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The Challenges of Implementing Advanced Analytics

Becoming analytics-enabled is perhaps one of the most important evolutionary steps forward an organization can make. The rewards of data-driven decision-making can be a powerful boost to the bottom line. For insurance companies, this may include using underwriting predictive models to increase profitability through more granular pricing, driving a six...

By Kevin Bingham, John Lucker, Laura Ward, Stacey Peterson | Updated on October 01, 2014

Becoming analytics-enabled is perhaps one of the most important evolutionary steps forward an organization can make. The rewards of data-driven decision-making can be a powerful boost to the bottom line. For insurance companies, this may include using underwriting predictive models to increase profitability through more granular pricing, driving a six to eight-point reduction in loss ratios. On the claims side, predictive models have helped insurers better segment and triage high severity workers’ compensation and bodily injury claims, driving a four to 10-point reduction in claims spend.

An important part of the analytics journey is overcoming the numerous challenges an organization encounters when experiencing the end-to-end development and deployment of predictive models. Model development (e.g., data assessment, data acquisition, data cleansing), scoring engine development (e.g., scoring engine and database design, development, testing, deployment), and business implementation (e.g., strategy formation, change management, tools for measuring business) are some of the common questions organizations should consider.

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