Generative AI in Insurance: Top 5 Use Cases

As insurers begin to adopt this technology, they must do so with a focus on manageable use cases. Because its algorithms are designed to enable learning from data input, generative AI can produce original content, such as images, text and even music, that is sometimes indistinguishable from content created by people. Appian partner EXL is actively working to explore the vast potential of generative AI and help insurers unlock the full power of this technology within the Appian Platform. Our practical guide for insurance executives to help separate hype from reality, including Web3 insurance opportunities and risk considerations. Insights from senior business leaders and CEOs strengthen our philosophy of what it takes for businesses to transform successfully in today’s market. This was driven by a combination of ease of access to consumer solutions (such as OpenAI’s ChatGPT or Google’s Bard), worldwide media coverage, and the promise of near-instant benefits (however real).

GAI’s implementation for threat review and pricing significantly enhances the accuracy and fairness of these processes. By integrating deep learning, the technology scrutinizes more than just basic demographics. It assesses complex patterns in behavior and lifestyle, creating a sophisticated profile for each user. Such a method identifies potential high-risk clients and rewards low-risk ones with better rates. Generative adversarial networks and virtual assistants can provide immediate assistance to customers 24/7. They can answer queries, provide information about policies, and guide customers through the claims process, resulting in faster response times and improved accessibility.

Traditional AI models can seem like “black boxes,” leaving professionals perplexed. GenAI addresses this by providing interactive decision support, explaining results in plain language, and even engaging in conversations. GenAI helps users comprehend the reasoning behind the model’s conclusions, playing an important role in establishing trust and accountability, essential in the insurance industry. At Allianz Commercial, Generative AI also plays a multifaceted role in enhancing customer service and operational efficiency. They use intelligent assistants to answer user queries about risk appetite and underwriting. These bots are available 24/7, operate in multiple languages, and function across various channels.

Nearly 40 percent of them are considered endangered, meaning they have a declining number of speakers and are at risk of dying out. Some languages are spoken by fewer than 1,000 people, while more than half of the world’s population uses one of just 23 tongues.”[1] Now, with the rise of ChatGPT and generative AI, further advancements will be made. Innovative insurance leaders who quickly adopt generative AI technologies will gain a significant competitive advantage over their slower peers. The maximum occupancy is high at 1000 persons, and it is located in a shopping complex.

II. Training Bias in AI

For policyholders, this means premiums are no longer a one-size-fits-all solution but reflect their unique cases. Generative AI shifts the industry from generalized to individual-focused risk assessment. The targeted and unbiased approach is a testament to the customer-centricity in the sector.

Additionally, Gen AI is employed to summarize key exposures and generate content using cited sources and databases. Artificial Intelligence-powered systems can provide real-time tracking of the claims process, offering transparency and peace of mind to policyholders. Similarly, Generative AI can address existing challenges within the field of service management. Field service management tools augmented with Gen AI can help insurers calculate losses precisely and speed up claims processing. Insurance is one such sector that has been slow in embracing process transformation widely to restructure traditional practices and create new possibilities.

The big win often involves combining multiple AI technologies to address different aspects of a project, such as semantic searching or language capabilities. Whereas building detail insights expose what is truly being insured, location detail insights show the context in which the building operates. In the case of the restaurant chain for example, it did not have its own hurricane protection units but according to the detailed geo-location data, the building is located approximately 3 miles away from the closest fire station. This in turn allows underwriters to identify and follow up on leakage drivers from insights and context gathering to recommend risk mitigation actions more effectively.

As the insurance industry grows increasingly competitive and consumer expectations rise, companies are embracing new technologies to stay ahead. Emerging technologies such as Generative AI are advancing at a rapid pace, and insurers may struggle to keep up with these developments. New and complex Gen AI systems might not fit precisely into existing regulatory frameworks. In many cases, insurance firms may not have established clear guidelines or standards for Gen AI-powered systems.

