For decades, insurers have carried the unwelcome reputation of being technological laggards in the financial services sector.
Burdened by enormous, complex legacy systems that are costly to update, insurers have found it challenging to implement new technologies. Add that to cultures and regulations prioritizing strong, stable balance sheets over risky technology punts and insurers have often fallen behind adjacent industries in adopting innovation - or so the story goes.
One of the main reasons legacy systems have hamstrung insurers is that they were one of the first industries to adopt mainframe computers, directly leading to an unfortunate era of monolithic systems. More recently, the industry has been working to change this perception. In a period of rapid transformation, insurers have been quick to adopt new technologies like cloud computing and continue to drive cloud adoption across the value chain.
This reputation of being slow to evolve is especially unfair when applied to claims functions. Over the past two decades, claims operations have undergone significant evolution, shifting away from in-house mainframes, burdensome human-led decision making and inconsistent data capture toward more streamlined, digital-first processes. Insurers are much more data-led today, much more focused on how analytics and AI can improve the efficiency and cost of their operation, while, at the same time actually improving the customer experience.
Six key areas where claims functions can find value from AI:
1. Fraud detection
AI can analyze patterns and anomalies in data to identify potential fraud faster and more accurately than traditional methods. This not only reduces financial losses but also improves the overall integrity of the claims process. For example, AI can analyze structured and unstructured data to uncover hidden fraud patterns and complex relationships within social networks, substantially increasing fraud detection rates.
AI solutions can enhance fraud detection by using machine learning models that continually learn and adapt to emerging fraud patterns. It's important to note that we are not talking about one fraud model here. A network of fraud models working together makes the biggest difference. Insurers have significantly improved fraud detection by implementing a series of fraud scoring models, including supervised models and unsupervised neural networks, within their end-to-end insurance analytics and pricing platforms.
2. Claims triage and allocation
AI optimizes triage and allocation, ensuring each claim is handled by the most appropriate team or individual. This improves operational efficiency and ensures that complex claims receive the attention they need, while simpler claims are processed quickly. AI-driven triage can prioritize claims based on severity, complexity and potential cost, leading to better resource management and faster resolution times. This not only speeds up the claims process but also enhances the accuracy of assessments and decisions.
This is particularly important in markets like the United States, where legal representation in casualty lines is rising, driving up claim costs and closure timelines. Identifying claims that are likely to be represented or litigated early in the process helps triage the claim to the right team, enabling proactive settlement strategies and controlling costs.
3. Predictive analytics
AI-powered predictive analytics can forecast the likelihood of various outcomes, such as the probability of a claim being fraudulent or the potential cost of a claim. This allows insurers to make more informed decisions and allocate resources more effectively. Predictive models can assess the ultimate case value at the First Notification of Loss (FNOL) stage, enabling proactive claims handling and reducing indemnity costs.
Insurers often underestimate the value of powerful predictive claim models. This isn't just about using better prediction in claims, for example, where we have helped insurers route claims that are likely to jump to specialist teams using unstructured data. Insurers need to be thinking more holistically and using the insights their predictive claims models offer in reserving and pricing processes.
4. Customer experience
Enhance the customer experience by providing faster and more accurate service. AI-powered chatbots and virtual assistants can handle routine inquiries and guide customers through the claims process, while AI-driven decision support tools help claims handlers provide timely and accurate responses.
The result? Higher satisfaction and loyalty, as evidenced by NPS scores, and stronger retention.
5. Cost optimization
Automation of routine tasks and improved claims processing accuracy reduce operational costs, freeing resources for high-value activities. For instance, AI can optimize repairer selection based on core claim metrics, leading to cost-effective repairs and shorter settlement times.
AI-driven automation can handle repetitive tasks such as data retrieval, input processing and quality checks, resulting in significant time savings and improved accuracy.
In casualty lines, an integrated generative AI tool can help summarize legal correspondence, medical reports and investigation updates and recommend negotiation strategies, saving claims handlers significant time.
6. Real-time decision support
Real-time AI engines provide claims handlers with instant insights for faster decisions. This includes assessing the likelihood of fraud, determining the best course of action for a claim, and identifying opportunities for cost savings.
These engines can integrate multiple models to provide comprehensive insights and support across the claims process. This real-time capability ensures that claims are processed efficiently and accurately, reducing delays and improving overall performance.
Insurers see enormous benefit by deploying scoring models that can feature in their claims systems, so claims handlers have greater real-time insight when making decisions.
What's next for AI and claims innovation?
The integration of AI into the claims process offers insurers numerous benefits. By leveraging AI, insurers can transform their operations, delivering better outcomes for both their business and their customers. But insurers that are tempted to lag might well be targeted by fraudsters or see their customers turn away due to relatively slow processing and decisioning.
The reality is that the scope and breadth of claims AI applications are vast, and part of the secret to successful AI deployment is to identify and prioritize effort.
