Operationalizing Explainable AI in Insurance Fraud Detection: From Model  Transparency to Decision Justification

Harender Bisht

Operationalizing Explainable AI in Insurance Fraud Detection: From Model Transparency to Decision Justification

Authors

Harender Bisht

Keywords:

Explainable Artificial Intelligence, Insurance Fraud Detection, Decision Justification, Human-AI Collaboration, Algorithmic Transparency

Abstract

Artificial intelligence and machine learning increasingly have their place in the insurance sector as a toolfor detecting fraud, but there is a big disparity between what an algorithm spits out and what datacombination fraud detectives, compliance officers, and regulators actually need. Model transparency is not sufficient in high-stakes situations

References

Amina Adadi and Mohammed Berrada, "Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)," IEEE Access, 2018. [Online]. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8466590

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Published

2026-01-31

How to Cite

Harender Bisht. (2026). Operationalizing Explainable AI in Insurance Fraud Detection: From Model Transparency to Decision Justification. Journal of Computational Analysis and Applications (JoCAAA), 35(1), 747–755. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/4791