AI to the Rescue – Navigating the Inflection Point in the Insurance Industry

The global life insurance and retirement industry is facing an inflection point due to the convergence of challenging economic, technological, competitive, and societal headwinds. A new whitepaper from E&Y advisory outlined how insurers can navigate to growth in the next decade. The paper contends that the product-driven business models of the past will not be sustainable in the future, primarily because insurers can’t adapt quickly enough to changing customer needs. The mature markets,  stringent regulatory requirements, low-interest rates, and tight margins further complicate the situation. Covid 19 pandemic has made it even more urgent for life insurers to redefine their role, take bold measures and address these changes. Here are the six scenarios of market leadership in the next decade based on research from EY:

The good news is that many global insurance leaders are already making large investments in digitization, innovation, and cultural change. Going digital has been a top priority as increased digitization not only helps reduce costs but also enhances customer experiences. The increasing adoption of predictive analytics, AI, and automation is gathering momentum in various business functions such as sales and marketing, operations, and risk management in this heavily regulated industry. According to McKinsey estimates, the potential total value of AI and analytics across the insurance vertical is approximately $1.1 Trillion.

Over the next decade, AI will be deeply embedded into the insurance value chain as the insurance industry embraces digital transformation. From automating manual processes in underwriting, eliminating errors and inefficiencies in claims processing, and enabling predictive insights to deliver superior outcomes, AI and machine learning in the insurance industry will provide unmatched power to insurers to address a multitude of problems and use cases, and help the industry navigate the daunting challenges:

• 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. Soon 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 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.

As insurance carriers get better at leveraging data and implementing predictive analytics, the focus will shift from product-led to customer-centric models. The insurers’ adopting and investing in digital capabilities to unify data, advanced analytics, and people will ultimately make the industry more agile, efficient and transparent. The winners will go above and beyond, offering personalized products based on individual customers’ unique needs, proactive interventions for service, and enhanced customer experience.