Equally important is the need to ensure that these AI systems are transparent and user-friendly, fostering a comfortable transition while maintaining security and compliance for all clients. However, its impact is not limited to the USA alone; other countries, such as Canada and India, are also equipping their companies with AI technology. For instance, Niva Bupa, one of the largest stand-alone health insurance companies in India, has invested heavily in AI. More than 50% of their policies are now issued with zero human intervention, entirely digitally, and about 90% of renewals are also processed digitally. Generative AI is rapidly transforming the US insurance industry by offering a multitude of applications that enhance efficiency, operations, and customer experience. Generative AI can generate examples of fraudulent and non-fraudulent claims which can be used to train machine learning models to detect fraud.

One of the most potential advancements for insurers is the incorporation of newer and smarter technologies, especially Generative AI. It refers to a class of Artificial Intelligence systems that are designed to produce content, often in the form of text, images, audio, or other data types. In short, deep learning models are capable of creating new data that is similar to existing data from a range of sources. The world of artificial intelligence (AI) continues to evolve rapidly, and generative AI in particular has sparked universal interest.

Risks and human oversight

Generative AI can build predictive models that take into account a wide range of variables from applicants’ documents to determine the risk. These models can assess factors like age, health history, occupation, and more, providing a comprehensive view of the applicant’s risk. Automated underwriting powered by Generative AI models can make risk calculations and decisions much faster than traditional processes. This is especially valuable for complex insurance products where the risk assessment is relatively straightforward. On the whole, Chat PG underwriting ensures that decisions are made consistently while reducing bias or human errors. A key concern in AI adoption is the concept of “explainability” or the system’s ability to explain how it makes decisions.

• Gen AI has the potential to reshape the insurance value chain, enhancing productivity and delivering increased customer satisfaction.
• Whereas building detail insights expose what is truly being insured, location detail insights show the context in which the building operates.
• Generative AI automates routine insurance tasks, enhancing efficiency and accuracy.
• By analyzing customer data and predicting behavior, insurers strive to exceed customer expectations, improve satisfaction and drive up retention.

Synthesizing a submission package with third party data in this way allows it to be presented in a meaningful, easy-to-consume way that ultimately aids decision-making. These can all enable faster, improved pricing and risk mitigation recommendations. Augmenting the information received from the broker with third party data also eliminates the long lag times caused by today’s back and forth between underwriters and brokers. This can be happening immediately to every submission concurrently, prioritizing within seconds across the entire portfolio.

For more, check out our article on the 5 technologies improving fraud detection in insurance. In this article, we will explain 9 potential use cases of generative AI in insurance and talk about its own challenges that can be problematic in the insurance sector. EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients. Discover how EY insights and services are helping to reframe the future of your industry.

“What GenAI is going to allow us to do is create these Digital Minions with far less effort,” says Paolo Cuomo. “Digital Minions” are the silent heroes of the insurance world because they excel at automating mundane tasks. By swiftly reviewing vast amounts of data, Digital Minions allow professionals to focus on their core competencies, such as customer relationships and make more informed risk-based decisions. Many insurers are training staff to improve their work and summarize key tasks through user-friendly tools. This includes checking and updating policies in a part of the business that doesn’t touch customers directly. First movers are well underway with the testing phase, putting GenAI to work on everyday operational tasks.

Five Generative AI Patterns

This not only helps ensure the legitimacy of claims but also aids in maintaining the integrity of the claims process. Typically, underwriters must comb through massive amounts of paperwork to iron out policy terms and make an informed decision about whether to underwrite an insurance policy at all. The technology could also be used to create simulations of various scenarios and identify potential claims before they occur. This could allow companies to take proactive steps to deter and mitigate negative outcomes for insured people.

Early pilots may require guardrails that reduce — or even counter — expected productivity gains in limited settings. Yet, persevering through short-term challenges may be crucial to gain a first-mover advantage and achieve long term success. Digital solutions enable client acquisition, customer identification, and Segment-Based Retention strategies with Acquisition and Churn Analytics. Synthetic profiles aid in segmentation and personalized marketing, adhering to privacy regulations. You can foun additiona information about ai customer service and artificial intelligence and NLP. “Essentially, machines have infinite memory, and while AI is not yet at the point where it can analyze as well as a human, it can prompt and nudge human beings.” Cybercriminals are already one step ahead, leveraging the technology to write malicious code and perpetrate deepfake attacks, taking social engineering and business email compromise (BEC) tactics to a new level of sophistication.

