It is widely accepted that the increased volume of NIHL claims is a result of the shift in focus of claims management companies and professionals who are looking to replace revenue lost from pursuing RTA whiplash claims following regulatory change The ABI and the industry are seeking regulatory change to improve this challenging situation.
But in the meantime, what can insurers do to detect fraudulent NHIL claims?
Consider link network analysis: although NIHL claims are associated with an opportunistic fraud phenomenon, link network analysis is useful to consider all parties involved in the claims process including any suspicious connections or networks.
|Deploy anomaly detection: given the high volume of NIHL claims, anomaly detection may be useful to analyse this big data to identify unusual cases, or suspicious behaviour hidden within the data that may seem to be homogeneous.
|Harness predictive analysis: use internally recorded data on both historic fraudulent and genuine NIHL claims to build models that provide the highest accuracy of fraud prediction via analysis and sophisticated algorithms.
Discover the top tips for insurers consulting data related to NIHL claims.