The integration of artificial intelligence (AI) is accelerating the evolution of the insurance industry. Firms that are adopting the technology hope to improve the customer experience, develop new products, provide coverage for previously uninsurable risks, and cut costs through automation.
Capgemini reports that AI-powered insurance experiences have increased customer satisfaction by up to 20 percent, and management consulting firm McKinsey & Company predicts that the use of AI-enabled automation by insurers could lead to a 40 percent decrease in operating expenses by 2030.
AI is increasingly proficient at performing tasks that have been difficult for computers to execute; tasks such as recognizing images, identifying spoken words, and parsing unstructured data, according to The National Association of Insurance Commissioners (NAIC). Increasing computing power and memory capacity, cloud computing, big data technologies, and global connectivity have enabled computers to run complex algorithms and handle the massive amounts of data now available faster than a human could.
The speed at which data is generated, particularly by devices with embedded “Internet-of-Things” technology, has accelerated adoption of AI. The NAIC notes that insurers are sitting on a treasure trove of big data — and data is the main ingredient AI requires to be successful. Insurers hope that AI will help them leverage the abundant, unstructured data to increase customer engagement, create more personalized service and marketing, develop new products, and match customers with appropriate products.
Whether it is structured or unstructured data (e.g., social media, wearables, telematics, sensors, news, weather and traffic reports), AI may help insurers make sense of big data.
AI Use In The Insurance Industry
AI has three main insurance industry functions that can help firms achieve their goals, according to reinsurance firm Swiss Re. First, it can automate repetitive knowledge tasks (e.g., classify submissions and claims). Second, it can generate insights from large, complex data sets to augment decision-making (e.g., portfolio steering, risk assessment). Third, it can enhance products and risk solutions. While AI has the potential to impact many industry processes, initial efforts are typically leveraging these three functions in the following five areas:
Customer service
Developments in this area are meant to improve customer access while easing the burden on insurers to respond to individual inquiries, according to insurance and financial services firm Nationwide. The experience of purchasing insurance may be faster, with less active involvement on the part of the insurer and the customer. Information about individual behavior can be provided to AI algorithms that create risk profiles, so cycle times for completing the purchase of an auto, commercial, or life policy may be reduced considerably.
Virtual assistants, such as chatbots, can interact with customers at any time to address common questions and provide basic information. Chatbots may also provide in a more advanced capacity, able to perform tasks such as completing transactions, giving basic advice, checking billing information, and initiating claims. Machine learning — a subset of AI — may enhance pattern recognition in interactions with consumers to provide new insights and improve decision-making.
Claims
Advanced algorithms can handle initial claims routing, increasing efficiency and accuracy, according to the NAIC. AI-enhanced automated customer service applications may handle many policyholder claim interactions through voice and text, directly following self-learning scripts that interface with the claims, fraud, medical service, policy, and repair systems.
AI may also reduce claims processing times from days to just hours or minutes. Reduced claim processing time may cut labor costs for insurance companies, and greater claim processing accuracy may produce other cost savings. It could provide recommendations based on quick data analysis, arming agents with the right information. The time agents may have previously spent on routine manual claims work can be used to focus on higher-skilled, valuable tasks.
Underwriting and Pricing
Insurance companies may use AI in underwriting to quickly develop more competitive and personalized prices. This may help determine the best rate and reduce the time needed to implement new pricing into the system. The majority of AI-supported underwriting will be automated and enhanced by a combination of machine and deep-learning models, according to McKinsey & Company.
These models will be powered by internal data as well as a broad set of external data accessed through application programming interfaces provided by outside data and analytics services. Information collected from mainline carriers, reinsurers, product manufacturers, and product distributors may enable insurers to make underwriting and pricing decisions before offering a product bundle quote that can be tailored to the buyer’s risk profile and coverage needs.
Sales
AI may open new digital sales channels and strategies while simplifying the sales process for agents and brokers through integration with third-party sales applications. AI-supported “Insurance-in-a-box” models, for example, would enable mortgage and real estate companies to embed homeowners and life insurance into real estate transactions, according to reinsurer RGA. The model allows a range of companies to embed widgets, APIs, and URLs; tailor quotes in real time; and integrate appropriate insurance offers seamlessly into existing home and loan purchase customer journeys.
A combination of automation technologies (robotic process automation, machine learning, low/no code software programming), analytics technologies (AI, predictive analytics), and usage-based connected Insurance (IoT, telematics) will result in more targeted insurance products and increased loyalty, according to insurance platform provider EIS.
Fraud Detection
AI and machine learning systems could scan enormous volumes of data to detect trends that may indicate fraudulent conduct. Insurers may detect and prevent fraud in real-time by automating the fraud detection process, saving billions of dollars in bogus claims. Insurance fraud costs more than $40 billion per year, according to the FBI. AI tools may identify oddities in claims data and pinpoint inaccurate information entered by customers much faster and more efficiently than a human could. It can then flag such situations for a claims specialist.
AI can learn and adapt over time, allowing it to stay ahead of new and evolving fraud schemes. One advantage is to not only detect fraud but act on it proactively. A second advantage is a reduction in the time required to process valid claims, which improves overall customer experience.
The Added Value of AI
AI tools continue to evolve and create opportunities for business growth. The use of AI has proliferated across all industries, led by an increase in accessible data, increasing computing capabilities, and changing consumer expectations. It will enable the insurance industry to move from a “detect and repair” framework to a “predict and prevent” framework, allowing insurers to help their customers manage their risks and avoid claims altogether, according to the NAIC; but the added value of AI only comes from the smart combination of AI models and human processes.
A 2021 PwC survey found that the most effective use of AI in insurance companies is in the customer experience space, while insurers’ top AI-related concern is the potential for cybersecurity breaches. AI is likely to enable the insurance industry to become more efficient and offer new solutions, but Swiss Re notes that so far, an entire system — including human interaction — is required for its use, not just a standalone AI model.
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