These simulations can be used to train predictive models to better estimate risk and set insurance premiums. The Appian AI Process Platform includes everything you need to design, automate, and optimize even the most complex processes, from start to finish. The world’s most innovative organizations trust Appian to improve their workflows, unify data, and optimize operations—resulting in better growth and superior customer experiences.

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DTTL and each DTTL member firm and related entity is liable only for its own acts and omissions, and not those of each other. Embracing AI isn’t a bold move; it’s a necessary step towards the future of work in the insurance industry. And it requires significant behavior and mindset shifts for successful, sustainable transformation.

AI is poised to revolutionize consumer experiences and reshape the narrative of insurance itself. Those who embrace this change will not only elevate the CX but also lead the industry into a new epoch. Such hyper-personalization goes beyond convenience, building trust and loyalty among customers. Insurers, by showing a deep understanding of individual needs, strengthen their relationships with the audience. Additionally, artificial intelligence’s role extends to learning platforms, where it identifies specific knowledge gaps among agents.

There were warnings of inherent bias in some large language models (LLMs) and the risk of “hallucinations” — false results — being accepted as truth. Indeed, MetLife’s AI excels in detecting customer emotions and frustrations during calls. Such an approach is particularly impactful in sensitive discussions about life insurance, where understanding and addressing buyer concerns promptly is vital. Anthem’s use of the data is multifaceted, targeting fraudulent claims and health record anomalies.

Also, these generated synthetic datasets can mimic the properties of original data without containing any personally identifiable information, thereby helping to maintain customer privacy. Cem’s work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited (“DTTL”), its global network of member firms, and their related entities (collectively, the “Deloitte organization”). DTTL (also referred to as “Deloitte Global”) and each of its member firms and related entities are legally separate and independent entities, which cannot obligate or bind each other in respect of third parties.

The development of our language has paved the way for some of civilization’s most significant milestones. From the ancient Egyptians with their pyramids to the Romans with their aqueducts and our modern space program—none of this would have been possible without words. Yet, the intricacy of our linguistic heritage is more fragile than we might realize. According to an article in Scientific American, “Scientists are aware of more than 7,100 languages in use today.

It then delivers targeted training, enhancing employee expertise and ensuring compliance. Our team diligently tests Gen AI systems for vulnerabilities to maintain compliance with industry standards. We also provide detailed documentation on their operations, enhancing transparency across business processes. Coupled with our training and technical support, we strive to ensure the secure and responsible use of the technology. Software powered by the transformative technology can be employed by insurers to automate underwriting, determine appropriate coverage and premiums, and generate simplified summaries or explanations of policies. Similarly, Generative Artificial Intelligence in insurance helps customers analyze and understand complex insurance policies, making it easier for them to comprehend the terms and conditions.

This advanced approach, integrating real-time data from sources like health wearables, keeps insurers abreast of evolving trends. The Generative AI’s self-learning capability guarantees continuous improvement in predictive accuracy. This also gives them a competitive edge in the market, as the providers of fair and financially viable policies. Besides the benefits, implementing Generative AI comes with risks that businesses should be aware of.

It analyzes customer data, instantly identifying patterns indicative of legitimate or fraudulent cases. This rapid analysis reduces the time between submission and resolution, which is especially crucial in health-related situations. It actively identifies risk patterns and subtle anomalies, providing a comprehensive overview often missed in manual underwriting. This way companies mitigate risks more effectively, enhancing their economic stability. Artificial intelligence adoption has also expedited the process, ensuring swift policy approvals.

For instance, after an accident, a customer may upload the details and pictures of the damaged vehicle. A generative model trained on similar data can evaluate the damage, estimate the repair costs, and hence help in determining the claim amount. The models can also generate appropriate responses to customer queries about the status or details of their claim, making communication more straightforward and efficient.

