Artificial Intelligence (AI) and Machine Learning (ML) are transforming the insurance industry in a number of critical ways. AI is a branch of computer science that develops machines to mimic human intelligence and solve problems faster and more accurately than we can. Traditionally, AI works by analyzing a set of inputs and producing the right output more consistently than humans can. ML is a subset of artificial intelligence and provides the computer with a set of known solutions to a given problem. The computer is then in charge of generating, or learning, the logic required to solve similar problems.
The insurance industry is using AI and ML in underwriting new business, distribution, claims, policy servicing, and enhancing the customer experience, among other areas.
AI-based technologies and machine learning data models are enabling insurers, program administrators, and MGAs to move from manual underwriting of property and casualty insurance to a more automated, standardized, and objective underwriting process. This new data set can be used in conjunction with configurable application programming interfaces (APIs) and internal, historical data analytics. Underwriters can use advanced analytic models to price risks more accurately, improve loss ratios, cut costs and save time at each stage of the underwriting process, and shorten quote-to-bind times.
For example, when looking to insure a property, AI and ML can be utilized to quickly obtain data on the property. The data can come from various sources including industry data (construction, healthcare), Internet data (SEO and web advertising), environmental data (climate, weather), business data (business credit rating, business review, commercial real estate), and location data (cell tower, GPS, IoT sensor, map, drone images). A high-resolution property image, for instance, can be analyzed in seconds and provide a multitude of characteristics for the underwriter to review and better assess risk factors. These same insights can be used for loss prevention.
A report by McKinsey shows that commercial insurance purchasing, particularly in the small to middle-market, is facilitated as a result of the use of drones, IoT, and other available data that provide underwriting with enough information for AI-based cognitive models to generate a bindable quote.
Chatbot deployment, document ingestion tools, AI, and ML for data extraction and analysis are being utilized by insurers to improve operational efficiency throughout the claims process. The use of drones, IoT-connected sensors, and mobile apps are also playing a role in automating claims, helping insurers improve the customer experience while shortening the time it takes to settle claims. Carriers are also benefitting from a better understanding of claims costs thanks to machine learning and predictive models. These insights can help a carrier save millions of dollars in claim costs by implementing proactive management, quick settlement, targeted investigations, and improved case management. Insurers can also be more certain about the amount of money they set aside for claim reserves.
Machine learning also assists insurers in identifying potentially fraudulent claims more quickly and accurately and flagging them for investigation.
Robotic Process Automation (RPA) is being employed to perform repeated tasks so that operational teams can focus on more complex actions. For example, the automated intake of policy details enables integration with a policy administration system to retrieve policy details and reduce the manual effort required to find and locate relevant fields for policy endorsements. This results in a shorter turnaround time for policy processing and servicing.
With today’s digital-savvy customers, virtual assistants are emerging as a quality resource for insurers to handle inquiries. Chatbots are being equipped with the ability to foster deeper customer relationships through AI-powered Natural Language Processing, enabling insurers and agents to improve the customer experience. These chatbots utilize user data and contextual data from multiple sources to resolve customer queries in real-time and help to maintain a consistent brand experience across all customer interactions. Virtual assistants can also provide a wide range of services, from policy renewal to upgrades to accepting claims and beyond, to meet the diverse needs of customers and empower them to make their own decisions.
These are just a few ways in which the insurance industry is adopting AI, ML, and other advanced technology to improve and transform underwriting, operations, and the customer experience.
Sources: Accenture, McKinsey, EY