AI Use Cases Accelerating Adoption in Insurance
As insurers continue to adopt AI, where will they focus? AI is already delivering real benefits in the following parts of insurers’ businesses:
Risk assessment and underwriting: AI can improve the precision of underwriting processes by analyzing vast amounts of data, including geographic and environmental information – such as weather patterns or flood zones – to assess property risks more accurately. Using AI, insurers can evaluate risks more thoroughly and improve their ability to straight-through process or involve an underwriter based on specific risk factors associated with a property’s location.
Pricing: Insurers are also using AI to create advanced machine learning models to develop much more accurate and sophisticated pricing models and strategies. While these models are already delivering powerful predictive capabilities, insurers must strive to build transparency and explainability as they move forward. This is important to demonstrate how algorithms make decisions and satisfy regulatory requirements.
Personalization: AI is helping many insurers deliver more personalized experiences by analyzing customer data to predict each customer’s preferences and needs. For example, AI can review a customer’s life events, behaviors, policy history, and other details to recommend new products or provide tailored coverage options. AI can also help insurers provide more proactive support, such as offering personalized discounts or informing policyholders about possible coverage gaps.
As insurance companies continue to build out these AI capabilities and use cases, they’re also looking to do more with AI-driven analytics.
Related: Data Modernization for Insurance: 74% Still Use Legacy Technology
AI-Driven Analytics: Unlocking Deeper Value in Insurance Data
By using AI-powered analytics, insurers can better understand patterns across customer data, claims trends, and risk factors, allowing them to make more informed, data-driven decisions and optimize their business strategies.
AI-driven analytics can be a game-changing innovation, and insurers clearly recognize the valuable new opportunity they represent. Yet once again, the majority of insurers still have some work to do to fully capitalize on their vision.
Our research found that 52% of executives reported that they currently use analytics to validate operational decisions in various processes. Only 6% claimed to be using analytics to their full potential: making predictive decisions to generate optimal outcomes.
But the good news is insurers are determined to do more with third-party data to fuel new analytical models. 27% told us that they were going to grow their investments in third-party data by 6-10% in the next three years – a 5.5% increase over last year. Insurers cited IoT data and telematics as the types of third-party data they were most interested in.
Why AI Adoption in Insurance Is Gaining Momentum
While insurers recognize the transformative potential of AI and AI-powered analytics, many are still in the initial stages of adoption, and could be held back by outdated, legacy technology (more on this topic in future articles). As investments in AI, AI-powered analytics, and third-party data continue to grow, insurers are poised to close this gap and unlock significant new opportunities for growth and efficiency.
Related: Why MGAs Are Facing an Insurance Capacity Crisis – and What Comes Next
Turn Insights into Action and Drive Results with AI in Insurance
For more information about our research findings related to AI and advanced analytics (and many other insurance trends and technologies, download the Earnix 2024 Industry Trends report today.
