Article Synopsis :
Insurers have been using predictive analytics to identify high risk groups in the underwriting process for quite some time. High risk group focus was the result of the lack of robust internal/external data and high cost of processing power plus storage capacity. These limitations are irrelevant today hence Predictive Analytics should be used to target entire consumer population including low cost low risk group to generate better insight on how to unlock significant business value.
In his white paper on predictive modelling, John Wilt, head of property and casualty consulting at Tata Consultancy Services (TCS), North America discusses why business leaders must implement predictive analytics and how it will enable the enterprise to remain profitable and competitive.
The white paper outlines 4 steps to build successful predictive analytics capabilities:
1. Data Transformation
2. Data Mining
3. Model Development
The primary drivers for broader application of predictive analytics are identified as:
• Cost reduction
• Desire for growth in a soft market, slow-growth conditions
• Search for competitive advantage
The author also discusses various applications of predictive analytics in fields like Marketing, Customer Acquisition, Underwriting and Pricing, Claims, Fraud Detection and Reduction on which insurance carriers should focus today.
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Digital Insurer's CommentsAdvancement in predictive analytics, digitisation and technology reach calls for a paradigm shift in the business model of insurers. The product design and marketing should now aim for the most profitable population.
Predictive analytics enables the insurer in identifying the most profitable customer base, but there are a few challenges which needs to be addressed first like:
1. Choosing the correct models as there are a lot of choices available
2. Dealing with the complexity of the model
3. Lack of skilled Analytics workforce (particularly in Asia)
Insurers in Asia are starting with simple models to help segment customers and deliver more focused advice.
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