This piece is by Martijn Gribnau, Chief Customer Success Officer at Quant.
Efficiency and accuracy are the two main ingredients to profitability and client satisfaction. The largest roadblock? Administrative tasks and the cost associated. In fact in 2024 there were over 270,000 people in the US working as insurance claims and policy processing clerks, or underwriters. What do all of these roles have in common? They can all be performed with an agentic artificial intelligence agent. By comparison, the UK has nearly 300,000 employees in the insurance industry in total. Both countries are primed for AI automation.
Agentic AI can eliminate routine administrative tasks in the insurance industry via automation of complex, multi-step workflows. This all occurs with minimum to no on-going human supervision. This leads to better, more accurate outcomes at a fraction of the cost. Plus, these agents scale endlessly and work 24/7 without need for breaks, holiday, or benefits.
Three Areas Perfect for Agentic AI
Claims Processing
Agentic AI will streamline the entire lifecycle of claims from initial filing to settlement in a fraction of the time and cost that a human can. AI agents can automatically initiate and handle the claim intake process via web, chat, or phone. With the use of natural language processing the agent can obtain relevant details from customer conversations and documents to open a claim file instantly.
Then, AI autonomously assesses the claim’s complexity and determines the best course of action. For simple claims, AI can fast-track straight-through processing, including damage assessment using computer vision on submitted photos and cross-referencing policy details. For complex claims, the agent can automatically escalate the case to a human adjuster, providing a pre-populated file with all necessary information.
In fraud detection, AI agents continuously analyze data and documents for suspicious patterns, flagging potential fraud for human investigators to review in real-time. In 2024 alone there were 33,027 instances of insurance fraud amounting to £157.24 million in the UK. It is negligent to not deploy AI in fraud detection at this point. As these numbers continue to rise, it clearly shows that human oversight and monitoring is not enough.
Payouts and communication can easily be handled by agentic agents in instances where straightforward and slightly complex information is needed. The agents can make payout recommendations and automate claim updates to keep the customer informed throughout the process and send all payments with little to no human involvement.
Pricing and Underwriting
Agentic AI works alongside their human teammates as underwriters by automating data collection and analysis which leads to faster, more accurate risk assessment and personalized pricing. This is done as AI agents automatically pull data from multiple sources, including broker submissions, policy forms, and third-party data sets. They can identify missing information and proactively communicate with brokers to resolve gaps without human intervention.
In the area of risk assessment, AI filters applications by complexity and profitability. It provides a comprehensive risk profile to an underwriter, highlighting potential concerns. For low-risk or standard cases, the AI can even make autonomous underwriting decisions within predefined guidelines set by their human overseers.
Dynamic pricing can be initiated by agentic AI through analyzing real-time data from multiple sources and continuously learning from outcomes, AI agents can dynamically adjust premiums to ensure personalized and competitive pricing.
In essence, AI acts as a digital assistant for underwriters, summarizing complex cases and drafting initial reports. This frees up human experts to focus on the nuanced judgment calls that require strategic thinking and create decisions.
Renewals
Agentic AI changes the renewal process from a static, manual effort into a personalized and proactive customer engagement strategy. This has the potential to boost retention rates via greater client satisfaction. Agentics will also automate the renewal cycle by autonomously handling back-end tasks for low-risk policies. This includes generating a personalized renewal notice to processing the update completely, with little to no human involvement.
Agentic agents can also aid in upselling via personalized offers. The agent will analyze a customer’s history, lifestyle changes, and claims data, and then create a customized renewal proposal based on that individual’s needs. This all happens automatically as the agentic agent will initiate targeted outreach to customers whose policies are due for renewal via their preferred channel.
Additionally AI can identify policies at risk of lapsing based on behavioral data. It can then trigger an alert for a human agent or offer automated incentives to retain the customer.
Benefits and Challenges
The benefits of agentic AI deployment are undeniable. There will be massive efficiency gains via the automation of high-volume, repetitive tasks. There will be exponentially improved accuracy as AI eliminates human errors in data entry and analysis. This leads to more reliable risk assessments, pricing, and claim outcomes.
Plus, customers benefit from faster response times, quicker claims settlements, and personalized interactions. And your internal clients, your employees, become empowered to drive results and revenue by being freed from routine work and the ability to focus on high-value tasks that require empathy, strategic thinking, and complex problem-solving.
This new path of deploying agentic AI will not be without challenges. The insurance industry is heavily regulated, and AI use, particularly regarding bias, explainability, and compliance, will take time and diligence to work through. During your coding and deployment phases you must put heavy focus and strategy into governance frameworks and audit trails.
Then you have to deal with legacy system integration. Many in your field currently work with outdated IT systems that aren’t designed to support modern AI, increasing the cost and difficulty of deployment. As with any AI, agentic drives the best returns when it is fed clean data. You will need to do a comprehensive data audit and cleaning before your deployment begins.
Lastly, you’ll need to address your talent gap. In order to fully leverage agentic AI you’ll need the consummate talent. Start with upskilling training paths on AI and then look into external candidates that know both the industry and the technology.
Insurance companies are by nature more risk adverse due to the nature of the business.
However to adapt to an inevitable Agentic AI wave you must pilot use as soon as possible. It is far more risky not to learn fast than to be too late of an adopter and end up missing the advantages it will bring.
Agentic AI has the potential to revolutionize the insurance industry in the areas of claims, pricing and renewals, but this will be no simple task. Remember that AI isn’t some bright and shiny new toy that just works with the push of a few buttons. You must be strategic, purposeful, and intentional in your coding, deployment, and introduction to your team. This sort of slow and measured approach will set you up for the greatest chance at success and the highest possible ROI.
Martijn Gribnau is Chief Customer Success Officer at Quant, which develops cutting edge digital employee technology.
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