Insurers need good data management capabilities in order to access and utilize these various kinds of data in an efficient and timely manner. Each data source or repository has its own access requirements; and the data itself will be in a variety of formats. An insurer’s systems must have the right connectors and protocols, and the IT group must continuously monitor the data management processes of access, validation, classification, storage, error resolution, and so on.
Here again, business rules can play an important role in managing the priorities and sequencing of data acquisition. For example, a predictive score for homeowners insurance may require external data from a mortgage company and the public registrar of deeds. The BRE can confirm the availability of that data, or request it from the appropriate sources. If a calculated predictive score is does not give a clear accept or reject signal, a BRE can then trigger a request for a property inspection at an additional cost.
Good data management capabilities will enable an insurer to reach an underwriting decision more quickly and accurately.
Optimization means organizing activities to achieve stated goals, subject to constraints. Typically, in personal lines insurance, the goals will be some combination of growth and underwriting profitability. Insurers often follow a zigzag course while trying to find the appropriate combination of those two numbers. For example, in recent years, a well-known insurance company strongly emphasized growth through pricing and underwriting actions, only to find that its underwriting results were quite poor. It then reversed course by raising prices and significantly restricting sales to new policyholders in certain territories. Optimization tools can clarify such trade-offs among growth, underwriting profitability, product line mix, distribution channels, home office and field staff, etc.
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