When an insurance company wanted more discipline in underwriting
We used predictive models to maximize the value of their data
Insurance Industry Case Study
Risk and Regulatory Analytics: Actuarial Risk Analytics
For insurance companies, few innovations are more important than predictive modeling, especially when it comes to underwriting and pricing. So when a major U.S. insurance carrier wanted to improve its underwriting and pricing discipline, it looked for a professional services provider that could not only develop algorithmic and predictive modeling capabilities but also deliver, integrate, and deploy an end-to-end business solution across a range of product lines.
How we helped
Deloitte first worked with the client to develop and implement predictive modeling solutions to improve its underwriting and pricing discipline as it entered a soft underwriting and pricing market cycle. We helped the company create and deploy solutions for its Errors and Omissions and its Workers’ Compensation lines of business. What we deployed were predictive models, which are actuarial and statistically derived multivariate formulas that relate predictive underwriting variables to predicted future policy profitability.
Building on the success of that initial project, Deloitte was brought back to develop predictive underwriting models and scoring engines for the client’s Business Owner’s Policy, Commercial Automobile and Commercial Package (General Liability and Commercial Property) lines. The scoring engine that was developed was a combination of IT infrastructure and software that generates the predicted profitability score and lets the company monitor the effectiveness of business strategies derived from the models.
Once the predictive modeling solution was fully integrated into the insurer’s technical and business infrastructure, Deloitte assisted in the business implementation of the models and the development and delivery of training content for regional underwriting offices.
Today, the client can effectively assess policies for risk quality, price adequacy, customer retention, agency management, and underwriting decision compliance. It can also flag policies for follow-up attention in areas such as claims handling, agent training, and customer service.
The insurance carrier is able to measure the benefits of its predictive models and implemented methods, learning from past data and responding proactively to future needs.