Its for Real: Generative AI Takes Hold in Insurance Distribution Bain & Company

LeewayHertz prioritizes ethical considerations related to data privacy, transparency, and bias mitigation when implementing generative AI in insurance applications. We adhere to industry best practices to ensure fair and responsible use of AI technologies. Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee («DTTL»), its network of member firms, and their related entities. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the «Deloitte» name in the United States and their respective affiliates. Certain services may not be available to attest clients under the rules and regulations of public accounting. Chubb CEO Evan Greenberg was the latest to convey a sober stance on the impact of AI on insurance, even as he confirmed Chubb is looking to scale its use of the technology claims over the next two to three years.

Generative AI for insurance can be considered a kind of generative disruption for insurers in the sense that it can open new clients, new optimized processes, and new product needs. Massive amounts of data are analyzed with the assistance of complex formulae and can provide insurance companies with the ability to automate tens of thousands of processes and erroneous determinations. CreateInsurance marketing teams have to perform the balancing act of creating content that follows strict compliance rules but also appeals to their target audience. Plus, editing complex content to fit individual needs can take up a lot of time and resources from high-value projects.

Since our founding in 1973, we have measured our success by the success of our clients, and we proudly maintain the highest level of client advocacy in the industry. It may come as no surprise that generative AI could have significant implications for the insurance industry. Insurance companies are reducing cost and providing better customer experience by using automation, digitizing the business and encouraging customers to use self-service channels. Large, well-established insurance companies have a reputation of being very conservative in their decision making, and they have been slow to adopt new technologies. They would rather be “fast followers” than leaders, even when presented with a compelling business case. This fear of the unknown can result in failed projects that negatively impact customer service and lead to losses.

AnalyzeInsurance marketing teams must analyze vast amounts of data to increase efficiency and make informed decisions. Generative AI can help alleviate this burden by providing powerful insights and identifying new opportunities. AI-driven tools can be used to uncover trends in customer behavior and marketing performance to guide future strategies. At LeewayHertz, we craft tailored AI solutions that cater to the unique requirements of insurance companies. We provide strategic AI/ML consulting that enables insurers to harness AI for enhanced risk assessment, improved customer engagement, and optimized policy management.

GenAI is poised to reshape the landscape of the insurance industry, offering transformative possibilities for technology suppliers and SPs. One of the key considerations for navigating this evolving terrain is a nuanced understanding of data dynamics. GenAI’s effectiveness hinges on the ability of technology providers to navigate the balance between structured and unstructured data within the insurance domain, ensuring seamless handling of both for optimal performance. Customization tailored to specific insurance processes is emphasized, from underwriting to claims processing, as the linchpin for enhancing efficiency and accuracy.

By analyzing vast datasets, it enhances fraud detection capabilities, safeguarding insurers from potential financial losses and maintaining their credibility. While AI’s role in underwriting is expanding, the replacement of human underwriters is a gradual process. Generative AI complements human underwriters by providing valuable insights and data-driven assessments, enabling insurers are insurance coverage clients prepared for generative ai? to offer tailored insurance plans that precisely meet customers’ needs. These are notable given the imperative for tech modernization and digitalization and that many insurance companies are still dealing with legacy systems. Yes, Generative AI can process unstructured data for insurance claims with natural language processing to get valuable insights for smooth claim handling.

This is your go-to place for learning how to use AI for insurance and the advantages you can gain from doing so. It’s a guide to help get the ball rolling on your AI-related initiatives and to figure out the right requirements for a successful AI platform. The first step in realizing such transformational benefits is identifying high-value use cases that’ll have the quickest, largest impact on your company. The global market size for generative AI in the insurance sector is set for remarkable expansion, with projections showing growth from USD 346.3 million in 2022 to a substantial USD 5,543.1 million by 2032. This substantial increase reflects a robust growth rate of 32.9% from 2023 to 2032, as reported by Market.Biz. “We recommend our insurance clients to start with the employee-facing work, then go to representative-facing work, and then proceed with customer-facing work,” said Bhalla.

