Few industries stand to gain as much by adopting artificial intelligence as insurance. From customizing policies to processing claims to preventing fraud, the opportunities to reduce costs and improve customer satisfaction can be found throughout the value chain.
Insurance companies rarely relate to their customers other than at the time of the claim, so making that experience as simple and productive as possible is key to customer retention. Natural language processing can be used to transcribe phone conversations in real time to fill out a customer’s claim form automatically. Machine learning algorithms can check for errors and immediately alert customers to the need for additional information. Some routine approvals that required human decision-making in the past may also be delegated to machines.
The time lapse between making a claim and receiving payment can be a major source of customer frustration. By applying image recognition and analysis to photos uploaded from the field, adjusters can more quickly approve claims and dispatch payment, boosting customer satisfaction. One solution being tested in the UK analyze images of vehicle damage and issues a decision within seconds, enabling repairs to be dispatched quickly. The system can also detect suspicious claims, such as damage that was not related to a specific incident.
Fraud is a major problem for insurers, accounting for 10% of all claims expenditures in some regions. Using machine learning, companies can scan millions of historical records to identify common patterns of fraudulent claims. AI can also pinpoint cases that show the highest likelihood of fraud so that investigators can focus their attention on the most serious and expensive candidates.
When customers contact their insurance companies, there is often a level of anxiety involved because the call relates to a claim. The last thing an insurer wants to do is put the customer on hold while a service representative sifts through history files.
Using a combination of natural language processing and machine learning, insurance companies can create unified customer records from many contact channels, including emails, phone calls, forms and website interactions. The result is that representatives answer the phone knowing the customer’s full history and are thus better prepared to respond to questions.
The same technology can be used to analyze a large number of customer communications to look for common complaints that need to be addressed or to spot frequent inquiries that can be handled with a more-efficient resource like an FAQ.
Ultimately, insurance companies are pursuing the goal of markets of one: policies and rates that are customized for each customer. Machine learning enables micro-segmentation by identifying behaviors that correlate to different categories of risk and matching them to policy-holders.
For many customers this will result in reduced premiums and the opportunity to lower their own insurance costs by agreeing to behavioral goals. For example, Progressive Insurance has handed out more than $700 million in discounts to customers who let the company track where and how they drive. Other insurers are experimenting with discounts tied to the use of fitness trackers and heart monitors. AI has the potential to achieve a win-win solution: saving customers money while maximizing insurer revenue.
To learn more, read the white paper Data Search and Discovery in Insurance