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The fraudulent claims for Unemployment Insurance (UI) have also risen massively in the United States especially during the onset of COVID-19 pandemic with billions of dollars that were lost. These approaches applied formerly in fraud detection and prevention have been challenged by new and advanced fraud systems. For this reason, AI, Big Data and Predictive Analytics are now crucial for improving fraud mitigation in UI programs. The aim of this research is to understand how far AI, Big Data and Predictive Analytics have been utilized, for how effective they are and the barriers they pose in tackling unemployment insurance fraud in the United States. An online quantitative self-administered questionnaire was administered where 200 participants from the unemployment insurance agencies of the U.S, covering fraud investigators, IT personnel, data analysts and policymakers, were included in the study. Data were analyzed to establish the correlation between technology adoption to application of anti-fraud measures using descriptive statistics, chi-square test and regression analysis. The survey shows that about 42.5% of agencies actively use AI and 34% of them use Big Data very often. However, difficulties like high costs, integration troubles and limited availability of specialized staff are still issues that make adoption a problem. The results of using AI and Big Data were moderate and optimistic but rather elevated, as seen through regression analysis; on the other hand, predictive analytics demonstrated large potential when applied successfully. The study’s results indicate that AI, Big Data and Predictive Analytics present enormous potential for mitigating UI fraud, although broad integration will not be possible without overcoming essential infrastructural and resource limitations.

The fraudulent claims for Unemployment Insurance (UI) have also risen massively in the United States especially during the onset of COVID-19 pandemic with billions of dollars that were lost. These approaches applied formerly in fraud detection and prevention have been challenged by new and advanced fraud systems. For this reason, AI, Big Data and Predictive Analytics are now crucial for improving fraud mitigation in UI programs. The aim of this research is to understand how far AI, Big Data and Predictive Analytics have been utilized, for how effective they are and the barriers they pose in tackling unemployment insurance fraud in the United States. An online quantitative self-administered questionnaire was administered where 200 participants from the unemployment insurance agencies of the U.S, covering fraud investigators, IT personnel, data analysts and policymakers, were included in the study. Data were analyzed to establish the correlation between technology adoption to application of anti-fraud measures using descriptive statistics, chi-square test and regression analysis. The survey shows that about 42.5% of agencies actively use AI and 34% of them use Big Data very often. However, difficulties like high costs, integration troubles and limited availability of specialized staff are still issues that make adoption a problem. The results of using AI and Big Data were moderate and optimistic but rather elevated, as seen through regression analysis; on the other hand, predictive analytics demonstrated large potential when applied successfully. The study’s results indicate that AI, Big Data and Predictive Analytics present enormous potential for mitigating UI fraud, although broad integration will not be possible without overcoming essential infrastructural and resource limitations.