Generative AI in Insurance

Like most industries today, the insurance sector is experiencing a transformation driven by technological innovation, more specifically generative AI (genAI), that intends to minimize the challenges agents and consumers face in their typical day-to-day. 

Rising customer expectations, regulatory complexities, and the need to enhance operational efficiency have become paramount concerns for C-level executives in a time of economic uncertainty. On top of this, the increased frequency of natural disasters (i.e., hurricane Helene) and evolving risks have made traditional risk assessment models less effective. 

Through the use of genAI, insurance professionals are leveraging more efficient processes and personalized customer experiences. AI-powered tools are being used to automate claims processing, enhance underwriting accuracy, and deliver instant customer service via chatbots. Moreover, this technology is helping insurers analyze vast amounts of data to better predict and assess risk, thus reducing fraud and improving decision-making.

As AI continues to evolve, it has the potential to further optimize insurance workflows, enabling companies to provide more tailored coverage options and improve profitability while keeping up with the dynamic needs of their clients.

Noticing an uptick in mentions of “genAI” within the AlphaSense platform, we dove deeper into the discussions taking place about this iteration of AI. From its proposed applications and the AI technology companies have already released to the shortcomings expert foresee, we’ve compiled the most crucial insights on how this groundbreaking technology is transforming the insurance space.

GenAI Use Cases in the Insurance Industry

Based on an EY study of 200 senior insurance decision-makers, virtually every insurer around the world is embracing genAI—42% are already investing in the technology, while 57% are making plans to invest. Learn how genAI is being leveraged in this industry to promote efficiency, effectiveness, innovation, and much much more. 

Streamline Claims Processing

Claims processing, one of the most routine yet time-consuming tasks for insurance professionals, can now be automated through genAI. AI models can quickly generate claim summaries, detect anomalies, and verify documents, reducing manual effort and speeding up processing times. Natural language processing (NLP) extracts relevant information from claim forms and supporting documents, while machine learning models predict potential fraud and identify valid claims more accurately. 

Generative AI also improves customer experience by automating communication, providing timely updates, and resolving queries faster. Additionally, AI-driven insights help underwriters assess risk effectively. By streamlining workflows and minimizing human intervention, generative AI reduces costs and ensures a faster, more efficient claims process, leading to better customer satisfaction and operational efficiency for insurers.

Enhance Underwriting and Risk Assessment

Agents are often bogged down by a seemingly endless amount of data in nearly every process of their profession, meaning a single task can take hours to complete. Generative AI accelerates this process because it’s able to analyze large volumes of structured and unstructured data much more quickly and accurately than a human can, leading to enhanced risk profiling and decision-making.

For instance, it can process data from medical records, financial statements, social media, and other non-traditional sources to identify patterns and generate insights that improve underwriting accuracy. AI models predict risk more effectively by incorporating historical claims data and using advanced algorithms to assess potential future scenarios. GenAI also supports personalized underwriting by creating tailored risk assessments based on individual customer profiles.

The technology helps underwriters by automating data analysis, identifying key risk factors, and suggesting optimized premium pricing. And potentially most importantly, genAI can detect anomalies and fraudulent activities in real time, ensuring a more robust risk management approach. This leads to faster underwriting processes, improved risk assessment, and better profitability for insurers while providing customers with fairer, data-driven premiums.

Customer Service

GenAI is transforming customer service interactions for the better by providing instant, personalized support through AI-driven chatbots and virtual assistants. These tools handle customer inquiries, assist with claims, and provide policy information 24/7, enhancing accessibility and response times. 

AI models analyze customer data to offer tailored policy recommendations, ensuring a more customized experience while being able to quickly generate necessary documents, thereby reducing wait times for policyholders. The automation of these typically client-agent facing interactions allows professionals to focus on complex issues and provide higher-value support that can ultimately save companies and consumers money. 

According to InnoLead, car insurance firm Jerry saved $4 million last year through its chatbot, “Kelly Bot,” which launched in May 2023. “By June 2023, 100% of inbound messages were responded to within 24 hours, up from just 54% in April 2023. Today, 96% of messages are responded to within 30 seconds. The number of queries requiring human intervention has dropped from 100% to just 11%.”

