By Steve Cavolick
Insurance companies face pressure from nearly every direction today.
From a financial perspective, the industry as a whole hasn’t fully recovered from COVID, with many insurance companies’ stock market valuations still below pre-pandemic levels. Amid an employee turnover rate of over 26%, insurance companies must create new and better engagement models for customers and products that are customized.
Add to that an environment where natural disasters are increasing in volume and destructiveness, and these companies are looking for new pathways to profitability.
We already know that data can be used to blunt the employee churn problem by refining workforce and retention strategies. After all, technology and people are merging into automated processes that create superior offerings, and infusing automation everywhere is a key goal in insurance. Insurers must use their data as the on-ramp to AI everywhere and remain relevant through product innovation, improved underwriting, and making the customer experience better.
Here are some potential Analytics use cases LRS sees for insurance companies that create new ways delight customers and reduce processing time:
Product Portfolio Simplification: Most of the new digital insurers have no more than 10 products, which makes it easy for customers to understand and select ones right for them. State Farm, in contrast, has about 100 products. Industry studies have shown that the top 10 to 15 in-force products usually bring in over 90% or written premiums. Using data to understand product acceptance and profitability will allow insurance companies to cut down on the operational cost of new product development, while allowing agents and digital channels to promote more profitable products.
Create Efficiency In Underwriting: Use advanced analytics and AI to increase straight-through-processing (STP). Many insurance companies are still using rules-based decisioning. Why not leverage all the historical policy data, but enhance it with outside and regulatory data sources? Automated data pipelines will feed that data into Machine Learning models. With ML, insurance companies can do more granular segmentation faster, and more easily identify risk factors to improve pricing and reduce losses. Also, by increasing STP with ML, fewer policies need to be touched by underwriters, increasing speed and customer satisfaction.
Understand New Risk: This one isn’t so much about using data as it is about shielding it and adjusting product offerings to protect against new dangers around us. Data and cybersecurity risks are growing by the day, so what about offering policies to protect the inherent value of data owned by companies? As AI takes a larger role in automated decisioning and process automation, does it make sense to offer coverage against machine-learning liabilities stemming from biased data or undetected model drift?
Data and AI will play a key role in helping traditional insurance companies modernize and deal with the threat from digital competitors. Armed with new technology platforms, insurers can use their data to increase premiums, maximize STP, improve the customer experience, and maintain loss ratios.
If you are interested in learning more about how LRS can help you use all of your data to build AI and analytical applications that drive better results, please contact us to request a meeting. Not there yet? We also offer strategic roadmapping services and can help you build an information architecture that will support your current and future analytical applications.
About the author
Steve Cavolick is a Senior Solution Architect with LRS IT Solutions. With over 20 years of experience in enterprise business analytics and information management, Steve is 100% focused on helping customers find value in their data to drive better business outcomes. Using technologies from best-of-breed vendors, he has created solutions for the retail, telco, manufacturing, distribution, financial services, gaming, and insurance industries.