Every organization has areas ripe for driving monetization from internal data and information. Take the Insurance Industry as an example.
The success of an insurance company lies in its ability to assume and diversify risk. This means being good at underwriting, pricing, and managing risks and claims which will effectively drive market share and maximize returns on reserves. But let’s look at some common claims related data issues that may be impeding these initiatives.
- Mismatches of Losses against Written Premiums: Losses may be being paid where the corresponding premium isn’t being collected, or perhaps for business agents are not licensed to write. (i.e. state limitations) This can impact the adequacy of existing rates and future ratemaking. It can also lead to inaccurate DOI reporting.
- Incorrect Cause of Loss and Coverage Combinations: Claims adjusters may be picking invalid combinations. (e.g. Coverage Type is Bodily Injury, Cause of Loss is Hail) These types of issues create inaccurate loss reporting which directly impact loss reserves and loss ratios. Furthermore, the accumulation of incorrect data results flawed development patterns used in actuarial models.
- Overpayment of losses on Policy Limits: The company may be paying out too much in losses, due to improper governance of losses being matched to incorrect sub-limits for bundled policies. Again, this can lead to the some of the same issues noted above.
Working with your business we can help you build and substantiate business cases to support your existing priorities, or help you identify areas of opportunity. Examples might include increasing revenue per policy holder, accurately recording the average cost per claim, maximizing return on surplus, or lowering loss ratios.
Here is our 5-step Data Monetization process to identify and drive your value-added opportunities:
- Identify the relevant stakeholders to gather input and insight
- Review existing or help build out new use cases to grow revenues, reduce costs, or manage risk
- Develop business cases by identifying root causes and critical data drivers, establishing assumptions and quantifying financial impacts
- Execute each business case, by implementing data governance and data management activities
- Track benefits realized from executable data monetization activities against planned assumptions
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