The better approach to driving business value is to reimagine domains and explore all the potential actions within each domain that can collectively drive meaningful change in the way work is accomplished. As with any nascent technology, new risks are emerging in areas such as hallucination, data provenance, misinformation, toxicity, and intellectual property ownership. To manage risks, insurers should adopt a responsible AI strategy that relies on successive waves of use cases, testing and learning as they go (see Figure 2). The power of GenAI and related technologies is, despite the many and potentially severe risks they present, simply too great for insurers to ignore. To take advantage of the possibilities, senior leaders must develop bold and creative adoption strategies and plans to drive breakthrough innovation. A strong risk-based approach to adoption, with cross-functional governance, and ensuring that the right talent is in the right role, is critical to driving the outcomes and the ROI insurers are looking for.

Another way Generative AI could help with risk assessment is by aiding coders in creating statistical models. This ability can speed up the programming work, requiring companies to hire fewer software programmers overall. 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. Insurance brokers play a vital part in connecting clients with suitable insurance providers to the satisfaction of both parties.

GenAI automates every step in this journey, significantly reducing settlement times and enhancing customer experiences. Generative AI accelerates claims processing by automating data extraction and validation. For instance, it can streamline the assessment and settlement of property insurance claims following natural disasters, ensuring faster and more accurate claim resolutions. Choose Generative AI models based on the specific requirements of your identified use cases. For instance, consider using Variational Autoencoders (VAEs) for generating personalized marketing materials or Generative Adversarial Networks (GANs) for simulating risk scenarios. Evaluate the models based on factors like scalability, interpretability, and their capacity to handle the diversity of insurance data.

Human Capital Analytics

Bearing in mind that the legislative framework for it has not yet been fully established, it may be hard for insurers to navigate. Accurate wording goes a long way toward developing clear and comprehensive policy documents. Generative AI, trained on a vast corpus of policy data, is already used to draft policies and suggest legal and technical terminology. Backed up by reliable data, this helps to prevent ambiguities, reduce disputes with policyholders, and enhance transparency. A rapidly developing area of the insurance industry is focused on the online delivery of products via apps or dedicated web portals.

As Generative AI becomes more widespread, the need for Explainable AI (XAI) will grow. Generative AI and the Internet of Things (IoT) will converge, creating a network of interconnected devices. Insurers will use real-time data from smart devices to offer personalized safety recommendations. The size of the dataset plays a pivotal role in determining the suitability of a Generative AI tech stack. Large datasets often necessitate the use of distributed computing frameworks like Apache Spark for efficient data processing, as they demand robust hardware and software capabilities.

User Training And Adoption

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. Insurers struggle to manage profitability while trying to grow their businesses and retain clients. In this sphere, generative AI analyzes customer data to create personalized risk profiles, which help in determining life insurance coverage and annuity offerings.

This article offers vital insights into the ways generative artificial intelligence is currently transforming the world of insurance services. Among other things, we look at the advantages of generative AI over traditional methods in insurance, integrating generative AI into insurance workflows, and its effect on customer satisfaction. The insurance market’s understanding of generative AI-related risk is in a nascent stage. This developing form of AI will impact many lines of insurance including Technology Errors and Omissions/Cyber, Professional Liability, Media Liability, Employment Practices Liability among others, depending on the AI’s use case. Insurance policies can potentially address artificial intelligence risk through affirmative coverage, specific exclusions, or by remaining silent, which creates ambiguity.

Generative models like ChatGPT or LLaMA are capable of locating and reviewing countless documents in seconds, freeing underwriters from this time-consuming and monotonous task. They can also extract relevant information and summarize it to evaluate claim validity and risks to better handle corporate and private clients. Many generative AI use cases in insurance focus on its ability to quickly and reliably aggregate information from a variety of sources to provide an efficient and time-saving overview. It can also assist with summarizing client histories and enriching existing profiles with structured data derived from policies, claims, and previous transactions. Our Technology Collection provides access to the latest insights from Aon’s thought leaders on navigating the evolving risks and opportunities of technology.

