5 use cases for AI in insurance

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For hundreds of years -- more than 300 in fact -- the traditional insurance model has evolved slowly, and has proven remarkably resilient, mostly due to significant entry barriers, including capital requirements, established relationships and tacit underwriting knowledge. The advent of artificial intelligence (AI), however, represents a quantum leap of sorts in how insurance is sold and bought, as well as how customers are served. At the same time, it’s creating the opportunities for companies the forge or cement their leadership positions within the insurance industry
Here are the top five reasons why insurance companies are likely to implement AI, or to continue to invest heavily in it. For upstarts, these represent opportunities to disrupt the old guard; for established firms, these approaches can stave off new competitors and distinguish themselves from traditional rivals.

Deliver insurance policies in minutes. Traditional insurance companies are notorious for the huge amount of information they request and time needed to deliver the policies to customers. As insurance products become ‘micro’ and on-demand, and data becomes ubiquitous, it now possible to underwrite in minutes. Many insurance companies have deployed virtual agents to interact with customers to gather relevant data in the most convenient formats. For example, rather than asking customers questions about their car/make details, customers can simply upload a picture of a VIN number. Similarly, for life/annuity products, customers can simply upload a selfie picture and AI will determine age, BMI, healthy habits, possible diseases and determine if medical underwriting is required.

Process claims instantly with zero touch. Insurance has a relatively low-touch customer relationship. Claims is a key moment of truth when insurance companies can make a lasting impact on the relationship. The claims process typically involves as lot of unstructured data such as pictures, drone videos, witness statements and notes, and it takes a lot of time to process this data manually. Many AI-based technologies, such as natural language processing and image analytics, can help derive insights from unstructured data, and process claims with speed and accuracy.

Provide always-on customer service. Consumers are expecting insurance companies to meet standards set by technology companies such as Amazon, Apple and Netflix. It is no longer possible to delegate customer service to agents or brokers completely. Customers expect multi-channel capabilities to reach to insurance companies with their service requests and questions. Again, AI comes to rescue to enable 24X7, always on, multi-channel customer service by deploying virtual agents (chatbots) and personalized, interactive videos.

Automate processes that were traditionally hard to automate. Insurance companies deploy a large number of people to perform many of the operational processes manually. It has been harder to automate the processes due to variations in products, state specific rules and lack of adoption of standards across the value chain. For example, one of the key problem in the industry is ‘intake’ of data from business partners. When brokers send data to carriers or when carriers send data to reinsurers, they send data in variety of formats in the form of excel or pdf files. A large number of people are required to map this data and process it manually.

With AI, it is possible to predict this mapping of ‘intake’ format and continuously improve by leveraging machine learning, and automate the processes significantly. By marrying cognitive technologies with RPA tools, insurance companies can automate processes such as endorsements, customer service requests, claims processing, and provide faster turn-around time and significant ROI from automation.

Continually improve the value from data – Insurance companies have predictive models to determine the maximum possible loss, probability, pricing and RoE, etc., that are critical to run the insurance business. However, as insurance companies innovate products, address new risks and reach out to new customer segments, these models get out of the tune rapidly, and it is difficult to keep up with the changes. With AI, it is possible to provide a feedback loop for machines to learn and adapt to changing business needs.

Underwriters deal with multiple unstructured documents such as underwriting guidelines, reinsurance contracts and loss sensitive pricing. It is a tedious and error-prone job to review documents and extract information to make business decisions. But with AI engines, users can extract the information from unstructured documents and align it to common vocabulary. This makes the information easily accessible through a search engine or virtual assistants.

In conclusion, AI has the biggest potential to transform the insurance business. It is now possible to personalize at-scale, innovate with new products and automate processes that traditionally relied on human intelligence. With many vendors in the market with cloud-based offerings, the technology is accessible and ROI is very promising. Insurers investing in AI today will be able to establish a fast mover advantage to differentiate with products and services in increasingly competitive market.

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