through large amounts of claimant data to identify fraud. Government and social agencies store ample amounts of data within their systems. These siloed platforms work to the benefit of the criminals, not the agencies trying to stop fraud, waste and abuse. Examining the information within these entities enables big data analytics to identify linkages from previous unknowns.
These analysis systems can identify individuals’ behavioral patterns, highlighting anomalies. While anomalies do not always mean fraud, waste or abuse, they do highlight behavioral changes worth noting. Using multi-sourced data analysis allows investigators to uncover more fraud, waste or abuse at a higher speed with less time and effort.
Fully integrated solutions focused on identifying and stopping fraud, waste and abuse help organizations combat the entire lifecycle of unemployment insurance fraud. IBM’s Counter Fraud Management Solution is designed to help prevent and end fraud, while detecting and identifying prior fraud, waste and abuse instances to stop improper payments from taking place and keep organizations and victims safe.