A new dawn for claims processing
The ever-changing insurance industry brings more and more dynamic insurance products into the market. This means that fraudsters, users and abusers become more sophisticated in their pursuit of enjoying the maximum financial benefits from the various products with the least detrimental effects. On the other hand, the insurance industry must comply with the highly regarded Twin Peaks model – the biggest structural reform in the financial services industry. Click here to read more about the challenges the insurance industry must consider during the validation process of claims.
Drastic changes tend to have a damaging effect on current business processes. The insurance industry is one of the most paper-intensive industries. Applications, proposals, underwriting, administration, claim forms, health insurance forms, disability forms…. even when a claim is settled, it often results in more paper. The combination of these ongoing changes and challenges necessitates insurers to more effectively structure their risk mitigation processes.
The optimal use of historical data has now become more important than ever before and has a vital role to play in the ever-changing insurance industry.
XTND is the proud owner of VERITAS. A web-based analytical tool identifying high risk claims based on historical and current claims experience relating to fraud. VERITAS is a live system that updates the fraud models real-time when new data is received from various assurers. VERITAS automatically filters claims using sophisticated machine learning predictive models and fraud detection business intelligence rules, resulting in a personalised risk score for each claim. In layman’s terms machine learning predictive models is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics and database systems to identify future patterns.
Traditional rules-based approaches to fraud detection typically used in the insurance and banking industries often result in many valid claims being flagged as suspicious, requiring unnecessary investigation. These false positive flags result in both unnecessary expenses being incurred, and delays in paying valid claims, potentially damaging insurers’ relationships with their clients. Our enhanced machine learning fraud models aim to more accurately identify fraudulent claims, greatly reducing the number of investigations performed without materially reducing the number of claims rejected due to fraud. The models also allow a quick assessment of which claims are extremely unlikely to be fraud, allowing these claims to be paid as quickly as possible and without any further client friction. An additional advantage is that the lack of fixed rules within machine learning models makes it difficult for fraudsters to design mechanisms to circumvent the fraud detection process.
The improved VERITAS model promises to give insurers the opportunity to create a paperless claims environment for low-risk clients and at the same time focus on a more effective and efficient fraud detection process. A paperless claims process on low risk-clients will result in a faster and more efficient process, delivering on the insurance industry’s promise to pay claims as quickly as possible.
In today’s business environment, survival requires insurance and banking industries to change and make use of new technologies, even if making the switch requires some re-engineering of current claims processes. For claims departments, implementing a paperless claims process on low-risk clients is not just a novelty, but a necessity.