Automating Data Analytics for Improved Customer Experience

By John Beal, SVP, Insurance Modeling Services, LexisNexis Risks Solutions

Storing data for the sake of storing it makes no sense if you can’t accurately link all of the data, normalize it and prepare it for immediate consumption. Because most machine learning or statistical algorithms heavily rely on attributes to feed them, a wide range of these predictors are needed to service a broad range of your customers’ needs. Innovation in data analytics now allows us to gather data and apply attributes to them as well as provide even more opportunities to score and predict what consumers will do during the entire insurance life cycle.

Simple data analytics has evolved into advance analytics, which combines the disciplines of machine learning capabilities with big data, or volumes of filtered and unfiltered information coming from internal and external sources, and data scientists, who know how to treat the filtered and unfiltered data, normalise it and apply predictive models. Because of this evolution, we can help insurers automate data analytics within their workflows and create a better customer experience for their policy holders and prospects.

As more and more data continues to become available, business users need to ask questions about how the data might be applied across all of their decision points. For consumers, these data analytics tools within the insurers’ infrastructure create a better experience because agents can more easily serve them with a quicker and more accurate quoting experience.

When offering mobile services, the main point to be taken into consideration is the requirement for a user-friendly interface with accurate and fast, real-time information. Insurers need to use internal and external data sources to make sure they have the most accurate information about the customer, for example, and with a single point of entry into the insurer’s workflow, the information can be filtered, analyzed and delivered, ready to be used. Then the consumers can spend their time quickly confirming accurate data rather than entering it. You need a trusted vendor to provide pre-fill data and accurate scores or attributes for scoring. This, in turn, will send the information quickly and will generate a better and more accurate premium pricing and a better overall experience for the consumer.

Additionally, with the adoption of cloud and data analytics, access barriers and time delays will reduce significantly. In addition, democratization of analysis tools will provide the ability for more employees to generate more insights and distribute more analysis driven conclusions. Data access controls, compliance and privacy training will become a wider concern outside of the traditional data consumers like actuarial, pricing and modelling teams. As more employees are allowed to create individualized insights, standardization and communication of data sources, permitted use cases, filtering requirements and data field naming become more complex. Furthermore, the demand for new third party data sources will expand to new users to generate new insights into their customer base.democratization of analysis tools will provide the ability for more employees to generate more insights and distribute more analysis driven conclusions. Data access controls, compliance and privacy training will become a wider concern outside of the traditional data consumers like actuarial, pricing and modelling teams. As more employees are allowed to create individualized insights, standardization and communication of data sources, permitted use cases, filtering requirements and data field naming become more complex. Furthermore, the demand for new third party data sources will expand to new users to generate new insights into their customer base.

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