A New Era of Predictive Analytics
AI and predictive analytics have been making waves for some time, but their impact has surged in the last few years. With new technologies being released, insurance companies are eager to stay at the forefront of research and development. The goal? To leverage these capabilities to improve their operations.
In an industry where policies are often sold by financial advisers, customers can end up owning policies that may no longer suit their needs if that relationship ends. Traditionally, insurance companies haven't built services to engage these orphaned customers once the advisers are no longer in the picture. This has resulted in missed opportunities for retention, upsell, cross-sell, and introducing new products. To drive efficiencies at scale, AI and predictive analytics are crucial in understanding customers better and presenting them with the right products at the right time.
Digital-First Adoption
One significant improvement brought by AI is the ability to provide digital journeys for customers. The adoption of digital channels by consumers has accelerated, especially since the pandemic, with more consumers buying policies online, using apps, and engaging with digital channels. Looking at the income protection market alone, in 2024 over 30% of consumers purchased cover directly from insurers versus other channels. The preference for digital experiences is particularly popular for younger generations and is a key consideration they cite when choosing an insurance provider.
Insurtech providers are coming to the market with a digital-first mindset, offering self-serve capabilities, requiring data and automation to handle data transfers, review data, and analyse it efficiently. Insurers who adopt these technologies are poised to benefit from growing consumer demand for digital channels and the enhanced insights that data and AI can provide.
Personalising Customer Experience
Data and AI are now essential in personalising customer experiences that were once adviser specific. Previously, agents would call customers and have personal interactions. Now, AI can analyse customer behaviour and tailor interactions accordingly. For insurers, this opens up the potential for cross-sell, retention and enhanced customer satisfaction through targeted messaging.
For instance, AI can differentiate between customers who miss a paying a premium because they need the money versus those shopping for a better product. AI-driven segmentation allows insurers to provide customised journeys: offering cash-out benefits to those in financial need and highlighting policy benefits to those looking for an upgrade. This personalised approach helps retain customers rather than losing them to competitors.
Automating Routine Tasks
Through automation, AI and ML has revolutionised numerous routine activities previously handled by agents, underwriters and claims handlers:
- Data Entry: AI streamlines data entry, reducing manual errors and speeding up the process.
- Policy Issuance: Automated systems can now issue policies quickly and efficiently, reducing wait times.
- Claims Processing: AI can analyse claims data, identify patterns, and process claims with minimal human intervention.
There will always be complex cases requiring the expertise of human claims or underwriting assessors. When supported by AI and ML, senior staff can be utilised more effectively, providing their expertise throughout the entire value chain.
Tackling Underwriting Challenges
Underwriting is a critical area where AI and predictive analytics can make a substantial impact. Different customers buy policies for various reasons, and the underwriting process can take a significant amount of time. Delays in underwriting can lead to lost opportunities as potential policyholders may explore competitive offers or change their financial priorities. AI can help reduce the underwriting cycle time, increasing the likelihood of policy conversion.
By analysing the factors contributing to cycle time—such as product complexity, process inefficiencies, or distributor issues—AI can help insurers optimise their workflows. Stacking cases based on urgency rather than duration can improve placement ratios and overall efficiency. Research by Celent suggests this is a popular area of adoption, with 65% of survey respondents already using GenAI in underwriting processes, and 12% utilising it significantly.
Easing the Learning Curve
The introduction of more AI tools has made people more comfortable with the technology. Familiarity with tools like ChatGPT and Midjourney has demystified AI, while regulatory clarity has provided guidance on its usage.
When AI is embedded in platforms, users do not necessarily need to understand the technology. AI can seamlessly integrate insights into workbenches, automatically changing priorities, staffing, and case assignments to enhance service and performance. This allows underwriters to continue their work without needing to learn new tools, making the transition smoother.
Embracing the Future
As AI technology evolves, the life insurance industry will continue to benefit from improved operational efficiencies. By combining a single source of truth, persona-based experiences, real-time insights, and advanced analytics, digital insurance platforms enable insurers to enhance efficiency, elevate customer satisfaction, and maintain a competitive edge. Sapiens' AI-enhanced, open, integrated business solutions empower insurers with the speed to market to swiftly adapt to market changes and offer innovative products and services more efficiently.
The future is bright, and the possibilities are endless as insurers embrace AI-driven solutions to deliver better service and performance.
Gain more insight into how AI is revolutionising operational efficiencies in Life Insurance in this article from Sapiens. By clicking "Learn More" you agree to the data protection statement below.
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