” to “What can I do with generative AI that is impactful, and how soon can this impact be delivered? Centralized data ensures accuracy, consistency, and compliance, optimizing operations and enhancing decision-making processes. Within personal lines, AI is already well underway in being leveraged to streamline operational models and enhance customer interactions across multiple channels.

Appian is your gateway to the productivity revolution, helping you operationalize AI across your organization and streamline end-to-end processes. In the series’ upcoming articles, we will explore questions around business value creation and new ways of working. We’ll help you unlock the power of generative AI, and take a deep dive into specific use cases and actions for your organization.

A notable example is United Healthcare’s legal challenges over its AI algorithm used in claim determinations. They were accused of using the technology which overrode medical professionals’ decisions. That’s why, insurers must obtain informed consent from policyholders and customers for collecting, storing, and processing their data.

In the long term, they plan to employ Gen AI for more personalized care and timely medical interventions. This AI-enhanced assistant efficiently handles queries about insurance and pensions. Bot’s integration of Generative AI improves accuracy and accessibility in consumer interactions. Such an enhancement is a key step in Helvetia’s strategy to improve digital communication and make access to product data more convenient.

As businesses begin to figure out how to integrate generative AI into their business processes, five key patterns have emerged that delineate their broad spectrum of capabilities. Our thought leadership for insurance leaders to drive new business growth and reinvent insurance solutions for customers. Generative AI is creating new operational efficiencies and solutions to transform the insurance business model. Earlier this year, we explored the fundamentals of generative AI and the impact it may have in the insurance industry, as we saw many insurers experimenting with its potential. We are now seeing industry discussions progressively shifting away from “What is generative AI?

These tools are designed to constructively challenge underwriters, claims managers and brokers, offering alternative routes to consider. While the ultimate decision remains in the hands of the professional, Digital Sherpas provide important nudges along the way by offering relevant insights to guide the overall decision-making process. With the ability to review vast amounts of data in a significantly shorter time, AI tools will continue to offer an efficient and cost-effective solution for fraud detection. It will save insurers valuable time and resources while enhancing their capabilities in the fight against fraud.

Ensuring the reliability and accuracy of the generated data or predictions is a significant challenge. Driving business results with generative AI requires a well-considered strategy and close collaboration between cross-disciplinary teams. Appian empowers you to protect your data with private AI and provides more than just a one-off, siloed implementation.

Getting Started with Gen AI in Insurance: Benefits and Use Cases

Generative Artificial Intelligence (AI) emerges as a promising solution, capable of not only streamlining operations but also innovating personalized services, despite its potential challenges in implementation. Models such as GPT 3.5 and GPT 4 gen ai in insurance present opportunities to radically improve insurance operations. They have the potential to automate processes, enhance customer experiences and streamline claims management, ultimately driving efficiency and effectiveness across the industry.

• Many customers want to speak to a professional claims handler in their time of need.
• Generative AI can generate examples of fraudulent and non-fraudulent claims which can be used to train machine learning models to detect fraud.
• This includes tailoring marketing messages, policy information, and customer service interactions to individual customers, making them feel valued and understood.
• Generative AI models require high-quality, diverse, and comprehensive data to make accurate predictions.

Such technologies revolutionize medical policy event management, making it faster, more accurate, and user-friendly. Furthermore, with Generative AI in health, insurers offer dynamic, client-centric help, boosting the overall experience. Generative AI identifies nuanced preferences and behaviors of the insured from complex data. It predicts evolving market trends, aiding in strategic insurance product development.

To determine how likely it is a prospective customer will file a claim, insurance companies run risk assessments on them. By understanding someone’s potential risk profile, insurance companies can make more informed decisions about whether to offer someone coverage and at what price. Generative AI models can generate thousands of potential scenarios from historical trends and data.

Generative AI can analyze customer data and market trends to provide customers with personalized communications. This includes tailoring marketing messages, policy information, and customer service interactions to individual customers, making them feel valued and understood. This pioneering technology has the potential to redefine the way insurance processes are organized, offering enhancements in efficiency, precision, and user experience. It enables insurers to harness the power of data and automation and launch more innovative product offerings.

