Other parts of this series:
In previous posts in this insurance analytics primer, we have discussed the basic concepts and theories and provided some examples of analytics in action. In this final post of the series, we would like to provide some additional examples of how insurers are using analytics to improve their top- and bottom-lines.
Several insurance companies are currently using advanced analytics to reduce claims fraud. When a claim is reported, the analytics software compares the claimant and the circumstances of the claim against the company’s own data on other fraudulent claims, and combines it with external information such as the claimants criminal record, credit score, social media posts and other data. This information is used to determine the likelihood of the claim being fraudulent.
Claims that result in a moderate score may receive special attention and require the individual to file a police report, provide receipts and appraisals and supply other additional information. Claims that score higher may be turned over to a fraud specialist immediately, who will then oversee a much more rigorous claims process.
With every percentage reduction in claims fraud resulting in millions of dollars in profit, insurance companies are able to see a very high return on their analytics investment.
Hiring happy, productive workers has always been more of an art than a science. When a seemingly qualified candidate doesn’t work out or leaves a company following a significant investment in training and mentorship, management often can’t understand what went wrong.
At least one insurer has turned to analytics to help them better understand what makes an employee successful, motivated and happy in a particular position and what traits often lead to failure.
The data can also be used to steer a qualified candidate away from the position they applied for towards one that appears to align better with their characteristics. As they progress in their career, the data can also be used to identify those who would excel in a management position or those who would do better in a technical track.
On an ongoing basis, the data can also be used to identify employees who are at risk of leaving the company, so that their career coach can offer incentives such as additional training, bonuses and other perks, before they become disgruntled with the organization.
These are just a few examples of what is being done in the marketplace. There are dozens of ways that insurers can leverage the awesome power of analytics across their organization to improve the company’s top- and bottom line.