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Notas: Sumario: The document explains that UK motor insurance is heavily affected by fraudulent claims and outlines actuarial and data-science methods to combat them. It distinguishes opportunistic and organized fraud and highlights tools such as automation, metadata extraction, machine-learning models, and especially graph-based techniques for detecting links between fraudulent entities. It also notes challenges related to data quality and entity resolution, emphasizing the usefulness of these approaches for early fraud detection in motor insuranceRegistros relacionados: En: The Actuary : the magazine of the Institute & Faculty of Actuaries. - London : Redactive Publishing, 2019-. - 02/02/2026 Number 7 - February 2026 , p. 54 - 57Materia / lugar / evento: Seguro de automóviles Fraude en el seguro Análisis de datos Machine learning Teoría de grafos Inteligencia artificial Mercado de seguros Otros autores: Micklethwait, Tessa
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