AI for Insurance: Real-World AI Use Cases Driving Transformation

The insurance industry is undergoing a profound transformation as technological advancements, particularly artificial intelligence (AI), redefine how insurers operate. Faced with rising customer expectations, complex regulatory demands, and the need for greater efficiency, insurers can no longer rely on outdated systems and manual processes. AI is emerging as a key driver of change, enabling insurers to streamline operations, improve decision-making, and unlock valuable insights from vast amounts of data. By automating routine tasks and anticipating risks, AI empowers insurers to not only meet current challenges but also position themselves for future growth. Solutions like Nomad Data’s Document Chat are at the forefront of this shift, providing insurers with the tools they need to harness the power of AI, deliver more accurate results, and reduce costs.

At Nomad Data, we see firsthand how AI-driven technologies are revolutionizing insurance workflows, from claims handling to risk management. In this piece, we explore the most transformative AI use cases in the insurance industry, demonstrating how these innovations are reshaping the sector and helping insurers stay competitive in a rapidly evolving landscape. Nomad Data is working closely with clients across these areas to deliver cutting-edge solutions.

Generative AI for Insurance

Generative AI is a branch of artificial intelligence that focuses on creating new content or data by learning patterns from existing information. Unlike traditional AI, which is primarily used for analyzing and predicting outcomes based on structured data, generative AI can produce original text, images, or even solutions to complex problems. In the insurance sector, generative AI can assist with drafting policy documents, automating customer communications, and even creating tailored insurance products by analyzing customer needs and risk profiles

Now let’s dive into several use cases where Nomad Data is helping insurers increase efficiency:

AI for Underwriting

Underwriting is traditionally a labor-intensive process, requiring underwriters to manually review a mountain of documents, validate information, and assess risk profiles. AI can streamline this entire process. For instance, with Nomad Data’s Document Chat, underwriters no longer need to spend hours on data extraction or validation. The AI agent can automatically pull key details from documents and cross-reference them with external databases, significantly reducing errors and speeding up decisions. Furthermore, the AI can highlight discrepancies that would be otherwise difficult to detect, improving risk assessment accuracy and ensuring better policy decisions.

1. Document Collection and Preprocessing

Collecting necessary documents such as applications, medical records, financial statements, and more can be extremely time consuming. A document-type AI agent can automatically collect, classify, and organize documents submitted by applicants, pulling from email, scanned files, or uploaded portals. AI reduces the time spent manually sorting through documents and ensures that the underwriter has immediate access to a complete file.

2. Data Extraction and Validation

Extracting key data points from documents such as income, health information, or property details, and validating them against existing databases. AI can automatically extract critical information using natural language processing (NLP) and optical character recognition (OCR) technology. It can then validate this information by cross-referencing databases, flagging discrepancies or missing data for human review. This can significantly reduce the amount of human involved work, with the AI quickly drawing human attention to areas of concern or missing data.

3. Risk Assessment and Analysis

Assessing the risks associated with the individual or property by analyzing data such as credit scores, health risks, or claim history. AI can perform risk assessment by analyzing historical claims data, applicant information, and external factors like credit scores or regional risk factors. The AI agent can generate a set of risks or recommendations based on predefined models, allowing underwriters to make data-driven decisions more quickly and accurately. Insurers can define the format of their risk assessments reports to have the AI go out and construct.

4. Automated Decision Support

Making the final decision on whether to approve, reject, or adjust the policy based on risk assessments. AI can provide automated decision support by comparing the applicant’s data with historical underwriting decisions, guiding underwriters toward consistent and data-backed conclusions. For routine cases with clear risk patterns, AI can automate approvals or denials, enabling underwriters to focus on more complex cases.

5. Regulatory Compliance and Audit Trails

Ensuring that all underwriting decisions comply with regulatory requirements and maintaining detailed audit trails for future reviews. AI agents can automatically review documents and decisions to ensure compliance with regulations and internal policies. They can also maintain detailed, time-stamped audit trails of decisions made throughout the underwriting process, simplifying audits and reducing regulatory risks.

Regulatory compliance monitoring & proactive risk mitigation AI use cases

Once a policy is issued, the management of risk doesn’t stop—it's an ongoing process that requires continuous monitoring to ensure that exposures remain in line with the insurer’s risk appetite and that policies stay compliant with evolving regulations. Traditionally, this process has been manual, time-consuming, and often reactive, addressing risks only after they materialize. AI is transforming post-issue risk management by automating the review of existing policies, identifying emerging risks, and ensuring ongoing compliance. With tools like Nomad Data’s Document Chat, insurers can analyze entire portfolios of policies in minutes, spotting potential liabilities, adjusting coverage where needed, and staying ahead of industry changes. These AI-driven solutions allow insurers to take a proactive approach, optimizing risk management while reducing operational costs.