Implementing generative AI in insurance for customer service operations can increase customer satisfaction due to fast and 24/7 support, together with cost savings. Industry-specific language-trained LLMs redefine customer service, streamline insurance operations, ensure compliance, cut costs, and foster innovation, catalyzing digital transformation in the insurance realm. Ultimately, the hope is that AI technology will free up insurance and claims professionals to focus on making more informed risk-based decisions and building relationships with customers. For now, far from replacing the underwriter, GenAI will instead be fine-tuned to offer prompts and suggestions that will ultimately lead to better risk selection and more profitable outcomes. By leveraging AI, insurers enhance their fraud-detection capabilities, proactively identify suspicious behavior, reduce financial loss and ultimately protect genuine customers.

If you’re an insurance company looking to leverage AI for insurance, you’ve come to the right place. At Aisera, we’ve created tools tailored to enterprises, including insurance companies. We offer products such as virtual assistants, personalized policy recommendations, claims automation, dynamic forms, workflow automation, streamlined onboarding, live AI agent assistance, and more. For one, it can be trained on demographic data to better predict and assess potential risks. For example, there may be public health datasets that show what percentage of people need medical treatment at different ages and for different genders. Generative AI trained on this information could help insurance companies know whether or not to cover somebody.

As insurance firms navigate this tech-driven landscape, understanding and integrating Generative AI becomes imperative. The insurance industry, on the other hand, presents unique sector-specific—and highly sustainable—value-creation opportunities, referred to as “vertical” use cases. These opportunities require deep domain knowledge, contextual understanding, expertise, and the potential need to fine-tune existing models or invest in building special purpose models.

This is certainly the case for the insurance industry, where generative AI is fundamentally reshaping everything from underwriting and risk assessment to claims processing and customer service. This combination streamlines insurance underwriting and claims processes, enabling insurers to make better decisions about risk, increasing policy pricing accuracy and enhancing claims outcomes. The global market for artificial intelligence (AI) in insurance https://chat.openai.com/ is predicted to reach nearly $80 billion by 2032, according to Precedence Research. This growth is being driven by the increased adoption of AI within insurance companies, enhancing their operational efficiency, risk management, and customer engagement. In the underwriting process, smart tools are embedded to assess and price risks with greater accuracy. For instance, GAI facilitates immediate routing of requests to partner repair shops.

What an underwriter might do over the course of a week could be done instantaneously and consistently while making informed, structured recommendations. The underwriter will immediately know control gaps based on submission details and where significant deficiencies / gaps may exist that could impact loss potential and technical pricing. Of course, these must then be considered in concert with each insured’s individual risk-taking appetite. These improvements ultimately create the ability to write more risks without excessive premiums; to say yes when you might otherwise have said no.

Now, everyone, as long as they have an internet connection, can generate more words, images, computer code, and music. This supplementary information is invaluable in calculating the real risk exposure and attributing the correct risk level to the customer’s situation. Our perspectives on taking a CustomerFirst approach—realigning corporate strategy with investments that are deeply tied to customers’ needs. Many enterprise solutions remain primarily focused on experimentation-type use cases, with major compliance, privacy and technology considerations — among others — yet to be resolved.

Similar to most technology disruptions, many technology players of all sizes and capabilities are rapidly announcing new generative AI solutions aimed at enterprise use cases for insurers. AI enables tailored offerings, efficient claims processing, and responsive support, elevating customer satisfaction and retention rates, fostering long-term loyalty and positive brand perception. Generative AI presents insurance organizations with an unprecedented opportunity to boost profitability, efficiency, and operational intelligence. Through strategic investments, insurers can unlock growth potential, drive cost savings, and enhance operational effectiveness for sustained competitiveness and success. In many ways, the ability to use GenAI to speed up processes is nothing new; it’s just the latest iterative shift towards more data- and analytics-based decisions. And it can make these digital transformations simpler and more straightforward for the technophobes.

Get the guide to driving responsible generative AI adoption in the insurance industry.