The Remaking of AI-Based Insurance: Agile, Efficient & Personal

The year is 2025, and you are on vacation enjoying the sunset on the beach. Suddenly your smartwatch flashes an alert that an intruder is in your backyard and that your home is about to be invaded! You run to your room, grab your smartphone, open an app, and instantly see a surveillance drone flying over your home, streaming live data, and capturing the scene. You can hear the alarm blaring and see that the intruder is baffled and aborts the mission. You receive a call from your virtual insurance agent informing you that the situation has been assessed, and a claim has been filed automatically. The virtual agent has already shared the pictures and video with the insurance company, thanks to the data sent to the cloud by the drone. By the time you settle down for dinner, your virtual agent texts you that all the damage was assessed, and the claim was processed. The system has also placed an order to replace the broken windows and damaged items and that all the repair charges will be covered as part of your smart insurance policy, voila!

While that’s wishful thinking for how the services ought to be (predict & prevent) compared to today (detect & repair), the insurance businesses are embracing emerging technologies. Over the next decade, AI will be deeply embedded into the Insurance industry value chain from underwriting, claims processing, and recommending personalized products. Leveraging AI and machine learning in the insurance industry provides unmatched power in helping insurers with a multitude of problems and use cases, and the industry is just getting started:

• Underwriting & Pricing – Underwriting is the process insurance carriers use to determine risks and calculate a fair price for insurance coverage. While pricing personal auto policies are mostly automated today, the process is still manual for commercial property. For commercial property insurance, the underwriter needs a lot of information such as occupancy, data on adjacent buildings, loss estimates, estimated replacement costs, typical hazards, etc. Some of the data may be available online but may be outdated and might require onsite verification.  Hence human judgment is critical. A PwC report on top insurance issues noted that carriers are devoting considerable attention to helping underwriters use models and AI-driven tools to supplement their knowledge. In this way, underwriters are becoming increasingly comfortable marrying what they’ve learned from personal experience with insight from models to make the most informed decisions possible. The day is not far when underwriting will be fully automated, supported by machine learning models that ingest vast amounts of internal and external data through an ecosystem of vendors that promote data exchange. 
• Claims Processing –  In the future,  machine learning algorithms will manage claims routing, increasing efficiency, and accuracy dramatically. According to a Mckinsey report, claims for personal lines and small-business insurance will be fully automated, enabling carriers to achieve straight-through- processing rates of more than 90 percent and dramatically reducing processing times from days to hours or minutes. Unlike the traditional practice involving manual methods of first notice of loss, the burden will no longer be on the customer to inform the insurance carrier about any relevant event. Instead, the process will be automated, relying on  IoT sensors and real-time monitoring to prevent incidents from happening and sending the notifications for critical events requiring immediate attention.  Smartphone apps will handle all interactions with the capability to trigger claims automatically upon loss. Technologies such as natural language processing, deep learning, and text analytics will support claims processing.
• Fraud Detection –  The total cost of insurance fraud is estimated to be more than $40 Billion per year, according to the FBI. There are thousands of claims filed every day. Assigning insurance agents to investigate each case will be time consuming and expensive.  Using AI automation, insurers can evaluate millions of documents and data points in record time. They can cross-reference several databases and incorporate multiple external data sources, which would be impossible without automation. Anomaly detection models can identify deviations from normal, and flag cases for review.  Leveraging learnings from previous fraud cases and using real-time data, AI, and ML models can identify threat signals before substantial problems arise. 
• Other Use Cases – There are several different insurance industry applications where AI and ML are in use today. One common use case is using predicting analytics for estimating policy cancellations. Customer churn is one of the most problematic aspects of customer management for insurance companies. When high-value customers churn, insurance companies often replace existing businesses with new, more costly customers that lower profitability. Understanding the drivers of customer churn and creating AI and machine learning models that can accurately forecast churn behavior can boost profitability and revenues.

The insurance carriers are undergoing a massive transformation, becoming more comfortable with the latest technologies,  and shifting focus from product-led to customer-centric models. This transformation is primarily led by the insurers’ adopting and investing in digital capabilities as part of a broader strategy to unify data, advanced analytics, and people across Property & Casualty, Life, and Health insurance segments. The insurers of the future will leverage AI to streamline processes, lower costs, and improve customer experience. The winners will go above and beyond, offering personalized products based on your unique needs, automated interventions for service or repair, and preventing a crisis from becoming a catastrophe.