Improving fraud detection rates through predictive analytics
Throughout the UK, insurance fraud is increasing. Research by Synectics Solutions published in the National SIRA fraud trends report this year showed that claims fraud in the UK increased by over 32% and policy fraud by just over 18% for the first half of 2017, compared to the same period in the previous year.
Moreover, the increasing level of identity and impersonation fraud has also increased exponentially, rising from 9 cases during the first half of 2016 to 6,059 cases for the same period of 2017, amongst members of the National SIRA syndicate. In fact a recent UK Government report from the Insurance Fraud Task Force published in 2016 put the cost of fraud from insurance at somewhere around £3 billion.
Furthermore the problem is far wider than the UK and is generally considered to be a global issue.
An Insurance Europe Report published in 2014 documented many of the fraud issues that insurers around Europe are facing that are costing them billions of Euros.
THE CHALLENGES INSURERS ARE FACING
The range of tactics used by those that are looking to perpetrate fraud to falsify claims and policies to reap the rewards of unauthorised funds is quite staggering. Whether dealing with false claims and identities, staged incidents, withholding of information, organised fraud rings, or ghost broking, insurers are being confronted with an unprecedented level of crime. This needs to be identified to prevent losses from this kind of activity destabilising their business and reducing their, already, slim profit margins.
Historically insurance companies have deployed a variety of data matching and rule based decision making solutions to help them identify, detect and address this problem. These solutions have been tremendously effective at arming these companies with the tools to defend themselves against this onslaught. And today the mainstay of many major underwriting and claims management fraud defences use rule based decision making very effectively to help them reduce their losses to fraud.
"...the increasing level of identity and impersonation fraud has also increased exponentially, rising from 9 cases during the first half of 2016 to 6,059 cases for the same period of 2017"
However, the volume of transactions that insurers are now having to deal with has created a number of factors that have contributed to those in this sector feeling that more needs to be done to help stay on top of this issue.
Overloaded investigation teams, high false positive rates, inability to effectively prioritise investigations - along with the need to speed up decision cycles to remain competitive, have all meant that insurers are now turning to predictive analysis models to help them augment and improve their fraud defences.
With limited resources for fraud investigation teams, hiring more staff to conduct manual investigations is an expensive and inefficient option outside the reach of most organisations.