Pet Insurance Fraud Detection
The UK pet insurance market continues to face rising claims costs and increasing fraud.
Thanks to Sherlock Detection, pet insurers can immediately and confidently make claim decisions informed by easily assimilated, evidence based intelligence rather than relying on traditional, unexplained black box rules. Genuine claims can be settled swiftly improving the customer journey.
Sherlock Detection can integrate any internal and external information source and analyse the results using innovative machine learning and AI-driven risk assessment capabilities.
Over 200 expert rules, which can be supplemented and tailored by the insurer, complete a claims risk evaluation in under three seconds. AI and proprietary machine learning techniques, fed by claims data as it is loaded onto the system, continually adapt and deliver a predictive accuracy far greater than manual analysis and traditional algorithms. Sherlock Detection equips the insurer with a fraud risk score for every claim supported by an intuitive report, written in simple business language, which evaluates the level of anomaly for each claim.
Innovative approach to fraud detection.
- Performance of a complete claims risk evaluation in just a few seconds thanks to more than 200 expert rules.
- Evaluation of the level of anomaly for each claim, identifying hidden connections between parties involved in past and present claims, and preventing and managing both opportunistic and organised claims fraud across multiple lines of business.
- Decrease in operational overheads and improved overall customer experience.
- Application of CRIF insurance fraud analytics engine and multiple techniques: business rules, AI and proprietary machine learning techniques, anomaly detection and network link analysis to automatically and selectively manage claims based on relevancy.
- Non-invasive and agile, Sherlock is a Software as a service (SaaS).
- Flexible and complementary, it is suitable for insurance companies with or without an existing anti-fraud platform in place.