Policy Generation

GenAI streamlines policy generation by automating the creation of personalized insurance documents, reducing manual workloads and errors. AI models can analyze customer data, such as demographic information and coverage needs, to generate customized policy recommendations and contracts tailored to individual requirements. Natural language generation (NLG) technology enables the creation of readable, precise policy documents in less time compared to traditional methods.

Because genAI can be trained on specific regulatory standards, it can ensure that the content is compliant by incorporating legal language and guidelines automatically. It can also update policies dynamically based on changes in regulations or customer preferences, ensuring policies remain relevant and accurate. Ultimately, genAI significantly reduces administrative burden, shortens the turnaround time, and enhances efficiency—all of which helps insurers provide a seamless and personalized experience to customers. 

Predictive Analytics

Producing predictive analyses is a complex, tedious task that entails reviewing vast datasets to identify patterns, predict future events, and optimize decision-making. However, AI models can analyze historical claims, customer behavior, and external factors like economic trends and weather data to predict risks and assess the likelihood of claims in a matter of seconds. It’s one of the technology’s capabilities that allows insurers to efficiently set more accurate premiums and proactively manage risk.

GenAI also aids in customer retention by predicting potential policy lapses, allowing targeted interventions. It can identify emerging risks, such as shifts in market conditions, and generate insights that help insurers adjust strategies, reduce bias and errors, and provide more precise predictions.

Benefits of Generative AI

There’s truly no limit to how much an insurance company can benefit from genAI. Bain & Company estimate that when applied to workflows, genAI could yield more than $50 billion in annual economic benefits. “The benefits will come through three routes: raising productivity and resizing the workforce, lifting sales through more effective agents and digital advice; and reducing commissions as direct digital channels gain share.” For an individual insurer, genAI could increase revenues by 15% to 20% and reduce costs by 5% to 15%.

Discover a few ways C-Suite executives, agents, and consumers are benefiting from the technology: 

Enhanced Productivity and Efficiency with Workflows

Quite simply, genAI automates repetitive tasks, streamlines communication, and provides data-driven insights. The technology these AI models leverage can handle routine processes like data extraction, claims validation, and document generation, significantly reducing manual workloads and minimizing human errors. This automation speeds up key workflows, such as underwriting, claims processing, and policy generation, allowing insurance professionals to focus on higher-value tasks.

Additionally, AI-driven workflow automation improves collaboration between departments by simplifying information sharing and minimizing bottlenecks. With faster processing times, fewer errors, and more efficient resource allocation, generative AI helps insurers boost productivity, reduce operational costs, and deliver a better overall experience to both customers and employees.

Risk Mitigation and Managing Compliance

One of the main three reasons EY says insurers are adopting genAI is to “manage compliance and mitigate risks, which are key concerns in this highly regulated industry. Automated compliance monitoring, fraud detection, and even generating content in the form of training materials and interactive modules for staff to stay current on the latest regulations are areas that companies are starting to explore.”

Through analyzing vast datasets to identify potential risks and fraudulent activities, genAI enables agents to take proactive measures. It conducts automated regulatory checks that ensure all policies and processes meet compliance standards, reducing the risk of penalties. Further, AI-driven insights help insurers assess risks accurately and adjust strategies in real-time.

Cost and Time Savings

By optimizing workflows, reducing manual labor, and enabling more accurate risk management, genAI promotes productivity, decreases costs, and improves insurers’ overall profitability.

According to an EY survey, insurers anticipate productivity enhancements, revenue uplift and cost savings as the primary returns on genAI investments. More specifically, “82% of large insurers (with more than $25 billion in direct premiums written) cite productivity gains as a primary driver for implementing genAI. Additionally, 65% of all insurers expect a revenue uplift of more than 10%, and 52% of respondents anticipate cost savings of 11-20%.”

Risks and Drawbacks of Generative AI

Proponents have lauded the potential applications of genAI as a way to solve some of the world’s biggest problems—from unemployment, creditworthiness, and even criminal justice. Yet, due to genAI’s quick overtake, there’s virtually no federal government oversight over how public or private companies wield this technology. It’s a gray area that’s left some C-Suite executives to question what genAI’s shortcomings are—and more importantly, how this iteration of AI could backfire. 

