How AI Improves Insurance-Customer Interactions and Relationships

James Hoshor | November 19, 2018 | In the News Mobile Strategy

In a previous blog, I shared my “Key Steps to Implementing AI in Your Enterprise.” If you haven’t read it already, I highly recommend it as a prerequisite to today’s article on AI/ML in the insurance industry (of course, I’m a little biased).

If your organization is considering AI and Machine Learning (ML), but struggles with how and where it could be applied to drive value, the following are examples of AI/ML solutions being used today by some leading insurance organizations:

  • Chatbots/AI assistants: Allstate implemented chatbots and AI assistants to respond to internal agent inquiries and provide 24/7 guidance on business protocols.
  • Driver performance monitoring: State Farm and Liberty Mutual apply ML algorithms to customer data to help inform the development of products for insurance customers.
  • Insurance market analytics: Progressive uses ML algorithms to interpret driver data in an effort to monitor market trends and identify business opportunities.

As you can see, AI/ML can help organizations achieve key business drivers, such as increased employee productivity, new product/service offerings and enhanced customer experiences.

Many opportunities exist for applying AI/ML in the insurance industry. Let’s highlight some of the big ones:

Retention and account penetration are key challenges for traditional insurance organizations that have been around for decades—due to “e-surance” companies popping up everywhere and offering competitive rates and easier interactions for consumers via mobile and web platforms. So, traditional insurance providers are looking for ways to stay competitive and retain customers by leveraging new technologies. As the old saying goes “it costs five times as much to attract a new customer than to keep an existing one.”

One method for strengthening customer relationships and retaining customers is by leveraging any available data (qualitative and quantitative) to identify cross/up-sell opportunities. Environmental factors may also be considered. For example, if a customer is located in a location experiencing high growth, insurers can manage their customer strategies accordingly. Using AI/ML to analyze this data enables insurers to more accurately predict which customers are at risk of leaving, and lets the company develop a proactive strategy by positioning tailored messages and product offerings to prevent cancellation or lapse, before it’s too late.

Another key area that has seen significant change in the last decade is underwriting. Many product lines have become commoditized, making underwriting more of a pricing exercise than a risk assessment. Using aggregated data and criteria programed into underwriting or new enterprise systems, AI can be used to analyze existing books of business, identify potential risks, and provide appropriate pricing recommendations. This doesn’t eliminate the need for underwriting, however. In fact, it should be managed at a book level, analyzing for profitability. AI can be used to continually monitor a book’s performance and to notify underwriting management if specific criteria or thresholds are exceeded.

Similar to underwriting, claims adjusting and processing has also become a decision based more on a claim adjuster’s training, experience, and department best practices. Some challenges claims adjusters face today are a) lack of access to all the data required for analyzing a claim, and b) lack of time or ability to examine all the data in order to identify the best course of action. AI solves this by assessing all the required data and utilizing ML to run algorithms based on best-practice strategies to deliver the best claims process possible. For example, one main objective for insurers is settling claims for a reasonable amount. Claim-settling rides a fine line. If a customer feels they didn’t receive a fair settlement for a claim, the insurer risks an unsatisfied customer who might spread the word or leave the company altogether. On the other hand, if an insurer overpays a claim, they risk damaging the loss ratio and overall profitability. AI can be used to compare a single claim to an entire book of claims to determine what the best settlement amount should be, using multiple factors (e.g. amount of settlement, estimated overall expense, settlement type (cash vs. replacement vs. services), possibility of litigation, etc.)

Many additional insurance industry AI/ML use cases exist. Insurers just need to learn how to use the vast amounts of data they already possess as well as the new data types already being captured to their benefit (e.g. social media interactions, data from connected devices). Harnessing all this data with AI/ML can trigger multiple processes and drive more productive and efficient client interactions.

If you haven’t given AI much thought, it’s definitely time to start. Industry-leading companies are already seeing great successes with AI and machine learning solutions. You can, too. Please don’t hesitate to contact us to help your organization develop an AI/ML strategy and roadmap that guides you in leveraging these emerging technologies to ensure alignment with business drivers and guarantee success. Our Emerging Technologies Kickstart or our Machine Learning Kickstart may be just the boost your business has been looking for.

James Hoshor

James is a Senior Mobile Strategist & Solutions Architect for Propelics. He has over 20 years experience in executive leadership, strategic planning, marketing and business development in information technology. For the past 10 years James has worked with many clients across multiple industries, including financial services, insurance, retail and manufacturing, approach mobile strategically to identify and deliver solutions that result in market differentiating solutions and great user experiences.

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