The role of AI, big data and predictive analytics in mitigating unemployment insurance fraud

Abstract

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.

Keywords: {'name': 'Artificial Intelligence, Big Data, Predictive Analytics, Unemployment Insurance Fraud, Fraud Detection, U.S. Public Sector'}

Authors & affiliations

Siddikur Rahman
Affiliation not supplied

Abu Sayem
Affiliation not supplied

Shariar Emon Alve
Affiliation not supplied

Md Shahidul Islam
Affiliation not supplied

Muhammad Mahmudul Islam
Affiliation not supplied

Arifa Ahmed
Affiliation not supplied

Mohammed Kamruzzaman
Affiliation not supplied

Deposited references

Supplied references, not a list of publications citing this article.

Aiman, S. (2024). Analyzing surveillance videos in real-time using AI-powered deep learning techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 12(2), 950–960. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/11158

DOI: 10.58496/MJBD/2024/001

Azeema, N., Nawaz, H., Gill, M. A., Khan, M. A., Miraj, J., & Lodhi, K. (2023). Impact of artificial intelligence on financial markets: Possibilities & challenges. Journal of Computing & Biomedical Informatics, 6(1), 287–299.

DOI: 10.59890/ijasr.v2i1.1295

DOI: 10.1016/j.giq.2021.101595

Bovino, D., & Sattiraju, N. (2021). Multi-state unemployment fraud schemes exploited during the COVID-19 pandemic. National Law Review. Retrieved from https://www.natlawreview.com/

DOI: 10.1111/puar.13350

Chen, X., Wang, Y., & Zhang, X. (2021). Big data and machine learning for fraud detection: A comprehensive survey. Journal of Data Science and Analytics, 14(3), 123–138. https://doi.org/10.1007/s41060-021-00237-2

DOI: 10.1016/j.ijinfomgt.2021.102389

Davenport, T. H., & Bean, R. (2021). The AI advantage in public policy enforcement: How governments can fight fraud more effectively. Harvard Business Review, 99(5), 114–124. Retrieved from https://hbr.org/

Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116. Retrieved from https://hbr.org/

DOI: 10.1111/puar.13315

DOI: 10.1016/j.ijinfomgt.2018.12.011

Hansen, B. (2021). International fraud ring stole over $600 million from Washington State’s unemployment system. The Seattle Times. Retrieved from https://www.seattletimes.com/

Nawaz, H., Sethi, M. S., Nazir, S. S., & Jamil, U. (2024). Enhancing national cybersecurity and operational efficiency through legacy IT modernization and cloud migration: A U.S. perspective. Journal of Computing & Biomedical Informatics, 7(2).

DOI: 10.30574/wjaets.2024.11.2.0101

Kshetri, N. (2021). Big data’s role in combatting public sector fraud. Journal of Government Financial Management, 70(4), 25–34.

DOI: 10.1016/j.jbusres.2022.01.003

Office of the New York Attorney General. (2022). New York-based company charged with unemployment insurance fraud conspiracy. Retrieved from https://ag.ny.gov/

O'Leary, D. E. (2019). Artificial intelligence and big data: Overcoming implementation challenges in financial fraud detection. Journal of Business Analytics, 45(2), 99–114.

DOI: 10.1080/14719037.2019.1645875

Sayem, M. A., Taslima, N., Sidhu, G. S., Chowdhury, F., Sumi, S. M., Anwar, A. S., & Rowshon, M. (2023). AI-driven diagnostic tools: A survey of adoption and outcomes in global healthcare practices. International Journal of Recent Innovation Trends in Computing and Communication, 11(10), 1109–1122.

Sharma, R., & Liu, H. (2021). Predictive analytics in public sector fraud detection: A case study in the U.S. unemployment insurance program. Journal of Policy Analytics, 25(2), 188–203.

DOI: 10.1016/j.ijinfomgt.2020.102116

The California State Auditor. (2021). Employment Development Department: Significant weaknesses in EDD’s fraud prevention efforts led to billions in improper payments. Retrieved from https://www.auditor.ca.gov/

Thompson, M., & LaCasse, C. (2020). The role of artificial intelligence in mitigating unemployment fraud during economic crises. Journal of Economic Perspectives, 34(4), 85–102.

U.S. Department of Justice. (2022). Ongoing investigations into COVID-19 related fraud in unemployment insurance programs. Retrieved from https://www.justice.gov/

U.S. Department of Labor. (2022). Unemployment insurance fraud: A growing threat during the COVID-19 pandemic. Department of Labor Report. Retrieved from https://www.dol.gov/

U.S. Government Accountability Office (GAO). (2021). Unemployment insurance: Actions needed to strengthen fraud risk management in state systems. Retrieved from https://www.gao.gov/

DOI: 10.55927/ijabm.v3i2.9101

Ullah, A., Islam, K., Ali, A., & Baber, M. (2024). Assessing the impact of social media addiction on reading patterns: A study of Riphah International University students. International Journal of Human and Society, 4(1), 1250–1262. Retrieved from https://ijhs.com.pk/index.php/IJHS/article/view/513

Zhao, Y., & Yang, C. (2021). Big data analytics in public sector fraud detection: Evidence from unemployment insurance systems. Journal of Government Financial Management, 71(3), 52–67.

Smith, J., & Brown, R. (2020). AI-driven approaches to tax fraud prevention: Lessons from the UK. International Journal of Public Administration, 43(4), 340–355.