1. Automated Policy Reviews for Unwanted Exposures

Identifying and reviewing policies for exposures that may no longer align with the insurer’s risk appetite or have become undesirable over time. AI can automatically scan and analyze policies post-issue to identify potential exposures that were either missed during underwriting or have emerged due to shifts in market conditions or regulatory changes. By continuously reviewing policy language and coverage areas, AI can flag clauses that increase risk, such as outdated coverage limits, exclusions that no longer apply, or industry-specific risks that were not originally considered. This automated process allows insurers to regularly update their risk assessments, helping prevent costly claims related to previously unnoticed exposures. At Nomad Data we see insurers manually reviewing a handful of policies a year due to the manual nature of the work. With our Document Chat engine insurers are able to review all policies as often as is needed and in a matter of minutes.

2. Portfolio Risk Optimization

Managing and balancing risk across a portfolio of policies to ensure proper distribution of exposures and prevent overconcentration in any one area. AI-driven tools can analyze the insurer’s entire portfolio of written policies to identify patterns of risk concentration, geographic exposure, or sector-specific vulnerabilities. These systems use real-time data to highlight trends that may pose a significant risk to the insurer if left unchecked. For example, AI can detect if too many policies are exposed to natural disasters in a specific region or if certain industries represent an outsized risk. AI enables insurers to take corrective action, such as adjusting coverage terms or acquiring additional reinsurance, ultimately ensuring a balanced and more secure portfolio.

3. Automated Compliance Checks

Ensuring that issued policies remain compliant with changing regulatory requirements and internal guidelines throughout the policy’s lifecycle. AI can automatically review issued policies to ensure ongoing compliance with both external regulatory frameworks and internal underwriting guidelines. As laws and industry standards evolve, AI systems can continuously scan policy language, exclusions, and coverage areas to ensure they meet updated legal requirements. By detecting non-compliant clauses or terms early, insurers can take corrective actions, such as amending policy documents or contacting customers to update their coverage, thereby avoiding regulatory penalties and maintaining adherence to best practices in risk management.

Claims Processing Automation

In claims processing, speed and accuracy are paramount. With Nomad Data’s Document Chat, insurers can automate document intake, quickly cross-check information against historical claims, and instantly flag suspicious patterns for potential fraud. For example, by detecting common fraudulent behaviors, such as repeated language in medical reports, the AI reduces the likelihood of costly payouts. In turn, customers benefit from faster processing times and more accurate assessments, enhancing their overall experience. For insurers, this translates to fewer resources spent on manual claim reviews and a faster, more reliable claims cycle.

1. Automated Claim Intake and Triage

Receiving and prioritizing incoming claims for processing. AI-powered systems can automatically intake claims from various channels—email, phone, or online portals—and classify them based on complexity and urgency. By analyzing claim details, AI can triage simple claims for quick resolution while flagging more complex ones for further investigation. This automation speeds up initial processing, allowing insurers to respond faster to policyholders and prioritize high-impact claims.

2. Document Review and Information Extraction

Reviewing submitted documents such as accident reports, medical bills, and repair estimates. AI can automatically extract key information from claim-related documents. This eliminates the need for manual review, reducing errors and processing time. AI can also cross-reference the extracted data with policy details and historical claims to validate the information. In cases where required documentation is missing or incomplete, AI can flag the issue for further follow-up.

3. Fraud Detection

Identifying potential fraudulent claims for further investigation. AI can analyze patterns in claims data and flag suspicious activity that may indicate fraud, such as highly similar language being used by a doctor across patient medical claims or frequent claims from the same party. By learning from historical claims data and known fraud cases, AI models can continuously improve their accuracy in identifying fraudulent behavior. This proactive approach reduces payouts on fraudulent claims and enhances the overall integrity of the claims process.

4. Automated Claims Adjudication

Determining whether to approve, deny, or adjust the claim amount. AI-driven decision support systems can analyze claims data against policy coverage, historical claim outcomes, and external data sources (e.g., medical or repair cost databases) to automatically adjudicate many claims. For simple claims, AI can automatically approve or deny them based on predefined rules, allowing for faster resolutions. For more complex claims, AI can provide decision support by generating recommendations for human adjusters to review.

