Insurance Risk Assessment with Big Data – Capgemini
Article Synopsis :
Improved data availability and evolving analytics help insurers see risk in a different light.
In the research report “Transforming Insurance Risk Assessment with Big Data”, Capgemini presents a high-level, actionable perspective on the benefits, challenges and opportunities of big data analytics in policy underwriting and, to a lesser extent, claims. With analytics it’s all about defining the questions you’re trying to answer, and the Capgemini team suggests five such questions. Answers to which potentially form the business case for increased investment in data accessibility and risk analytics:
- How will supplementing account-level management with individual risk-level management help mitigate risk and improve compliance?
- By moving from piecemeal data to unified information how much can be gained in operating efficiency?
- By how much can the cost of claims settlement be reduced by utilizing the ability to more accurately identify higher risks?
- How much can customer satisfaction and retention be improved through the ability to reward good risks instead of taking a “one size fits all” approach?
- By achieving lower loss ratios, how much can profitability and use of capital be improved?
Beyond the numbers, Capgemini cautions though analytics are bound to open new windows of opportunity for improved risk assessment, pricing, and prevention, there are four factors insurers must consider when endeavouring to build Big Data capabilities:
- Skill Sets – Do we have the people?
- Data Integration Needs – Do we have the Data?
- Ease of Use – What is the level of complexity? Will we be able to incorporate findings into the business or will they be too esoteric?
- Transferability – Can we scale success across the organization?
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Digital Insurer's Comments
Integrating Big Data into risk analytics is a challenging endeavour, but worthwhile as the payoff is competitive advantage. And, typically, the harder an edge is to find the easier it is to keep.Internal data combined with a growing array of external data helps insurers view the past with more clarity and the future with more certainty. Beyond risk assessment and pricing, analytics help enhance customer experience and improve operational efficiency. Most findings are incremental, others may be game changers.
As bullish as we are on analytics, it’s also wise to proceed with caution. Sources of real-time data should be chosen judiciously as they’re not always 100% reliable (nor secure) and prone to errors and manipulation. And good people with the right skill sets are hard to find, much less afford.
Proceed with caution…but by all means proceed.
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