(Image credit: Pixabay.)
Data and analytics capabilities are the top function insurance business leaders are seeking from their IT counterparts in 2019. The projects that fuel these capabilities, however, come with a big price tag and an extended timeline. Even the most enthusiastic business sponsors can become skeptical of a data and analytics project’s value over time. Insurer IT leaders should showcase high-value use cases from the outset to bolster support from the leadership team, ensure data and analytics stay top of mind, and realize benefits early on.
There are numerous challenges and pitfalls awaiting carriers beginning a data and analytics initiative. Insurer IT leaders risk alienating their business users while compiling their data into a central source—a time-consuming process. It is also common to see carriers hire a data science team that is then unable to operate based on business needs. Analytics should be embedded into the business process to ensure that actionable insights are being acted upon.
Novarica’s Three Levers of Value framework provides a simple approach for offering a strong, compelling business case for data analytics. These Levers represent the three ways of creating value in insurance; each of them is powered by technology. They are: Sell More, Manage Risk Better, and Cost Less to Operate.
Selling more doesn’t only mean increasing the number of policyholders a carrier has. It also could involve improving existing services and introducing better products into the marketplace. Data can also help carriers reach new segments that may have been unknown or deemed unreachable previously. Now, insurers rely on data and analytics to improve product offerings—and make sure they are meeting the needs of today’s consumers—as well as to increase speed to market.
Business use cases for this Lever of Value range across the sales cycle. Analytics can ensure that insurers are using targeted marketing to tailor communications and marketing initiatives for specific customer segments. Data from internal and/or external sources can be used to pre-fill applications when it comes time for a prospective policyholder to submit or for pre-underwriting to offer prospects an estimated premium early in the process. Data and analytics can also be used for rules-based offers, shepherding distributors to the best product based on analytics, optimal product design based on factors like market capacity and customer behavior, and upsell and retention modelling.
Manage Risk Better
The way insurers navigate risk is changing as new capabilities become available. Predictive scoring in claims and underwriting as well as third-party data are helping carriers improve their risk selection process and their pricing. Advanced analytics are being used to improve claims and underwriting processes along with how carriers understand risk. Reducing losses, improving rate accuracy, and fighting fraud are top technology priorities for this area.
Most insurance carriers are managing risk via performance reporting to distribution partners, and many are also relying on predictive analytics for claims fraud scoring as well as claims severity scoring. Analytics can help insurers identify alternative payer or subrogation opportunities, generate predictive underwriting scores, and determine how each risk could affect aggregate exposure of the overall book of business. Data and analytics applied through machine learning and AI can also help carriers develop predictive models and underwriting guidelines.
Cost Less to Operate
Effective data strategies can lower costs by making analytics insights available across the organization and automating processes where possible. Insurance IT leaders may struggle to receive credit for productivity improvements that do not lead to headcount reductions. But tech-enabled efficiency and automation investments that increase throughput and capacity should be credited with those benefits to cost justify the efforts.
Claims and underwriting skills-based routing can automatically assign risks to specific adjusters depending on factors set by the carrier; similarly, claims and underwriting straight-through processing automatically adjusts and scores claims or risks, then sends them to be reviewed or hand adjusted. Quick quote functionality speeds up the initial quoting process and can reject risks based on previously determined data points.
Value-Based Business Cases Give Initiatives Time to Deliver Value
Data and analytics journeys should be backed by a value-based use case approach. Without emphasizing the real-world business benefits waiting at the end of a project, sponsorship and funding may both become scarce outside of the IT department. Developing an effective, results-based business case for a data and analytics initiative can help ensure that it receives enough resources and can be evaluated against the organization’s business goals.