25 AI Insurance Examples to Know

Summary: AI in insurance leverages artificial intelligence to help insurers operate more efficiently, reduce fraud and offer personalized insurance policies, transforming the way the industry serves clients and manages risk.

The insurance industry has always dealt in data, but it hasn’t always been able to put that data to optimal use. Until now.

With the rise of artificial intelligence, which analyzes and learns from massive sets of digital information, insurers are embracing the technology’s many facets — from machine learning and natural language processing to robotic process automation and audio / video analysis — to provide better products.

What Is AI Insurance?

AI in insurance uses advanced algorithms to craft individualized policies, automate underwriting processes and provide estimates with greater accuracy to customers. It can boost efficiency, accuracy and customer satisfaction, revolutionizing how insurers operate and manage policies.

Customers, too, are benefitting from practices like comparative shopping, quick claims processing, around-the-clock service and improved decision management.

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AI Insurance Use Cases and How It Works

Insurance is undergoing a major transformation driven by artificial intelligence technologies. This is reshaping the industry by improving efficiency and accuracy across key areas:

1. Underwriting and Risk Assessment

AI analyzes large volumes of data — from medical records to driving behavior — to assess risk more accurately and quickly. This leads to faster underwriting decisions and more personalized insurance policies for customers.

2. Automated Claims Processing

AI tools like image recognition and natural language processing streamline claims by automatically reviewing documents, detecting damage and estimating costs — allowing claims to take minutes to process.

3. Fraud Detection

AI helps identify suspicious patterns and flag potentially fraudulent insurance claims in real time, reducing losses and improving fraud investigations.

4. Personalized Customer Experiences

AI-powered chatbots, virtual assistants and data-driven recommendations offer faster support and more tailored insurance products based on individual needs and behaviors.

AI Insurance Advantages and Disadvantages

Below are some of the ways AI has reshaped the insurance industry, leading to benefits (and some challenges) for insurers and customers.

Advantages of AI in Insurance

Streamlined Insurance Processes

AI helps insurers find evidence of potentially fraudulent claims and speeds up the underwriting process, during which insurance companies evaluate potential customers to determine their risk. AI can do these tasks faster — and more cost-effectively — than human employees by training models with historical data and using the models to automatically process new customers and claims.

Reduced Human Bias

Rates for car insurance are traditionally determined by a buyer’s personal factors, such as credit score, income, education level, occupation, and marital and homeowner status. But these factors penalize low-income buyers and aren’t directly related to a driver’s likelihood of getting into collisions. Companies using AI to build models can reduce these biases by actively excluding these factors during the training process.

Flexible Insurance Options

Insurers can track the habits of drivers for organizations like Uber and Lyft with wearable technology. If drivers for a service demonstrate safer driving habits, insurers can then offer that service lower premiums. Devices can also be used to activate insurance coverage only when drivers are actually driving, cutting costs while insuring service workers who would otherwise have had to purchase their own policies.

Safer Driving Habits and Less Vehicle Accidents

As an example, if a delivery company that insures its drivers is experiencing a spike in accidents or traffic mishaps, AI and machine learning systems can crunch the data collected by connected devices to recognize patterns that would explain the reason for the accidents. Based on that analysis, the insurer can make recommendations to the company that would help reduce the number of accidents and expensive claims.

Disadvantages of AI in Insurance

Lack of Model Transparency

When AI-based risk models are built, it can be harder to pin down what insurance companies are basing higher premiums on. For instance, if companies use neural nets — an AI technique that’s the basis for deep learning — the resulting model is opaque. Insurance companies would know what factors were used to train their AI model, but companies wouldn’t know how the model internally related those factors to risk and which inputs are more important.

Potential Proxy Discrimination

Even if companies don’t provide data about factors like gender, race and income, AI could still find other factors that stand in for that data and have effectively the same outcome. If something like the time of day when driving is taken into account to build a car insurance model, that could be a proxy for income level.

“If people who drive at a certain time of night are more likely to have claims, an insurer might say we should charge them more,” said Daniel Schwarcz, professor of insurance law and regulation at the University of Minnesota. “But it may be that the reason there’s a correlation is not because driving at that time is riskier, but because people with lower income drive at the time of night, and people with lower income are more likely to make claims.”

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AI Insurance Examples to Know

While there are pro and cons to the technology, insurers and customers have widely reaped the rewards of AI-based algorithms, making processes simpler and safer.

To get a better sense of how AI impacts the insurance industry, check out these AI insurance applications.