Using data analytics to tackle Insurance fraud

2011-07-01

This excerpt from Tom van der Spek’s blog describes the importance of an integrated approach to developing data analytics when tackling insurance fraud.

Most insurers collect enormous amounts of data, but have a poor understanding of the true extent of fraud and lack the ability to quickly and actively anticipate such cases. This is due to a limited focus of the organization’s data analysis and lack of commitment to improve their data quality. Tackling fraud therefore remains primarily reactive at the moment a customer reports the damage. If insurers can proactively determine which factors have a high probability through data analytics, fraud can be detected better and faster.

An effective approach to fraud asks for integral planning where organizational structure, business processes and applied data analytics meet. The combination of these 3 elements form the core of detection and prevention that discourages fraud without a decrease in service to clients. The steps to such an integral approach are:

• Building an analytic capability where insurers can continuously monitor and increase their insight in the enormous amounts of data
• Support the capability with the right tools and methods for substantial improvements
• Optimizing and keeping business processes up-to-date when all right tools and methods are obtained.

With an integrated process insurers can make a significant improvement in their approach to fraud. Particularly in areas of data analytics there is still a lot of room for improvement in order to come to a more proactive approach to tackling fraud. Therefore the key to successful fraud management is to keep learning and improving, while fulfilling one’s promises to clients and customers.

(Source: Tom van der Spek)