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Smart Analytics, Insurance Fraud and Customer Experience

A small car drives into a parked bus, causing less than 100 Euros worth of damage.  The 46 passengers, who were going to a nightclub, were unaware of the collision and had a great night of dancing. The next day they all made a claim for whiplash injuries totalling over 300,000 Euros. This is a recent story from our customer Aviva, who decided to challenge this injury claim in the courts. They won.

While this case is from the UK, bogus insurance trends are reported in all countries. For example, at our recent customer conference I saw a dash cam video of a man in South Korea deliberately jumping onto a customer’s car and bashing his head repeatedly against the windscreen to make a claim. Other infamous bogus claims trends included claims for car fires in Sweden which proved to be a deliberate criminal scam.

For European insurers, it is estimated that 10% of claims costs are fraudulent. Some countries like Germany have estimated the annual cost is 4 billion Euros. Given how some scams involve staging serious accidents involving innocent insurance customers, there also are human costs in terms of injuries or worse.

The insurance industry has not been complacent about this problem and there has been considerable investment in national and trans-national anti-fraud agencies, campaigns and initiatives. That said, while some scams are so outrageous and as hard-to-miss as the bus claim, most fraudulent claims have tended to be difficult to detect because they are tricky to disprove or are literally lost in the data.

This is where the insurer as an analytics business can come to the fore. Analytics that might be used to make smarter decisions about customer service can also be deployed to detect hidden trends of fraudulent behaviour. This might be cross-referencing claims history and even accessing shared data from other insurers about unusual claim activity.

Conversely, the use of analytics to detect fraud will be hindered unless data consolidation has been completed. The ability to obtain a 360 degree view of your customer requires that you can access and analyse all the data in one place. Data consolidation projects may sound dull but without them insurers will find that using analytics for customer care and anti-fraud checking and investigations is essentially flawed.

In developing a claims strategy is important that it should be focused on the customer. Insurance fraud makes victims of the vast majority of honest customers who end up paying more in premiums to cover false claim pay-outs. This customer first focus must go much further in how fraud is investigated, with real-time analytics being used so normal business isn’t disrupted and anti-fraud background checks happen without needing to involve an innocent customer at all.

As insurers fight back against the fraudsters, analytics technology that can do some of heavy work in detection is a valuable part of their arsenal. This includes ensuring customer are carried with the insurer in their fight against fraud. Smart analytics can play a valuable role in ensuring fraudsters are caught and avoid any mishaps that ensnare law-biding customers. Technology can complement other strategies around continued effort to educate customers, train staff and take on the fraudsters in the law courts. As Aviva did.



Comments: (1)

Ketharaman Swaminathan
Ketharaman Swaminathan - GTM360 Marketing Solutions - Pune 12 February, 2016, 12:18Be the first to give this comment the thumbs up 0 likes

Brilliant post. Kudos for upfront describing the business problem(s) that analytics technology can solve. I'm sick and tired of hearing fintech providers going on and on about how some spanking new technology will kill banks / insurers and, when asked for what business problems their product or service can solve, listening to some inane examples like how their "disruptive" PFM can save $3 on coffee or $5 on beer. Against that backdrop, your post is a breath of fresh air.

From my interactions with a few general insurers who provide household insurance products, I understand that many people who lose N items in a (say) flood are filing fraudulent claims for >>N items. I'm keen to know your thoughts - maybe in a follow-on post - on how analytics can help insurers unearth such frauds.