Data Privacy and Security

GenAI poses risks for insurance companies due to its reliance on vast amounts of sensitive customer information. Most AI models require access to personal and financial data, increasing the risk of data breaches if security measures are inadequate. Additionally, accidental data exposure during model training and the potential for unauthorized use of personal data pose significant privacy challenges.

GenAI may also inadvertently generate outputs containing confidential information, leading to compliance issues with data protection regulations like GDPR or HIPAA. Insurers must therefore make sure that data encryption, secure storage, and strict access controls are in place, along with adherence to privacy guidelines, to mitigate these risks.

Bias and Discrimination

When it comes to model training, if historical data contains biases—such as those based on race, gender, or socioeconomic status—genAI may replicate or amplify these biases in its decision-making processes. This can lead to unfair treatment in underwriting, pricing, or claims processing, where certain groups may be disadvantaged based on flawed algorithms.

Therefore, genAI can also unintentionally produce outputs that reflect societal biases, potentially leading to discriminatory practices. If not carefully monitored, these biases can result in compliance issues, reputational damage, and legal consequences for insurers. To mitigate these risks, companies must implement rigorous bias detection methods, regularly audit AI systems, and ensure diverse and representative datasets are used in training to promote fairness and equity in operations.

Regulatory Compliance

The complexity of AI models may lead to non-transparency, making it challenging for insurers to explain decisions related to underwriting or claims, potentially violating regulatory requirements for accountability—especially for outputs that do not align with industry regulations, such as GDPR, HIPAA, or other data protection laws. 

To that end, genAI may also use customer data in ways that are not explicitly authorized, leading to breaches of privacy regulations. Failure to properly manage and audit AI models could also result in the use of outdated or biased data, leading to non-compliance. 

Future Outlook for Generative AI in Insurance

In an era of economic uncertainty and heightened competition, C-level executives are increasingly focused on meeting rising customer expectations, navigating regulatory complexities, and boosting operational efficiency. In tandem with these challenges comes a solution: generative AI, a new iteration of AI that has already revolutionized nearly every industry. 

This technology is helping insurance professionals implement more streamlined processes and create personalized customer experiences. GenAI-driven tools are automating claims processing, improving underwriting precision, and providing faster, better customer support through chatbots. Furthermore, genAI allows insurers to analyze extensive datasets for improved risk prediction, fraud detection, and decision-making.

However, adopting generative AI does come with certain risks. The dynamic nature of AI and frequent regulatory changes, then, means that insurance professionals must continuously monitor and adapt AI processes to meet compliance standards, ensuring transparency, accountability, and data protection. 

Stay Ahead of GenAI Developments in Your Industry With AlphaSense

In a volatile market that’s producing new developments around genAI every day, it’s challenging to decipher what technology, product, or insight could revolutionize the insurance industry next. Cutting through the noise to find insights is nearly impossible in the age of information overload. You need a tool that does all of the heavy lifting, so you can focus on leveraging information rather than searching for it. 

AlphaSense is a leading provider of market intelligence, including 10,000+ high-quality content sources from more than 1,500 leading research providers—all in a single platform. Analysts, researchers, and decision-makers in the insurance sector can access exclusive research reports only found elsewhere in disparate locations and often behind expensive paywalls. With AlphaSense, companies can conduct comprehensive research that gives them a competitive edge with smarter, more confident decision-making.

Specific types of content you’ll find on the AlphaSense platform include:

• News, industry reports, company reports, 510(k) filings, and regulatory content
• Over 1,500 research providers including Wall Street Insights®, a premier and exclusive equity research collection for corporate teams
• Expert call transcript library that gives access to thousands of insightful interviews with professionals, customers, competitors, and industry experts

The AlphaSense platform also delivers unmatched AI search capabilities and features for analyzing qualitative and quantitative research, and can mine unstructured data for the most critical insights. These features include:

• Automated and customizable alerts for tracking regulatory filings, companies, industries, and potential investments
• Table export tools that support M&A workflows like target lists and due diligence
• Smart Synonyms™ technology that ensures you never miss a source important to your research
• Sentiment analysis goes beyond the semantic meaning of what an executive leader, news source, or customer is saying. It does this by assigning a sentiment score that shows how positive, neutral, or negative the sentiment is underneath the statement 
• Smart Summaries, our first generative AI feature, summarizes key insights from earnings calls, expert calls, and more for faster and better analysis

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