Artificial Intelligence in the Insurance Industry

The insurance industry is riddled with time-consuming paperwork and red tape. This explains why the industry has been slow to adapt to technological change. But the COVID-19 pandemic marked a turning point for most insurance companies. The industry suffered a loss of about $55 billion – the second most significant loss event in the industry’s history. Almost overnight, insurance companies had to adjust to support remote workforces, upgrade their online mediums and expand their digital capabilities to support distribution. The pandemic also reinforced the need for the adoption of AI in the insurance industry. And that’s what we’re going to discuss today.

Today, most CIOs are willing to spend heavily on artificial intelligence use cases and pilot projects. According to McKinsey, AI investments can generate up to an impressive $1.1 trillion in potential annual value for the insurance sector.

Read on to find out how artificial intelligence is transforming the insurance industry and its future in years to come.

Faster claims processing

The claims management process is a monotonous, standardized, and attention-demanding process. Insurance agents must review multiple policies and scrutinize every paperwork detail to determine the rightful claim compensation for the customer.

It is mainly paper-based and not end-to-end digitized, which eats up around 50-80% of the premium’s profit returns. Manual claims processing is also prone to errors and considerable delays, much to the customer’s frustration. It is, in fact, one of the main reasons for losing an insurance customer.

Artificial intelligence can help automate manual claims processing. AI comprises umbrella technologies like deep learning, machine learning, and predictive analytics. These technologies aim to mimic the perception, reasoning, learning, and decision-making of the human mind. Machine learning algorithms, for instance, can quickly comb through all incoming claims, verify the basis of the claims, and provide quicker settlements to customers.

For example, in December 2016, Lemonade, an insurance firm powered by AI, set a world record for paying a claim in just 3 seconds with no paperwork. Another example of AI and insurance is Fukoku Mutual Life. This is a Japan-based life insurance company that is leveraging an AI-powered app to handle its medical claims processing. The app can automatically scan through all medical files linked to the case, derive relevant data, and auto-calculate correct payouts. After adoption, the employees’ productivity increased by 30%. The accuracy of payouts also improved significantly.

Accurate risk assessment

Every insurance client comes with a different level of risk based on the possibility of something happening and the severity of the incident. A thorough risk evaluation before insurance is crucial to prevent massive losses.

Traditionally, insurance underwriters based their customer risk assessment on the information provided by applicants. The dilemma, of course, is that applicants may lie or make errors, making these risk evaluations erroneous.

AI-driven risk assessment, specifically natural language understanding (NLU), allows insurance companies to comb through textual data sources. These include sources like social media posts, yelp reviews, and SEC postings. Thus, the insurer can pull relevant information to understand the client’s potential risk better.

Computer vision technology, coupled with IoT data (Internet of Things), allows insurers to record the state of the asset at the time of underwriting and continue making amendments in almost real-time. For instance, AI in the insurance industry can link a GIS data stream with the analytics system. Moreover, this will help the insurance company avoid in-person property inspections. It also allows the insurer to monitor the state of the property over time to fine-tune the policy price.

Fraud detection

Insurance fraud accounts for at least $80 billion annual loss for American consumers. It may happen at different stages of the insurance lifespan in different forms: application fraud, fatality fraud, and inflation fraud.

What has contributed to such staggering numbers is the fact that many insurance companies still rely on outmoded computerized systems and are thus unable to detect intricate fraud schemes.

AI-backed fraud detection systems can navigate the shortfalls of traditional rule-based systems. They have an inherent ability to identify repeating patterns. Thus, they can detect unusual behaviors amongst insurance clients that the human eye may miss.

AI technologies can also supplement the investigations by human analysts by providing valuable intelligence reports. They conduct quick, automatic background checks during the client onboarding stage, allowing insurers to forecast accurate risk assessments of businesses or individuals.

Consider a case in point of Anadolu Sigorta. This is a Turkish insurer that recently adopted a new predictive fraud detection system developed by Friss. Initially, the insurer was spending more than two weeks manually combing through filed claim forms to detect any fraud.

Considering they were processing up to 30,000 claims every month, the processing costs went through the roof. Upon the adoption of AI technologies, the insurer gained real-time fraud detection capabilities. Also, they achieved a 210% return on investment within 12 months and saved over 5 million Euros that would have otherwise gotten lost through fraud or processing costs.