Insurance Litigation AI

Insurance litigation can be a highly complex and resource-intensive process, often involving vast amounts of documentation, data analysis, and legal maneuvering. Traditionally, insurers have relied on manual methods to manage discovery, review legal documents, and build case strategies, which can be time-consuming and prone to error. AI is revolutionizing this space by automating the review of discovery materials, predicting case outcomes, and identifying key legal insights faster and with greater accuracy. With AI-powered tools like Nomad Data’s Document Chat, insurers can process and analyze thousands of documents in minutes, navigate complex legal landscapes efficiently, and focus more on strategic decision-making. By leveraging AI, insurers can significantly reduce legal costs, improve litigation outcomes, and ensure compliance with evolving regulations.

1. Efficient Document Discovery and Review

Managing the discovery process, which often involves sifting through thousands of documents, emails, and other records. An AI-powered document agent like that from Nomad Data can process and analyze vast amounts of discovery materials quickly, allowing insurers to navigate through tens of thousands of pages with ease. AI can automatically categorize documents, highlight key sections, and pull out summaries and specific details relevant to the case. This reduces the time spent manually reviewing documents, improves accuracy, and allows legal teams to focus on higher-level case strategies.

2. Automated Case Summarization

Preparing case summaries for legal teams or court submissions. AI tools can read through complex legal documents, extract the most critical information, and generate summaries for quick reference. By identifying the key facts, claims, and defenses, AI ensures that legal teams are well-prepared without having to manually condense the material. This significantly speeds up the preparation process and helps legal teams communicate more effectively with courts, clients, and opposing counsel.

3. Predictive Case Outcomes

Assessing the potential outcome of litigation based on case history and legal precedents. AI can analyze historical litigation data, case law, and court rulings to predict the likely outcome of ongoing cases. Insurers can use these insights to better understand their chances of success, evaluate settlement options, and make informed decisions about whether to litigate or settle. AI’s ability to process vast amounts of data ensures that predictions are based on robust analysis, allowing insurers to develop more effective litigation strategies.

Assessing Risk in Books of Business

When insurers acquire books of business from other carriers, they must quickly assess the associated risks to ensure alignment with their underwriting appetite and strategic goals. This process traditionally involves a time-intensive review of thousands of policies to identify key exposures, coverage gaps, and loss trends. With tools like Nomad Data’s Document Chat, insurers can streamline this process by automatically reading each policy, extracting critical risk factors, and compiling them into a clear, structured format such as a spreadsheet. This allows risk management teams to swiftly evaluate concentrations of risk, identify potentially high-loss policies, and make data-driven decisions about pricing, coverage adjustments, or policy cancellations. By leveraging AI, insurers can transform a traditionally manual and reactive process into a proactive, high-efficiency operation.

Reinsurers and Risk Assessment at Scale

Reinsurers face a similar challenge when considering whether to provide coverage for large books of business transferred by primary insurers. These transactions often involve portfolios containing thousands of policies, each with unique risk characteristics and coverage terms. For reinsurers, quickly analyzing aggregate risk exposure—such as geographic concentrations, catastrophic event exposures, or line-of-business distributions—is crucial to making informed decisions. Nomad Data’s Document Chat can automate the extraction of this data, summarizing key metrics like loss ratios, policy limits, and premium structures into actionable insights. By using AI to identify high-risk clusters or unexpected correlations, reinsurers can better model potential liabilities and negotiate terms with greater confidence. This technology not only accelerates due diligence but also ensures a more granular understanding of portfolio risks, ultimately supporting more accurate pricing and improved risk diversification.

Conclusion

The insurance landscape is changing rapidly. Companies that fail to adopt AI technologies will soon find themselves outpaced by competitors who can process claims faster, reduce costs more effectively, and offer a more seamless customer experience. Nomad Data’s Document Chat not only automates complex workflows but also gives insurers the tools to predict risks and make data-driven decisions. Early adopters of these AI solutions are already reaping the rewards, gaining an edge in an industry where efficiency and agility are becoming critical to long-term success.

Artificial intelligence is no longer a futuristic concept—it’s here, and it’s transforming the insurance industry in real-time. From underwriting to claims handling, policy monitoring, and litigation, AI-driven solutions like Nomad Data’s Document Chat are helping insurers automate their workflows, reduce costs, and deliver better outcomes. The future of insurance lies in data-driven decision-making, and Nomad Data is at the forefront of this transformation. Don’t wait to see how AI can revolutionize your insurance operations. Contact us today to learn more about how our solutions can make your processes faster, smarter, and more efficient.