Insurance companies are using AI systems for a wide variety of purposes, from risk assessment to fraud detection to underwriting. The goal of each of these AI use cases in insurance is the same: To improve accuracy and efficiency, and to deliver a better customer experience.
Risk assessment is the process of assessing the risk associated with offering insurance. For medical malpractice coverage, the goal of risk assessment is to determine the likelihood of a physician facing a medical malpractice claim.
Medical malpractice insurance allows physicians to transfer the financial risks of medical malpractice claims to an insurance company. Insurers must understand the likelihood and severity of potential malpractice events before issuing a policy. AI tools give insurers the ability to analyze vast amounts of data for a more comprehensive risk assessment.
AI-enhanced risk analysis processes can gather more data points that go into risk assessment, reduce mistakes caused by human error, and identify details or patterns that humans might overlook. That results in more accurate, more comprehensive risk assessments.
Chatbots are AI programs that are designed to simulate real-life human conversations through text. They’re now common in the insurance industry, especially for customer service.
AI-enabled chatbots allow customers to easily contact their insurer for help with common requests, such as billing questions or copies of policy documents. Chatbots enhance the customer experience by minimizing the need for traditional support lines, which may have high call volumes and long wait times.
Some insurers are publicizing major successes from their use of chatbots. For example, a property and casualty insurer says its chatbots can help customers secure a home or auto policy in as little as 90 seconds. The technology also shows promise for many other types of insurance, including medical professional liability coverage.
Insurance fraud is a big business in the United States: The Coalition Against Insurance Fraud estimates the total cost of insurance fraud at $308.6 billion per year, which drives up premiums for honest consumers. AI fraud detection is a use case increasing in importance today.
Two common types of insurance fraud are submitting false claims and inflating real claims. With artificial intelligence, insurance companies can detect claims fraud, for example, by automatically validating data provided by a customer against third-party data.
Insurers are turning to AI tools to pinpoint fraud attempts. If an AI tool is trained with data of previous insurance fraud, it can flag suspicious requests for coverage or suspicious claims for further review.
Claims processing is the process of investigating customer’s claims and issuing payment per the policy terms. Insurers are increasingly using AI tools to accelerate claims processing times and pay settlements faster.
Underwriting is the process of deciding whether or not to offer coverage, and if accepted, how much the policy will cost. Insurers use information they gathered during the risk assessment to make underwriting decisions. Increasingly, AI is playing a role in the underwriting process.
Companies like Indigo are using AI systems to underwrite medical malpractice policies faster and more accurately. AI tools are able to analyze large amounts of data to accelerate policy approvals and identify appropriate coverage terms.
Dynamic pricing adjusts premiums based on real-time data about each consumer’s individual risk profile. With AI, it’s possible to accurately price a physician’s risk of facing a medical malpractice claim, based on thousands of data points. That rewards good physicians with larger potential premium savings.
We're written an entire blog about the benefits of AI underwriting for customers of medical malpractice insurance.
AI has the potential to significantly transform the insurance industry for the benefit of both insurance companies and their customers.
As AI continues to advance, insurance companies will likely find more ways to use it to streamline their operations. For example, in the future, many functions could be fully automated, allowing customers to get coverage and file claims through self-service systems.
Like any emerging technology, there are still some challenges with artificial intelligence. Insurers must be aware of the technical limitations of their chosen systems, keep ethical concerns such as data privacy and job redundancy in mind. Ultimately, ensuring that changes create value and enhance the overall customer experience will be crucial to sustainability.
There are many benefits of AI in insurance, from faster policy approvals to more personalized pricing. Indigo uses a proprietary AI-powered process to help physicians get the medical malpractice coverage they need quickly.
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