Fraud Risk Management in Life Insurance: Challenges and Control Strategies in the Digital Age

Authors

  • Daimah Politeknik Siber Cerdika Internasional
  • Siti Ainul Kholipah Politeknik Siber Cerdika Internasional

DOI:

https://doi.org/10.59141/jiss.v7i8.2460

Keywords:

risk management, life insurance fraud, digitalization, internal control, digital fraud

Abstract

The development of digital technology has driven significant transformations in the life insurance industry, especially in the process of marketing, policy administration, and claims management. Digitalization provides operational efficiency and ease of service, but at the same time increases the complexity of the risk of life insurance fraud. Fraud is no longer limited to conventional practices, but is thriving in the form of digital data manipulation, identity forgery, and collusion that exploits the weaknesses of technological systems. This condition makes fraud risk management a strategic issue that needs to be studied in depth, especially in the context of life insurance in the digital era. This research aims to analyze the challenges of fraud in life insurance and identify risk control strategies implemented in the digital era. This study used a qualitative approach with a case study design. Data was collected through in-depth interviews with management, questionnaires open to licensed employees, and observations of operational processes and internal control systems. The results of the discussion show that digitalization improves service efficiency, but also widens the gap in fraud risk if it is not balanced with adequate control. The main challenges identified include limited human resource competencies, the lack of optimal use of data-based fraud detection technology, and weak internal control culture. The findings of the study also show that there is a gap in perception between management and operational employees regarding the level of fraud risk. The conclusion of this study confirms that life insurance fraud risk management in the digital era requires an integrated approach between technology, governance, and human resource development. Strengthening the internal control system and implementing digital-based preventive strategies are key to mitigating fraud risks in a sustainable manner.

References

Akomea-Frimpong, I., Andoh, C., & Ofosu-Hene, E. D. (2016). Causes, effects and deterrence of insurance fraud: evidence from Ghana. Journal of Financial Crime, 23(4), 678–699.

Aslam, F., Hunjra, A. I., Ftiti, Z., Louhichi, W., & Shams, T. (2022). Insurance fraud detection: Evidence from artificial intelligence and machine learning. Research in International Business and Finance, 62, 101744. https://doi.org/10.1016/j.ribaf.2022.101744

Eling, M., & Lehmann, M. (2018). The impact of digitalization on the insurance value chain and the insurability of risks. The Geneva Papers on Risk and Insurance—Issues and Practice, 43(3), 359–396. https://doi.org/10.1057/s41288-017-0073-0

Farrington, C., & Whitfield, D. (2014). Detecting insurance fraud. Journal of Financial Crime, 21(4), 496–510. https://doi.org/10.1108/JFC-10-2013-0076

Major, J. A., & Riedinger, D. R. (2008). Temporal aspects of fraud detection. Journal of Financial Crime, 15(1), 7–17.

Morley, N. J., Ball, L. J., & Ormerod, T. C. (2006). How the detection of insurance fraud succeeds and fails. Psychology, Crime & Law, 12(2), 163–180. https://doi.org/10.1080/10683160512331316325

Paula, F., Salah, A. A., & van der Aalst, W. M. P. (2021). A survey on machine learning for insurance fraud detection. Archives of Computational Methods in Engineering, 28(6), 4331–4357.

Schrijver, G., et al. (2024). Automobile insurance fraud detection using data mining: A systematic literature review. Intelligent Systems with Applications.

Subudhi, A. N., & Panigrahi, S. (2022). A systematic review on insurance fraud detection. Journal of Financial Crime, 29(4), 1184–1212.

Artís, M., Ayuso, M., & Guillén, M. (2002). An econometric model of insurance fraud detection. The Geneva Papers on Risk and Insurance—Issues and Practice, 27(1), 94–108.

du Preez, A., et al. (2024). Fraud detection in healthcare claims using machine learning. Artificial Intelligence in Medicine.

Li, J., Huang, K.-Y., Jin, J., & Shi, J. (2007). A survey on statistical methods for health care fraud detection. Health Care Management Science. jhjin.engin.umich.edu

Nabrawi, E., et al. (2023). Fraud detection in healthcare insurance claims using (machine learning approaches). Risks, 11(9). MDPI

Idrus, M. (2023). Fraud in life insurance companies in Indonesia. Amkop Management Accounting Review (AMAR), 3(1), 24–29. https://doi.org/10.37531/amar.v3i1.521

Puspasari, N. (2015). Fraud theory evolution and its relevance to fraud prevention in the village government in Indonesia. Asia Pacific Fraud Journal, 1(2), 177. https://doi.org/10.21532/apfj.001.16.01.02.15

Downloads

Published

2026-08-10

How to Cite

Daimah, & Kholipah, S. A. (2026). Fraud Risk Management in Life Insurance: Challenges and Control Strategies in the Digital Age. Jurnal Indonesia Sosial Sains, 7(8), 3452–3468. https://doi.org/10.59141/jiss.v7i8.2460