Artificial intelligence has taken over the news cycle, driving the line of tools revolutionizing enterprises across industries. In fact, 36 percent of those in business, legal, and professional services are already using AI regularly for work, personal reasons, or both, according to the most recent annual McKinsey Global Survey.

AI is becoming a mainstay in the zeitgeist of necessary technology, and the insurance industry is no exception. Let’s look at how AI will impact your experience with your insurance company.

Pros and Cons of AI in Insurance

Pros: Efficiency, accuracy, and tailored coverage are the leading improvements AI implementation can bring. Reduced processing times and faster responses will become the norm. And the data within large language models will allow risk assessments to be much sharper.

Cons: Privacy and bias are some of the main concerns. Evaluating the data of customers means significant privacy implications for those customers. And inaccurate or biased training data can lead to skewed outcomes. This is where it is important for human underwriters or brokers to double-check the work of AI.

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How Will AI Modernize Risk Assessment?

The insurance industry’s current data orchestration methods are time-consuming and prone to errors. Insurance is an old industry; finding ways to safely employ AI can modernize the purchasing experience and fast-track the entirety of the submission, quote, bind and policy issuance process.

AI can help companies assess risk more efficiently and accurately, as well as potentially transform the insurance underwriting process from data mastery to fraudulent claims and enhanced risk profiling. It’s able to scrutinize a broader array of factors than humans, resulting in precise and personalized risk assessments.

Let’s use cyber insurance as an example.

When you apply for coverage, the insurance carrier — or the company selling you insurance — could use an AI-driven tool that continuously scans and analyzes your digital infrastructure. This tool could identify vulnerabilities, outdated software, and other potential entry points for cyber threats. It could also evaluate your response time to known vulnerabilities, offering insights into your cybersecurity hygiene and patch management practices.

Additionally, by monitoring network traffic and user behavior, AI could identify patterns that deviate from the norm and help detect potential insider threats or compromised accounts that might not be caught through standard vulnerability scans.


Can AI Determine Your Insurance Premium?

Threat intelligence integration could be another key component of AI underwriting, meaning the insurance carrier’s AI model would integrate with global threat intelligence databases accessing vast amounts of data. This would allow the underwriter to assess how potential and emerging threats might specifically impact a prospective policyholder, considering their size, security posture and industry segment.

AI may also consider predictive modeling by using historical claim data and real-time data. With this information, the AI models could predict your likelihood of a cybersecurity incident in the coming policy period. This predictive analysis would aid the underwriter in determining the appropriate premium, coverage and policy limits.

Beyond evaluating risk, AI can provide you with actionable recommendations to mitigate detected vulnerabilities, helping you improve your overall cybersecurity posture. These recommendations can lead to premium discounts, aligning incentives for both you and your insurer.


How Will AI Fight Insurance Fraud?

AI can analyze large amounts of claims data in a short amount of time, identifying patterns and anomalies that can indicate insurance fraud, which costs the U.S. more than $308 billion annually. Basically, once AI is trained, it can assess new claims for insurance companies, analyzing the details of the claim and identifying patterns that might be out of the ordinary. From there, AI can generate a risk score for humans to determine the likelihood of the claim being fraud.

Let’s explore this in the following example about fraudulent auto insurance claims.

If an auto insurer receives a new claim, AI will analyze the details submitted, cross-referencing the damage described, the stated location of the incident, the time, and other relevant factors. AI will then check for inconsistencies in the story while assessing a claimant’s history.

This is where AI really excels. If the claimant has filed similar claims in the past or has a high frequency of claims in a short period, AI will note this as a red flag. The system also compares the claim patterns against known fraud patterns or commonalities seen in previously confirmed fraudulent claims.

What’s more, if a claimant uploads photos of the damage, image recognition algorithms can assess it. For example, if someone submits a claim for a dent caused by another vehicle, but AI identifies patterns more consistent with intentional damage (like hammer blows), it triggers an alert. AI can also analyze footage from available surveillance cameras to validate the claimant’s story. Moreover, if the claimant’s car has a connected GPS system, AI can analyze the GPS’ data to confirm the vehicle’s location at the time of the alleged incident, ensuring it matches the claim.

Should AI detect inconsistencies or need further clarification, it can send automated messages to the claimant, requesting more information or directing them to provide further evidence.

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How Can We Incorporate AI Into an Old Industry?

No one is immune to the risks of implementing AI, including insurance companies. Embracing responsible AI practices goes beyond just minimizing risks: it unlocks new opportunities. A deeper integration of AI in insurance will necessitate robust regulatory frameworks to ensure fairness, nondiscriminatory practices and data privacy.

The road to successful AI integration within the insurance industry also entails transparency with stakeholders, customers and employees. Organizations must be open about their AI practices, ensuring that people understand how it is being used. Building trust in AI technologies is a shared responsibility. As the AI journey unfolds, we can mitigate the new risks it poses with responsible integration, paving the way for innovation like never before.

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