Article Synopsis :
Achieving optimal cost efficiency for insurers is a tough task but modern approaches to predictive analytics can help ease the burden. Predictive analytics have proven handy in addressing cost concerns of business processes where expert judgment is a key component of the decision making process. Innovative analytics can successfully pinpoint the operational inefficiencies and exposes factors that influence cost, making it easier for administrators to identify opportunities to improve cost efficiency.
In the article “Six Ways to Lower Costs with Predictive Analytics” Eric Siegel explains how predictive analytics can be utilized for cost reduction by targeting right customer. The article provides an overview of the six cost cutting value propositions that can help insures maximize profit and revenue namely :
1. Response modeling for direct marketing
2. Uplift response modeling
3. Targeted retention with churn modeling
4. Churn uplift modeling
5. Risk modelling
6. Fraud detection
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Digital Insurer's CommentsOperational costs are undoubtedly a major concern for insurers amid an intense competitive environment and uncertain economic conditions. Modern approaches to predictive analytics presents great opportunity for those insurers who want to raise the cost efficiency bar by gaining a unified view of the dynamic factors that drives cost.
Although the author focuses on removing marketing and sales inefficiency, the other applications of predictive modeling can be to remove
1. Infrastructural inefficiency
2. Distribution channel inefficiency
3. Underwriting inefficiency
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