Michael Elliott

Data mining techniques may be able to more readily identify red flags or spot patterns that suggest fraud, but insurers will still need to rely on data-minded teams to create the algorithms and systems that will detect it and root it out.

As analytical efforts mature at an organization, it becomes increasingly important to build cross-functional teams in which data science is the primary focus and tool.

Truly infusing an insurance organization with data-driven decision-making cannot start with hiring external employees and building stand-alone departments. It must start with frontline employees.

Within companies transitioning to analytics-driven organizations, there are significant opportunities for frontline insurance professionals who can develop their data analytics knowledge and bring data-driven decision-making to their work and effectively collaborate with data scientists.

As big data spreads to more core insurance operations, data scientists will increasingly rely on leaders in traditional insurance roles to identify areas where data analytics and expensive modeling tools can have the greatest impact on business outcomes.