Article Synopsis :
Shifting demographics and customer behaviours, emerging risks, and new forms of competition all influence insurer strategy. Increasingly, senior management is leveraging data analytics to inform not only strategic but operational decision-making.
In “Leveraging Advanced Analytics and Optimizing Big Data”, Wipro touts the value of data analytics in operational decision-making and provides examples of analytics use cases specific to the insurance industry. Per the report, the ability to deliver data-driven decisions in the following core operational areas is becoming critical:
- Leveraging underwriting/pricing and big data for risk-based pricing and improved risk selection
- Capturing customer sentiment, buyer behaviors, product afﬁnity models, and segmentation
- Improving operational efﬁciencies through cost reduction, activity-based costing, automation, and reporting
- Preventing claims leakage and fraud and capturing enhanced service improvement metrics
- Enhancing commission, channel and distribution effectiveness using multiple data points
The report’s analysis and use cases are organized around six “key themes” for business analytics:
- Customer analytics
- Operational analytics
- Commission analytics
- Underwriting/pricing analytics
- Claims analytics
- Analytics-enabled IoT
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Digital Insurer's CommentsTaken together, the six themes identified and discussed in this report cover every aspect of your typical insurer—which, we think, is the point. No part of your business is safe from a competitor with more data and superior means of analysis.
With data volumes exploding (to an estimated 44 trillion gigabytes by 2020, per Google) from sources ranging from toothbrushes to smartphones to automobiles, insurers must find a way to not only capture meaningful data, but manage and analyse it to some defined strategic end. Where to start? This report breaks the problem down into manageable chunks.
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