Mar 12, 2020
In monetary terms, we can get close to grasping the reality. Fraud with a value of around £2.6 billion is either attempted or successfully perpetrated against UK insurers every year, according to the Association of British Insurers. In the US, meanwhile, it’s estimated 10 per cent of all claims have some element of fraud, costing the industry around $80 billion a year, says the Coalition Against Insurance Fraud.
Of course, these are estimates. No one really knows the true cost, nor can anyone claim to understand the true nature of the threat. Yet. What the industry does know is that it is targeted by both organised and opportunistic fraudsters. It also knows what types of fraud these criminal organisations and individuals perpetrate.
So why, despite the excellent work done by the likes of the Coalition Against Insurance Fraud and the Insurance Fraud Bureau in the UK, is a comprehensive view of the threat still lacking? And what can be done to change that?
The answer could lie in a tool that most insurers are already using. Data analytics is known to be beneficial in understanding customer behaviour. But its role in looking for and understanding patterns of criminal activity is less widely known.
Many insurers do use advanced analytics to identify and stop fraud; but the way it is used is often siloed within a particular department or product line. This is effective with individual and opportunistic cases of fraud, but it fails to provide a broader, more detailed view of the threat insurers face, particularly from organised crime.
It’s worth noting that data scientist have a key role in this activity – and that third party services can and do help them realise the potential of data science for insurers.
Building a comprehensive view of the threat
Internal silos within companies must be broken down to enable insurers to identify and track fraudulent behaviour across their business. If insurers then feed the resulting data to the central anti-fraud organisations, the sector can start to build a comprehensive view of the nature and scale of the threat.
Only then can the industry truly future-proof its defences. Once it is clearly understood who is perpetrating the fraud, how they are doing so, who they are collaborating with and where their favoured weak spots are, the industry can start applying predictive analytics on a broader scale.
The industry will then be armed with the necessary overview to understand where criminals are most likely to strike next.
To date the industry has been lagging behind the latest criminal tactics, but with analytics, collaboration and, most importantly, the determination to see this through, insurers should be able to turn the tables on the fraudsters and drive that annual fraud bill down from the billions to the millions.
(Editor's Note: The article was written by Dennis Toomey; original article here. Reposted with permission.)
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