Insurance firms routinely use data analytics to prioritize risks, prevent losses related to fraud, and optimize expenses. For companies that regularly deal with high volumes of data, the ability to gather third-party data, accident statistics, and personal information from policyholders is essential for speeding up the decision-making process.
Captive insurance firms can benefit from data analytics as well. In a 2016 article entitled “Captives Capitalize on Analytics”, Robert J. Walling outlined several scenarios by which captives could utilize predictive analytics.
The principal and consulting actuary of Pinnacle Actuarial Resources, Inc., Walling described how captives could use data analytics to benefit their operations and risk management efforts in general. He also emphasized the drawbacks of running a captive without analytics, citing missed opportunities related to the inability to utilize predictive analysis.
One of the key benefits of data analytics is that it allows captive insurers to leverage data that they already have‒or have access to‒in the service of their organization. Furthermore, data analytics gives captives numerous opportunities to enhance their overall risk management efforts.
On the contrary, captive insurers that don’t take advantage of data analytics will experience more operational challenges than companies that utilize this model. They are also much more likely to miss opportunities for mitigating risks.
Munich Re America Inc.’s Strategy and Analytics head Risa Ryan emphasizes the importance of premium and loss data for insurance firms. For Ryan, data analytics is integral for developing strategy, optimizing pricing, and ensuring insurers’ profitability.
Ryan further states that data is at the core of everything that insurers do. According to her, companies that develop and maintain proprietary data can utilize them to optimize their risk appetites and portfolios.
Captive insurers that develop and maintain exclusive data would also be better positioned to identify organic growth shortcomings in their existing portfolios or new insurance packages. They would then be able to create new business opportunities.
Ultimately, in-house data analysis capabilities provide insurers with many strategic benefits. They can reveal new perspectives on business strategy and help companies improve their market position.
Furthermore, in-house resources enable captive insurers to develop solutions more closely attuned to their client’s needs. Consequently, these firms can better formulate custom products and structures based on their unique experiences with specific clients.
Despite the many benefits of data analytics, we’ve yet to see widespread adoption in the insurance sector. Three factors, in particular, have been identified as hindrances to the adoption of the technology:
Difficulty with skills development. Skills development is one of the biggest stumbling blocks to the widespread implementation of predictive data analytics. This is particularly true among captive insurance firms, especially those servicing several corporations.
Challenges related to model deployment. Many companies experience considerable difficulty with model deployment, particularly with regard to understanding and defining problems that models are designed to solve. The challenges are often compounded by the costs entailed in getting firms to the level where they can deploy models.
Insufficient infrastructure. Many smaller companies simply do not have the technology and human resources necessary to develop in-house data analytics capabilities. For most captive insurers, the overwhelming challenge is how to obtain the hardware and software required for developing this capability.
A report entitled “Best Practices Report: Practical Predictive Analytics” published on TDWI.org stated that predictive analytics is poised for widespread adoption. According to the report, more and more companies are eager to utilize predictive analytics and machine learning after realizing their value.
But while many companies already utilize predictive analytics, widespread adoption has yet to become a reality. TDWI suggests that if more firms continued the planned adoption of predictive analytics, as many as 75% to 80% would already be utilizing the technology. Unfortunately, current estimates place the actual figure closer to 35% to 40%.
Predictive modeling and data analysis are clearly essential for maintaining competitiveness in the insurance sector. Captive insurers will have to adopt these new technologies if they hope to remain viable in the coming years. Although the industry is still a long way from widespread adoption, more and more captives will undoubtedly utilize data analysis over the next several years.
Caitlin Morgan Captive Services provides clients with captive insurance solutions supported by years of experience in establishing the successful formation and implementation of a wide range of captives. To learn more about how we can help you, please contact us at (855) 975-4949.