Generative AI can also generate personalized insurance policies, simulate risk scenarios, and assist in predictive modeling. In insurance, autoregressive models can be applied to generate sequential data, such as time-series data on insurance premiums, claims, or customer interactions. These models can help insurers predict future trends, identify anomalies within the data, and make data-driven decisions for business strategies.

Generative AI-driven customer analytics provides invaluable insights into customer behavior, market trends, and emerging risks. Generative AI’s predictive modeling capabilities allow insurers to simulate and forecast various risk scenarios. By identifying potential risks in advance, insurers can develop proactive risk management strategies, mitigate losses, and optimize their risk portfolios effectively. In insurance, while traditional AI excels in structured data analysis and rule-based tasks, generative AI empowers insurers with creativity, adaptability, and the potential for highly personalized services.

Data Strategy And Preparation

We focus on innovation, enhancing risk assessment, claims processing, and customer communication to provide a competitive edge and drive improved customer experiences. As the insurance industry continues to evolve, generative AI has already showcased its potential to redefine various processes by seamlessly integrating itself into these processes. Generative AI has left a significant mark on the industry, from risk assessment and fraud detection to customer service and product development. However, the future of generative AI in insurance promises to be even more dynamic and disruptive, ushering in new advancements and opportunities. All three types of generative models, GANs, VAEs, and autoregressive models, offer unique capabilities for generating new data in the insurance industry.

IBM’s experience with foundation models indicates that there is between 10x and 100x decrease in labeling requirements and a 6x decrease in training time (versus the use of traditional AI training methods). Even though generative AI introduction into the insurance sector is far from complete, it offers proactive agents a sizable number of advantages. The capacity of this technology for automation, personalization, and large-scale data analysis can put those embracing it far ahead of the competition. Privacy and security concerns with generative AI in insurance are tied primarily to protecting and preserving the confidentiality of customer data. Phishing attacks, prompt injections, and accidental disclosure of personally identifiable information (PII) — these are just a few key risks to be aware of. Like in any other industry, onboarding customers and supporting them on their journey is a significant part of providing insurance services.

Underwriting

The introduction of ChatGPT capabilities has generated a lot of interest in generative AI foundation models. Foundation models are pre-trained on unlabeled datasets and leverage self-supervised learning using neural networks. Foundation models are becoming an essential ingredient of new AI-based workflows, and IBM Watson® products have been using foundation models since 2020. IBM’s watsonx.ai™ foundation model Chat GPT library contains both IBM-built foundation models, as well as several open-source large language models (LLMs) from Hugging Face. Generative AI for insurance marketing gives companies a solid advantage by creating content that is not only engaging but also compliant. It assists marketing teams with tone of voice, brand image, and regulatory consistency all at the same time, which is otherwise a daunting task.

Computerization in claims processing will also help to reduce the number of procedures as well as the number of evaluations made and this, in the long run, will be of help to the clients. Generative AI finds applications in insurance for personalized policy generation, fraud detection, risk modeling, customer communication and more. Its versatility allows insurance companies to streamline processes and enhance various aspects of their operations. Generative AI automates claims processing, extracting and validating data from claim documents.

This transcends conventional methods by harnessing robust Large Language Models (LLMs) and integrating them with the insurance company’s distinct knowledge repository. This architecture opens up a new frontier of insight generation, empowering insurance enterprises to make real-time, data-informed decisions. It provides an insightful overview of the distinctions between traditional and generative AI in insurance operations, highlighting their unique contributions.

• CreateCreating and repurposing content for insurance customer support teams can be a challenging task given the breadth of topics they need to handle — from customer inquiries to insurance regulations and product features.
• Now it is time to explore exactly what makes it possible to harness Generative AI  for Insurance and obtain truly impressive results.
• It requires in-depth research and analysis, the selection and use of appropriate language, and the review and verification of information.
• Toxic information, which can produce biased outcomes, is particularly difficult to filter out of such large data sets.
• LeewayHertz’s generative AI platform, ZBrain, serves as an indispensable tool for optimizing and streamlining various facets of insurance processes within the industry.

