Preventing Crash for Cash with Analytics

At #AnalyticsX, the SAS Analytics Experience event in Milan, I was fortunate enough to have the opportunity to learn about fraud prevention in the insurance industry. SAS has nearly 40-year history of working in analytics and insurance and I caught up with Ben Fletcher, Director at the Insurance Fraud Bureau (IFB), to understand better how the non-profit is using data, analytics and SAS technologies to combat insurance fraud at all levels – from mischievous fraud to highly organized fraud on a large scale, including organized ‘crash for cash’.

The IFB is a not-for-profit company established in 2006 to lead the insurance industry’s collective fight against fraud. The IFB act as a central hub for sharing insurance fraud data and intelligence, with the objective of detecting and disrupting organized fraud networks. While the fraud may often appear to be a simple concept – e.g. causing deliberate crashes to make claims and pocket the pay-out – these scams often involve many people in highly organized gangs, to help make the claim seem genuine or not obviously fraudulent. Not only that but they might be doing this on a large scale making them big contributors to the overall level of fraud.

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Whether we like it or not, we are all impacted by insurance fraud. It raises the insurance premiums that we all have to pay. The gangs committing much of this fraud are likely to be involved in other criminal activity, such as drug dealing or human trafficking. So, aside from higher insurance premiums, this is an issue which impacts UK society in other ways and represents an illustration of how data can be used for social good.

Through the use of data, it’s possible to understand the scale of organized crime and to communicate that message to the rest of the industry.

The first step is understanding the scale of the issue, so it can be tackled; but until the industry can see and recognize the issue properly, then there isn’t a clear understanding of the need to tackle it. Further, the audience for the system is complex, with various bodies looking at the data through different lenses, such as lawmakers, insurance specialists and the police. When people are making fraudulent claims, the fraudster makes the claim appear genuine. The challenge is you want to identify these cases but you don’t want to wrongly question what turns out to be a genuine claim – otherwise referred to as a ‘false positive’.

The SAS platform was built to help promote collaboration between insurance companies, which are essentially struggling with the same issue; how to prevent fraud from occurring. Insurance is a risk-averse industry and this was an incredibly bold step for the industry to take. Companies would not want to share data directly with other insurance companies for competitive reasons but are more likely to do so if it’s through a third party industry body (in this case the IFB).

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Given the criminal activity involved, how could the insurance industry recognize, understand and tackle the issue? 

Using the recently-launched SAS digital platform allows the industry to better understand and recognise when fraud is taking place by drawing on lots of data points to highlight common themes and anomalies. Until this point, there was aggregated data, but it was not enough to meet the challenges faced in the industry. To find the trends and patterns, the system needed to cater for the whole process; from granular data right through to summarized data, analytical models with advanced statistical analyses and data visualization.

There was a lot of historical data and risk analysis data involved previously, and the IFB needed a system that could cope with the cross-platform intricacies of granular data, as well as offer opportunities for further growth.

Each contributing organization had to ensure that their data was clean. To be accepted into the system, the data had to meet stringent guidelines and standards before it could be used. Data preparation was a key part of the process.

All in all, this sounds like a fearsome project and anyone who has tried to work with data will be able to envisage the potential issues. Within companies, there is often a great deal of data that is siloed in verticals, e.g. the marketing data versus the sales data. Getting a single company to think about their data in a horizontal fashion is incredibly challenging and organizations struggle with it. The challenge facing the IFB was exponentially larger because of the number of insurance companies involved. There are many complexities involved in ensuring that data is good enough and clean enough to be used within a single organization. Here, it’s multiplied because each organization’s conception of clean data will vary, and perhaps differ between departments of the same organization.

What has helped the project get to this point? Fletcher noted that a key part was the business expertise of the SAS team, who had a lot of experience in the insurance and fraud industry. He also noted that the SAS team understood the value of instilling robust practices and processes to support the business objectives. There was a strong conviction that the business users need to be put front and centre of the system in order to ensure that the correct insights and conclusions are drawn from the data. Throughout the project, there was an emphasis on obtaining good user requirements with an emphasis on the business practicalities that emerged from discussion.

The overall solution needed to be comprehensive and complete. Ultimately, the IFB wanted a business partner to work with them on a partnership that had longevity, and they wanted a partner that would be invested in their success as an organization.

Finding Simplicity in Complexity

As Daniel Goleman commented in his book Emotional Intelligence, when we go through high stress situations, we eventually stop thinking clearly. We are unable to prioritize, and the stress means that we can start seeing things as much more complicated than they really are. When people are analysing data in complex circumstances, this, in itself, can cause stress. So things need to have a level of simplicity.

For the project, the audience comprised a wide variety of interested parties such as lawmakers, insurance specialists, fraud specialists and police. As with many domains, the gap between user and developer can be quite wide. In this case, Fletcher noted that the SAS team helped to shrink the gap, essentially being the glue to hold things together from a technical and a business standpoint.

In terms of the output, the teams say they love the visual analytics and the simplicity of the solution. It’s helping them to be more productive because they are working with meaningful data and can get to the heart of what they need to know.

What’s next?

Data can be used to improve the customer experience, and one area of interest is increasing automation throughout the whole insurance process. While that step is further away, it’s clear that this sort of initiative can only help the insurance industry become more analytical and insights-driven.

 

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