Insurers are embracing digital transformation programmes to keep customers at the centre of their business.
However, insurance fraud and related crimes have become a growing concern in today’s connected landscape.
This is where fraud analytics has emerged as the first line of defence to safeguard data and systems while enabling business leaders to make real-time decisions on claims recovery to reduce leakage.
Given the rapid need to modernise and digitalise legacy insurance environments, insurance companies need to find innovative ways of retaining and growing their customer base.
This sees insurers looking to capture the values achieved through the traditional insurance agent but without the operational cost.
Investing in websites, operational systems and processes, and people are making this happen at a scale previously unimaginable.
Unfortunately, the risk that comes from pushing the resultant self-service functionality for new business and claims processing means the company removes experienced staff from vital interactions.
“Not only does this mean that clients loose personal engagement where there once was, but also the potential for fraud increases as criminal elements invest time and resources in learning how to exploit any weak links in the fraud defences,” says Amit Kumar, Practice Leader, Fraud, AML & Security Intelligence, for SAS in Middle East & Africa.
Premeditative fraudsters can easily make claims or introduce new business by using personal information from non-existent clients or from those who have had their details significantly changed, such as through synthetic identity theft.
This can be done quicky, from any location using any device. Many digital programs do not look at the full spectrum of clients.
Many claim managers admit that the claims incidence for a book of business has increased in a statistically significant way after implementing a digital program.
“This is where fraud analytics helps steer the customer journey,” says Kumar. “Real-time analytics determines which claimants should go through the STP channel versus those that should not. Analytics reveals which people should be automatically accepted as new business – versus those who need to be connected with an experienced insurance person for a deeper conversation, for example.”
Using this analytics, each new business application can be automatically evaluated to see if it fits into a known, high-scoring fraud ring.
If so, it is passed on for further action. Insurers see emerging threats and trends based on existing data that helps them get ahead of the next fraud wave.
Additionally, this same technology can extend beyond fraud to other areas. For example, analytics makes real-time decisions on claims recovery – significantly reducing leakage.
“Analytical solutions that incorporate elements such as entity resolution, an open data model, data importing, and the ability to orchestrate all analytic activities with choice and control, provide a strong foundation to give the insurer a holistic view of risk. Furthermore, it helps to authenticate a customer’s identity and automatically tunes models through an adaptive learning feedback loop,” concludes Kumar.
