Becoming analytics-enabled is perhaps one of the most importantevolutionary steps forward an organization can make. The rewards ofdata-driven decision-making can be a powerful boost to the bottomline. For insurance companies, this may include using underwritingpredictive models to increase profitability through more granularpricing, driving a six to eight-point reduction in loss ratios. Onthe claims side, predictive models have helped insurers bettersegment and triage high severity workers' compensation and bodilyinjury claims, driving a four to 10-point reduction in claimsspend.

An important part of the analytics journey is overcoming thenumerous challenges an organization encounters when experiencingthe end-to-end development and deployment of predictive models.Model development (e.g., data assessment, data acquisition, datacleansing), scoring engine development (e.g., scoring engine anddatabase design, development, testing, deployment), and businessimplementation (e.g., strategy formation, change management, toolsfor measuring business) are some of the common questionsorganizations should consider.

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