PROBLEM

Customers expect more from their interaction with their insurance provider and are becoming more difficult to satisfy. They expect that the insurance provider delivers on their promises made during the sales process. To support enhanced decision-making and timely interactions, insurance, banking and medical aid providers need a 360-degree view of their customer risk profile. However, the reality is that many insurance providers rely on a variety of disparate risk processes. The widespread modernization and major regulatory changes are also forcing insurers to take a long, hard look at the traditional ways of mitigating risk. Digital innovation is having a profound influence on the insurance sector, challenging providers to evolve their risk strategies to remain competitive.

Responding to this evolution means delivering cost-effective yet rich tools, systems and processes accessible from a familiar interface.

SOLUTION

XTND are the proud owners of VERITAS. A web-based analytical tool identifying high risk insurance and medical aid claims based on historical and existing claims experience relating to fraud, waste and abuse. It is important for any institution to adapt faster than the fraudsters by employing cutting-edge tools and making fraud prevention an integral part of every point in the transaction chain.

VERITAS is a live system that refreshes and updates the fraud models as 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 personalized propensity for fraud risk score for each claim.

Traditional rule-based approaches to fraud detection typically used in the insurance, banking and medical aid 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, delays in paying valid claims and potential damaging relationships with clients. Our machine learning fraud models aims 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 us to quickly assess which claims are extremely unlikely to be fraud, allowing these claims to be paid as quickly as possible and without any further client inconvenience.

XTND follows and end-to-end assessment process during the validation of any claim. Our focus is to identify and prove fraudulent activities during the new business process and subsequent claims process. Our assessment methodology supports a court driven investigation. Our assessors are qualified Certified Fraud Examiners and adhere to the highest ethical standards.

We believe in partnerships for life and together we will enhance and optimize your current fraud prevention and detection process on all your products to achieve better results in less time, with minimal effort, at a lower cost and within the prescribed Policy Protection Rules and prescribed legislation.