Customer service

Good customer service can make a world of difference in the insurance sector. The adoption of AI-powered chatbots, in particular, can improve your customer service considerably.

According to a survey by Statista in 2019:

•43% of customers are comfortable using chatbots to apply for insurance policies.

•44% of customers are comfortable using chatbots to make insurance claims.

AI-enabled chatbots facilitate round-the-clock customer service and handle complex and monotonous tasks. They can, for example, evaluate and validate claims and pinpoint signs of fraud. This is viable as AI integrates years of behavioral patterns in chatbots.

Here are ways in which insurance providers can leverage chatbots in their day-to-day operations:

•Offer personalized quotes to new clients: Chatbots can gather pertinent information about the customer. These include their financial standing, vehicles, properties, and health status. Based on the derived insights, chatbots can provide potential customers with tailor-made products.

•Respond to policyholder queries: Policyholders can benefit from instant feedback from chatbots. They respond to queries on an insurance policy, deductibles, premiums, and states or countries of coverage.

•Cross-sell: Chatbots use machine learning to gather data about the customer’s preferences. Thus, they can predict which insurance solutions might appeal to customers. Based on these valuable insights, AI-powered chatbots can cross-sell by talking to the customer and offering suitable insurance solutions.

•Claims filing: Claims filing requires the policyholder to submit relevant information and attach documents. Chatbots can facilitate this by asking the policyholder for the relevant information and documents required for filing a claim.

•Damage assessment: A physical inspection may be required to assess the damage to the insured property or entity. Moreover, the policyholder can send photos or videos of the car accident or property damage to the chatbot for inspection. AI and insurance then use AI image recognition techniques to evaluate the damage and establish liabilities.

•Claims processing and settlement: The chatbot will notify the policyholder of the compensation amount and when the insurer will pay. Lemonade was able to settle its claim within 3 seconds because the whole process was handled by its chatbot called AI Jim. Likewise, Metromile, an auto insurance firm, deployed a chatbot called AVA to automate its claims service. The new AI claims assistant approved 70-80 percent of claims instantly.

Personalized services

According to Youbiquity Finance Report, 21% of customers say that their insurance providers don’t offer any personalization. Further, 80% of customers want personalized offers from providers. Personalization may take different forms. For example, exclusive offers and product recommendations based on the client’s needs and circumstances.

Personalization has a direct impact on a company’s bottom line. A report by Boston Consulting Group reveals that brands achieve revenue growth of 6-10 % when offering customized solutions.

Unluckily, most insurance providers confuse personalization with segmentation. They don’t personalize their insurance products on the individual level. Instead, they segment their customer base using various characteristics and then predict what might appeal to them.

Metromile Insurance Co is a good example of how to combine AI and insurance for customer-targeted offerings. The company offers personalized recommendations based on miles driven instead of driving behavior. Thus, customers can save 47% on average in comparison to what they used to pay to their previous can insurer.

Here’s how AI-powered personalization works:

Data Collection

The requirement for any form of personal targeting is data. The means of data collection and interpretation for use are crucial for insurers.

There are different ways to collect customer data. For example, you can scan through their social media accounts or track their online moves through website cookies. For car insurers, telematics can help generate valuable driver data.

There are also IoT-connected devices like heart monitors, smart watches, and smart home alarms. These data collection devices can relay customer data in real-time to your cloud.

Data Analytics

Advanced analytic tools come in handy to analyze data depending on the customer’s location, habits, and preferences. It then uses the data to uncover customization opportunities.

Sending personalized recommendations

Once you know who to target, it’s time to determine which communication channels are ideal. You can use text messages or emails to send recommendations and exclusive offers.

Final thoughts: Artificial intelligence in the insurance industry

Post the covid-19 pandemic, the insurance industry has accelerated its foray into artificial intelligence. And for good reasons- AI in the insurance industry can transform the experience for policyholders from protracted and bureaucratic to something swift, customized, and more affordable. AI-powered systems, for example, can help insurance companies achieve cost savings by streamlining workflows. They can also uncover new revenue opportunities as AI and insurance offer new cross-selling opportunities.

And that’s not all. AI technologies can comb through the mountain of customer data to understand what customers need. This enables insurers to offer tailor-made policy packages.