MAPPING FINANCIAL CRIMES IN THE INSURANCE INDUSTRY: A BIBLIOMETRIC ANALYSIS OF MONEY LAUNDERING AND FRAUD LITERATURE

Özet

Money laundering (ML) remains a persistent threat to the integrity and transparency of the global financial system, increasingly exploiting sectors beyond traditional banking. Among these, the insurance industry represents a particularly vulnerable yet underexplored domain due to its complex product structures, reliance on intermediaries, and comparatively fragmented compliance infrastructure. This study provides a comprehensive bibliometric assessment of financial crime research in the insurance industry, with particular emphasis on antimoney laundering (AML) and insurance fraud studies. Rather than treating these concepts as identical phenomena, the study examines how they coexist within the broader financial crime literature and how their thematic boundaries have evolved over time. Bibliographic data were retrieved from the Web of Science Core Collection using a Boolean search strategy combining “insurance” with “anti-money laundering” or “fraud.” Following a multi-stage screening process (2005–2025, English-language, peer-reviewed publications, and relevance-based manual validation), a final dataset of 121 documents was analyzed using Bibliometrix (R) and VOSviewer. Findings reveal that research output has grown unevenly over time, often responding to regulatory shifts and digital transformation rather than cumulative theoretical development. The literature is characterized by fragmentation across journals and institutions, with notable geographic concentration in high-income countries and limited international collaboration. Co-word and thematic analyses identify five major clusters: algorithmic fraud detection, behavioral ethics, regulatory compliance, sector-specific applications (e.g., motor and health insurance), and jurisdictional policy contexts. The results further highlight the increasing prominence of machine learning and explainable AI concepts, yet emphasize a persistent gap in integrating algorithmic tools with institutional and ethical governance frameworks. Overall, this study contributes a structured overview of financial crime research in the insurance industry, particularly focusing on money laundering and fraud.

Abstract

Money laundering (ML) remains a persistent threat to the integrity and transparency of the global financial system, increasingly exploiting sectors beyond traditional banking. Among these, the insurance industry represents a particularly vulnerable yet underexplored domain due to its complex product structures, reliance on intermediaries, and comparatively fragmented compliance infrastructure. This study provides a comprehensive bibliometric assessment of financial crime research in the insurance industry, with particular emphasis on antimoney laundering (AML) and insurance fraud studies. Rather than treating these concepts as identical phenomena, the study examines how they coexist within the broader financial crime literature and how their thematic boundaries have evolved over time. Bibliographic data were retrieved from the Web of Science Core Collection using a Boolean search strategy combining “insurance” with “anti-money laundering” or “fraud.” Following a multi-stage screening process (2005–2025, English-language, peer-reviewed publications, and relevance-based manual validation), a final dataset of 121 documents was analyzed using Bibliometrix (R) and VOSviewer. Findings reveal that research output has grown unevenly over time, often responding to regulatory shifts and digital transformation rather than cumulative theoretical development. The literature is characterized by fragmentation across journals and institutions, with notable geographic concentration in high-income countries and limited international collaboration. Co-word and thematic analyses identify five major clusters: algorithmic fraud detection, behavioral ethics, regulatory compliance, sector-specific applications (e.g., motor and health insurance), and jurisdictional policy contexts. The results further highlight the increasing prominence of machine learning and explainable AI concepts, yet emphasize a persistent gap in integrating algorithmic tools with institutional and ethical governance frameworks. Overall, this study contributes a structured overview of financial crime research in the insurance industry, particularly focusing on money laundering and fraud.

Yazarlar

Eyyüp Ensari ŞAHİN, Ceyda AKTAN, Melikşah AYDIN

Anahtar Kelimeler

Insurane, mapping financial crimes, money aundering and fraud.

JEL Codes

G22, E44

Yayın Bilgileri

Cilt 6, Sayı 1, 2026 · Sayfa 69-98

DOI: 10.52898/ijif.2026.5

Dosyalar