What’s new in the fight against insurance fraud?
Here's how new data sources and technology are aiding in fraud detection today.
Insurance fraud has been around since, well, the beginning of insurance.
The ancient Greeks created a form of maritime insurance to indemnify against potential losses incurred with the sinking of a commercial ship in transit. It became a common scheme for the boat owner to hide the boat in a foreign port and collect the insurance money. Even in those early times, special investigators were hired to determine if the boat had indeed sunk. Fast-forward to the present, and for the last few decades, the industry has been using increasingly sophisticated technology to address fraud.
So, is it useful to ask what is really different now?
My answer is, yes. There are several transformational technologies that can change the game for fraud detection. For example, machine learning, social media, and aerial imagery can all contribute to new solutions. All of these generate and rely on massive amounts of data, including many new data sources. Whether we are talking about opportunistic fraud or organized crime rings — these technology areas provide terrific new opportunities to combat fraud.
Of course, fraud may occur during the underwriting or the claims process. When a person or business is applying for insurance, there is always the potential to supply incorrect information to get a lower rate purposely. On the claims side, fraud may occur at many points during the claims lifecycle. In the case of staged accidents, it is occurring even before the accident occurs.
So how is the advance of technology aiding in fraud detection today? First, let’s look at new data sources.
Rate evasion can be more easily spotted today due to the wide variety of new data sources that can provide checks on the information provided by a customer or agent. For example, for auto, it is easier to spot true garaging locations or identify if a vehicle has been in flood. For property, there is a wealth of data about the current characteristics of the property.
When it comes to machine learning — big data approaches with massive computing power and huge data sets can spot patterns and anomalies that it would be impossible for humans to spot — and do so with a lower rate of false positives. Social media has become a central tool for SIU and law enforcement — especially for workers’ comp fraud. We’ve all heard stories about individuals claiming disabling injuries that show up in Instagram pictures skiing or skydiving. The social media universe also yields a lot of information about connections between various individuals and businesses that can be mapped to identify fraud rings. Using aerial imagery, it becomes easier to compare before and after pictures of a property to determine if damage was caused by a particular weather event.
One of the biggest benefits of all this new capability is that technology allows fraud to be detected significantly earlier in a claim and with greater accuracy, so that Special Investigative Units (SIUs) and claims processes are more effective (compared to before when SIUs or management found out about a fraud 3-4 weeks or longer after FNOL, and then it was too late).
There is still much work to be done to find the right solution partners, integrate new solutions with existing systems, and determine the optimum balance of technology and human expertise. But there is now greater potential to finally make significant headway in reducing fraud, especially the potential for earlier identification and more accurate outcomes. That’s what’s new and encouraging in this ongoing battle!
Mark Breading (mmbreading@strategymeetsaction.com) is a partner at Strategy Meets Action (SMA), a strategic advisory firm working with traditional insurers and InsurTech solution providers to manage unprecedented industry change. These opinions are the author’s own.
This column first published at SMA’s blog and is republished here with consent from SMA.
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