Data analytics play a huge role in flagging anomalies in unemployment insurance claims and predicting threats. 

In the wake of Covid-19, there was a global digital transformation, which also led to a shocking rise in cybersecurity breaches and frauds across public and private sectors. The pandemic led to a shrink in the economy resulting in mass unemployment in many countries and in such a scenario, it becomes difficult for people to find new employment opportunities. Many countries have adopted the system of unemployment insurance in which, the state provides wages to unemployed people, especially those who are affected by layoffs,  for a temporary period. 

The federal and state together in the United States introduced an unemployment insurance program, called the Coronavirus Aid, Relief, and Economic Security Act in March 2020 to aid the people out of work because of the pandemic. CARES act extended its benefits towards the people who do not fall under the conventional category like freelancers and part-time workers. Immediate reactions like these towards the escalating unemployment insurance claims benefited many but also increased the unemployment insurance fraud rates. 

Unemployment insurance was jam-packed in the course of the pandemic and this phase of uncertainty was exploited by fraudsters to illegally obtain sensitive data, resources, and claims. According to a CNBC report, by early November 2020, at least 36 billion dollars of the 360 billion dollars in CARES Act unemployment benefits was lost to improper and fraud payments, according to a conservative estimate from the Office of the Inspector General for the Department of Labor.

A notification by the University of Arizona says, “The University, Arizona, and states across the nation have seen a significant surge in unemployment benefits fraud, largely in association with identity theft. Although there has not been a breach of information stored by AZ Department of Economic Security (DES) or the University, fraudsters are using phishing scams, previous corporate data breaches, and other tactics to collect information from individuals across the country.” 

Identity thefts dominate the fraud ring wherein fraudsters disguise as applicants and claimants, and use the stolen identity to gain benefits. The surging unemployment insurance claims during the pandemic demanded large resources and staff, the lack of which enabled bad actors to slyly commit frauds. The same person using different stolen identities can increase the fraud rate and loss that is not easily retrievable. 

As the Arizona University implies, phishing scams are also playing a great role in the data breaches revolving around the unemployment insurance systems. Fake companies and websites created by these bad actors can act as bait to trap government agencies and steal valuable resources. 

Can Analytics be a Rescue Measure? 

Since data plays a pivotal role in these unemployment insurance frameworks, advanced analytics can aid the prevention of fraud. Predictive analytics can be leveraged to detect threats and flag suspicious claims so that these applications go through stronger scrutiny. Many companies are providing various AI and analytics-based services for identity proofing and multi-factor authentication to minimize fraud rates. Elder Research has developed a flexible and robust predictive analytics tool that enables systems to dig deeper into the data to expose anomalies in the unemployment insurance payments. The tool uses three machine learning algorithms that were trained using the unemployment insurance data of the state and the insights were leveraged for the predictions. 

Using analytics to cross-match data from different sources can uncover fraudulent claims and detect inconsistencies. There are various systems like the Integrity Data Hub that offer this service expanding it to a multi-state approach. Automated databases and operations can enhance the possibility of detecting frauds and validating independent contractor claims. Apart from these immediate measures consistent monitoring of data and fraud cases can help reduce the probability of threats in the future. Various big data analytics tools will study the history and patterns of employer wage filings to understand the process and detect any unusual changes that might be a hint towards fraud.