The AI Surge in Insurance: What’s the Real Story?

To put it lightly, AI, machine learning, generative AI, and language learning models (LLM) have caused a storm in the business market – including in insurance. It’s left insurers grappling with some pretty big questions:

  • How do I use AI and generative AI effectively in my business? 
  • Where should I channel my efforts with this new technology?

They’re good questions to be asking. True innovation, especially with AI, isn’t just about trends; it’s also about creating value and enhancing humanity. Let’s navigate the noise and pinpoint the real story of AI in insurance today.

Generative AI is Revolutionizing Risk Assessment

Just how is generative AI making waves in risk assessment? Here are four examples: 

  1. Predictive analysis: By analyzing vast sets of demographic and publicly available data, generative AI can anticipate potential claims, helping insurers decide on coverage amounts.
  2. Simulation & prevention: With the ability to simulate various scenarios, generative AI helps insurers identify possible claims and insured might make in advance. This paves the way for risk prevention, helping carriers mitigate undesirable outcomes for their customers. 
  3. Model creation aid: Assisting in the creation of precise statistical models, AI can review and identify code bugs, accelerating software development.
  4. Personalized risk assessment: Taking things like age, health, and location into consideration, generative AI can tailor-make risk assessments for individual customers, making sure their premiums are accurate.

To hear about more real-life use cases of generative AI in insurance, check out our LinkedIn Live Replay: Looking Through the Hyperbole of Generative AI.

AI’s Underwriting Impact

AI and generative AI can help insurers out a lot in the underwriting process. The “simple” automations it can take care of are really powerful in and of themselves, but the generative powers of AI really shine here. 

These are three ways AI can impact an insurer’s underwriting practices: 

  1. Streamlining & speeding up processes: On the “simple” side of things, AI can automate tasks like data collection, running an analysis, and decision-making, making your staff of underwriters far more efficient and less error-prone. 
  2. Tailored decisions: Generative AI can take factors like age, health, lifestyle, and location into consideration to personalize underwriting, giving insurers more accurate risk assessment and pricing tailored to individual needs.
  3. Innovative underwriting models: In the machine learning realm, AI can be trained on vast amounts of historical data to create better underwriting models that will enhance risk assessment and pricing.

Streamline Claims Processing

A major leakage area for a lot of insurers, many claims departments are open to smart innovations that will help them cut costs and keep more of their revenue. Right now, smart insurers are using the technology to help them: 

  • Automate mundane tasks like data entry
  • Detect fraud by recognizing patterns in claim histories, records, and even a claimant’s internet activity
  • Prioritize claims to make sure urgent claims get addressed first, improving customer satisfaction 
  • Generate insightful business reports to find opportunities for better processes and smarter resource allocation

Fraud Detection

Fraud is one of the costliest expenses in insurance (the FBI estimates the cost at over $40 billion per year in the US), and it dings the insurer’s profits as well as costing individuals and families more in increased premiums to cover the risk of fraudulent claims. 

Pair with it the fact that fraudsters are getting more clever, especially with the help of AI, and you’ve got a massive gap that needs to be covered. Fortunately, AI can lend a serious helping hand, in a number of ways: 

  • Analyze claim patterns: By examining patterns, AI can spot suspicious activities, alerting fraud teams for further investigation.
  • Generate synthetic data: It can create data that resembles real-world insurance figures, bolstering fraud detection models with expanded datasets.
  • Identify anomalies: By comparing synthetic and real-world data, it can detect irregular patterns indicative of fraud.
  • Create fraud scenarios: Generative AI models can simulate fraud scenarios to test the robustness of detection systems.

Fraud detection and elimination is one of our favorite topics at EIS. Check out our LinkedIn Live discussion where we go over practical use cases of generative AI, including fraud detection.

Personalize Products & Services

In every sector of insurance, customers are expecting more and more personalization… from product packages and pricing to customer service interactions. It’s nearly impossible to provide all of this if done manually, and old school if-this-then-that personalization rules are easy for customers to see through and are starting to feel more and more out of date. 

Generative AI is revolutionizing this corner of insurance by providing a few things: 

  • Tailored product recommendations: Generative AI evaluates a customer’s needs, preferences, and budget to recommend the most fitting insurance product. This boosts customer satisfaction by aligning offerings with their requirements.
  • Enhanced customer service: Generative AI streamlines customer service using data from previous interactions, preferred communication methods, and even the customer’s emotional state. The outcome? Faster, more personalized responses.
  • Optimized marketing campaigns: With insights into a customer’s interests, purchase history, and social media engagement, generative AI can craft marketing messages that resonate. Insurers can then reach the right audience with precisely timed, relevant content.

Extract Valuable Business Insights

The beauty of machine learning is that it’s an AI function that can identify patterns far beyond what a human might notice, even in the most comprehensive data reports and dashboards. 

Beyond just identifying underlying patterns and trends, generative AI also enables insurers to generate hypotheses about their causes, leading to stronger decision making. What’s even cooler, though, is if your team is having a hard time coming up with a solution, generative AI can use the data and the patterns it’s mined to suggest some for you. 

For example, as consumers, we already know how companies like Netflix and Amazon use our behavior and consumption data to make suggestions based on our preferences, solving their need to get certain products in front of certain customers, without having individuals get lost in content or products that aren’t a good fit. This helps increase their customer retention and revenue per customer. 

Likewise, in-person retailers will use generative AI to extract info on customer behavior, that helps them better-allocate resources and improve supply chain management.

How are Real-World Insurers Using AI?

Even though generative AI has only recently taken the world by storm, it is something that’s been on the radar of ambitious, market-leading insurers for a while. Here are three concrete ways some of today’s top insurers are already using generative AI in their business: 

  • A leading P&C company uses AI to analyze data from weather patterns, social media data, and economic data to identify potential risks. Automatically generating this data analysis can be helpful in a number of things: from premium pricing to fraud detection.
  • A life insurance company creates personalized risk assessments for individual customers based on age, health, lifestyle, and location. This impacts either making an individual’s premium more affordable, or covering any additional risk that might be present due to one or multiple of these factors.
  • Another top life insurer uses AI to analyze claim history, medical records, and social media activity to identify fraud patterns. Once they develop a strong data set, they’ll be able to automate more of their claim approval/denial processes.

Needless to say, there’s a reason generative AI is front and center in the zeitgeist right now, and we’re excited about it. We believe that, if properly enabled and used correctly, generative AI could be a tool that ultimately decides which insurers will be the winners in the future, and which ones will be left behind. 

To learn more about what leading insurers are thinking about in this space, check out our LinkedIn Live: Looking Through the Hyperbole of Generative AI.

And if you’d like to know more about how the EIS Suite enables a future-proof technology setup, check out our website to learn more.

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