ValueMomentum

UK insurers detected £1.16 billion in fraudulent general insurance claims in 2024. As fraud schemes become more sophisticated, traditional rules-based detection may no longer be enough. Discover five best practices for using AI to identify suspicious claims earlier, reduce false positives and improve investigative efficiency.

Is your fraud detection approach finding suspicious claims early enough? Learn how UK claims organisations can combine AI, external data and intelligent triage to detect emerging fraud patterns before handling costs and settlement pressures build.

Better fraud detection should not mean delaying legitimate claims. Explore how an integrated, AI-driven approach can help claims organisations reduce fraud leakage, improve SIU productivity and accelerate low-risk claims while supporting transparent decision-making.

What if claims teams could identify more subrogation opportunities in real time, without adding unnecessary manual reviews? This case study shows how one insurer used predictive modelling to improve recovery outcomes and adjuster efficiency.

Claims teams cannot afford to miss recovery opportunities. This case study explores how predictive modelling helped a Fortune 500 insurer improve subrogation identification, reduce leakage and create a more efficient referral process.

A Fortune 500 insurer wanted to reduce claims leakage and improve the accuracy of subrogation referrals. By integrating a machine learning model with its claims platform, the carrier automated nearly 20% of referrals and reduced recovery cycle time by 9%.