Four Ways that AI Enables Insurers to Differentiate

Insurance companies are generating more data than ever before, and the value of that data is rapidly increasing as new products are able to properly analyze and make use of it. Beyond traditional analytics, one way that data can deliver even more value for insurers is by introducing artificial intelligence (AI) to reduce cost and time spent, as well as to drive a better customer experience and smarter pricing decisions.

AI has been a popular topic for tech companies lately, but its use will be critical for insurance companies going forward. In fact, according to a recent survey, 67 percent of insurance CXOs agree that AI is critical to their organization’s ability to differentiate in the market. In my opinion, that differentiation will occur as a result of AI’s ability to do four key things:

Make the use of analytics more pervasive by increasing access to data and insights

First, the introduction of AI will require companies to build a solid data foundation, and employees will find themselves almost immediately capable of making better decisions with that one improvement alone. To get their data in order, companies will need to first identify all internal and external data sources available to be leveraged, then create an analytics data pipeline that combines those sources into meaningful insights. After creating a complete, trusted, aggregated journey, the whole organization should be able to view customer interactions and activities across channels—web, mobile, in-office—for the entire customer life cycle. With this foundation in place, insurance companies can begin to introduce AI in a few areas, perhaps starting with tasks that are easily automated. In the meantime, insurance employees will also reap the benefits of having access to a more comprehensive data set that is personalized to their workflow.

As an example, let’s consider using AI to determine if a particular customer became a burden — requiring too much time or financial resources — for customer service or claims. Armed with better access to existing data and insights, an underwriter can assign a risk score to that customer to drive a better pricing decision. That action and that customer’s information can automatically be fed into the machine-learning model to continuously drive improved outcomes and ensure that the sales and marketing teams can optimize their customer acquisition strategies to target the most profitable customers—and avoid those most likely to introduce losses. Managers and executives can observe real-time updates on the overall customer journey experience through their own lens or data pipe.

Bridge the “last mile” by delivering the right insights to the right people at the right time

AI will enable insurers to bridge the last mile by delivering the right insights at the right time. What we mean by “the last mile” is the gap that exists between the work done creating the insight behind the scenes and the user taking action based on that information presented. Things like moving from scoring claims for fraud on a weekly or nightly basis to scoring a new claim upon first notice of loss (FNOL) uptake or re-scoring the claim any time a new piece of information is added to the claim file—enabling the user to ultimately take action more quickly.

In underwriting, the submission is first scored against predictive models in real time for critical areas such as “appetite match,” “broker sincerity,” “projected loss ratio for this class,” and then ranked for underwriting prioritization. The last mile of analytics in this case involves creating a ranking system around the organization to answer questions like “Which risk should I work on next that will be most advantageous for our company?”

For marketing teams, embedding analytics and AI into the last mile reveals new insights from your customer journey and experience analytics. For example, one can create machine learning algorithms that identify the profiles of your “best prospect” based on the characteristics of your existing book of business. When one of these prospects lands on your website for a quote or comes in via an agent submission, the “last mile” of insights is used to score this prospect and determine the appropriate action to take. As a result, insurers can better segment the customers and improve retention of customers with high lifetime value (LTV).

It’s clear that some insurers have already seen the value of analytics for bridging the last mile. According to McKinsey, on average, leading analytics-driven companies spend almost five times more money than their peers do on advanced analytics solutions that include machine learning and AI. Of their IT budget, 25 percent or more is dedicated to analytics-related expenditures in such areas as third-party data acquisition, technology systems, analytics talent acquisition, and embedding these insights into the end user’s workflow.

Enable less experienced underwriters and processors to perform like veterans

AI’s capabilities remove much of the guesswork from employee decision-making, enabling your newly trained underwriters and adjusters to perform like more experienced adjusters by getting recommendations at their point of work. While training is still a necessary component, AI helps less experienced employees get up to speed much faster, and it removes some of the risk associated with a new employee. For example, a less experienced adjuster may needlessly overcompensate a customer for a claim when left to their own devices. With insights from AI, this same adjuster can access recommended next steps based on past data, all without leaving their point of work.

Allow for faster and more strategic data-driven decision-making across your organization

Using AI allows for faster and more strategic data-driven decision-making across your organization. Let’s look at fraud as an example. If an insurance company shares examples of known fraudulent activity, AI can look at the data points and events that took place prior to the fraudulent claim and use the data to train the model. For future fraudulent claims, the machine learning algorithms will pinpoint those patterns that it would take humans too long to find—if they found them at all. When that pattern is recognized, the fraudulent claim will be automatically rerouted to the Special Investigative Unit (SIU). This feedback, now captured within the investigation process, enables the model to continuously improve by becoming smarter and more accurate with each new claim. This is especially impactful when compared to humans who rely on a gut feeling or may not notice a pattern until it’s more obvious.

AI and machine learning can transform and improve your organization in many ways. By incorporating the concepts outlined above, insurance companies will find that they’re able to make analytics more pervasive, deliver the right insights to the right people, help less experienced employees make the right call, and make faster, more data-driven decisions overall.

Mark Rusch, vice president – Insurance at GoodData, is an advanced analytics executive and thought leader, with deep Insurance and cross-industry expertise.