Vacatures voor Schade Specialisten

Road testing: machine learning and the efficiency of fraud detection

Bron: Nefeli Pamballi, Phanis Ioannou and Yiannis Parizas in The Actuary - 02 juli 2021

Nefeli Pamballi, Phanis Ioannou and Yiannis Parizas outline how machine learning could help increase the efficiency of fraud detection in motor insurance

Fraudulent claims are a significant cost to personal motor insurance products, typically increasing the combined ratio of the insurer by 5%-10%. Traditionally, firms would use expert judgment algorithms to decide which claims would be investigated for fraud. We set up a machine learning pipeline to help optimise processes in the Fraud Management Unit (FMU), to reduce the cost of fraudulent claims. Using data science techniques, we focused on reducing costs and increasing the efficiency of fraud detection processes by concentrating fraud investigation efforts on claims that were more likely to be fraudulent. Organisations would benefit from:

  • A reduction in operating expenses, as claims with low probability of being fraudulent will be fast-tracked
  • A better customer experience from fast-tracked customers, leading to higher customer satisfaction and retention levels
  • An increased fraud detection rate that reduces the combined ratio.

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