However, amidst these promising prospects, there exists a need to navigate the intricate terrain of data privacy, adhere to regulatory compliance, and uphold ethical considerations. Striking the right balance becomes imperative in unlocking the full potential of generative AI in the insurance domain. Specify the desired outcomes, such as improved claims processing efficiency or enhanced customer service through chatbots.

It requires in-depth research and analysis, the selection and use of appropriate language, and the review and verification of information. It’s also a complex process that involves understanding insurance policies, regulations, and legal requirements. LeewayHertz ensures flexible integration of generative AI into businesses’ existing systems. The benefits include improved risk assessment accuracy, streamlined claims processing, and enhanced customer engagement, offering a seamless transition for small and medium-sized insurance enterprises. AI agents/copilots don’t just increase the efficiency of operational processes but also significantly enhance the efficiency of the insurance sector’s operations.

Expert raises a warning on ‘unpredictable’ development

The initial focus is on understanding where GenAI (or AI overall) is or could be used, how outputs are generated, and which data and algorithms are used to produce them. Most LLMs are built on third-party data streams, https://chat.openai.com/ meaning insurers may be affected by external data breaches. They may also face significant risks when they use their own data — including personally identifiable information (PII) — to adapt or fine-tune LLMs.

On the contrary, group insurance plans are offered to a defined group of people, such as employees and members of an organization or professional association. Here, the coverage costs are typically lower than those of individual policies due to the group purchasing power. Individual insurance is designed to shield individuals and their families against financial threats from unforeseen events. You can foun additiona information about ai customer service and artificial intelligence and NLP. Broadly speaking, these insurance types are geared toward protecting a specific population segment, which means that insurers may greatly profit from GenAI powers of customization. This talent shortage can be addressed with the help of generative AI, and particularly LLMs, providing underwriting support.

Trade, technology, weather and workforce stability are the central forces in today’s risk landscape. Our Better Being podcast series, hosted by Aon Chief Wellbeing Officer Rachel Fellowes, explores wellbeing strategies and resilience. This season we cover human sustainability, kindness in the workplace, how to measure wellbeing, managing grief and more. The contents herein may not be reproduced, reused, reprinted or redistributed without the expressed written consent of Aon, unless otherwise authorized by Aon. Therefore, data security becomes a paramount concern when implementing Generative AI systems. Ensuring the utmost data security and privacy safeguards against vulnerabilities and breaches.

Similar enhancements for data management, compliance or other operational risk frameworks include data quality, data bias, privacy requirements, entitlement provisions, and conduct-related considerations. Generative AI can streamline the process of creating insurance policies and all the related paperwork. It can help with the generation of documents, invoices, and certificates with preset templates and customer details. Unlike transformer-based models, diffusion models do not predict the upcoming token based on preceding information.

AI can also help underwriters identify potential risks and flag any irregularities so that they can make informed decisions. Generative AI automates claims processing by extracting and validating data from claim documents, reducing manual efforts and processing time. Automated claims processing ensures faster and more accurate claim settlements, improving customer satisfaction and operational efficiency. For example, property insurers can utilize generative AI to automatically process claims for damages caused by natural disasters, automating the assessment and settlement for affected policyholders.

By analyzing patterns in claims data, Generative AI can detect anomalies or behaviors that deviate from the norm. If a claim does not align with expected patterns, Generative AI can flag it for further investigation by trained staff. This not only helps ensure the legitimacy of claims but also aids in maintaining the integrity of the claims process. Generative AI systems are developed based on prompts and extensive pre-training on large datasets. Essentially, Generative AI generates responses to prompts by identifying patterns in existing data across various domains, using domain-specific LLMs.

In insurance, VAEs can be utilized to generate novel and diverse risk scenarios, which can be valuable for risk assessment, portfolio optimization, and developing innovative insurance products. GANs are a class of generative models introduced by Ian Goodfellow and his colleagues in 2014. They consist of two neural networks, the generator and the discriminator, engaged in a competitive game. The generator’s role is to generate fake data samples, while the discriminator’s task is to distinguish between real and fake samples.