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How Generative AI is Shaping the Future of the Insurance Industry

Last week, I had the opportunity to attend Celent’s Generative AI Symposium in Chicago. I was fortunate to learn a lot and meet many knowledgeable professionals. It was the first time I had attended an event on this particular technology topic, but giving where we are headed at DigitalOwl and the explosive buzz of Generative AI, it was a great opportunity to hear what some of the other experts in the industry are saying.


The whole event centered around Generative AI. In this blog, I’ll talk about some of the things I learned at the Celent event, including the different types of AI, how AI can revolutionize the insurance industry, how AI is creating new jobs, things to consider from a regulatory perspective and much more.


What is Generative AI?


Generative AI, also known as Gen AI or Large Language Models (LLMs), is a new form of Artificial Intelligence (AI) that can write, brainstorm, code, and perform various tasks based on existing examples or training data.


Unlike traditional AI, which is reactive and based on historical data, Generative AI actively learns and creates something new. This technology has gained significant attention, with Chat GPT being one of the most popular applications of Generative AI.


The impact of Generative AI is significant, with ChatGPT reaching 100 million active users in just two months, making it the fastest-growing consumer app in history. This technology cannot be ignored, a recent Celent survey show that 50% of insurance companies are either testing LLMs or planning to do so by the end of the year. Customer service is seen as the lowest hanging fruit, but underwriting and claims processing are also ripe for disruption.


The Generative AI Revolution


Generative AI has the potential to revolutionize the insurance industry in several ways. It can help in fraud detection by analyzing large amounts of data to identify patterns and anomalies indicative of fraudulent activity. LLMs can also be integrated into customer service platforms to provide instant responses to inquiries, reducing the workload on human advisors. In terms of operational efficiency, LLMs can improve processes such as claims processing, underwriting, and premium calculation.


Different approaches can be taken to leverage Generative AI tools. On one hand, insurers can use LLMs to automate the underwriting process, gather applicant information, and assess risk profiles. LLMs can be utilized for claims processing by automating data entry, document verification, and eligibility determination. Additionally, LLMs can support marketing departments with content generation, email marketing, social media marketing, data analysis, and A/B testing.

While Generative AI has the potential to disrupt and augment every industry and process, it is important to note that it doesn't replace humans but instead empowers those who understand AI. Think of it like a co-pilot or a wingman for the underwriter or the claim adjuster.


Different Types of AI


Generative AI falls within the broader spectrum of AI, which includes various technologies and approaches. It all can become confusing and with all the buzz, everybody is trying to jump on the bandwagon. Here is my version of an explanation of the spectrum;


  • AI refers to machines mimicking cognitive functions and can range from voice assistants to customer service chatbots.

  • Machine Learning is a type of AI that uses algorithms to learn from data and refine their operations over time.

  • Deep Learning, inspired by the human brain, processes vast amounts of data and has proven effective in tasks such as computer vision and natural language processing.

  • Generative AI focuses on producing text, images, audio, or code based on human language prompts. LLMs are specifically trained to predict the next word in a sentence, using the previous words.


Generative AI, with its focus on human language, helps organizations understand and analyze human-to-computer interactions. It involves tasks like text classification, sentiment analysis, and machine translation. LLMs, in particular, generate value by using an organization's proprietary data and external sources to produce human-like text, but keep in mind that proper setup is required.


Enhancing the Claims Industry


In addition to Life Underwriting (that we at DigitalOwl are making signficant progress with on this front), AI holds huge potential for enhancing the claims process in the P&C insurance industry. By utilizing advanced pattern recognition, Generative AI can streamline claims processing by comparing new claims with past data. This enables AI to identify relevant information, predict potential disputes, and even suggest optimal settlement amounts. Accelerated processing like this can result in improved customer satisfaction and more efficient claims handling.


Additionally, Generative AI is proficient in processing large datasets and can be employed for risk assessment. By analyzing property data, historical claims, weather patterns, and customer information, Generative AI can create personalized risk profiles for policy applicants. This enables insurers to make more precise underwriting decisions and provide more tailored policies. Ultimately, this can lead to increased profitability and customer satisfaction.


Regulatory and Risk Considerations


Although Generative AI offers great benefits, there are also areas of caution that need to be considered. Firstly, the quality and interpretation of data are crucial. Dependence on incomplete, incorrect, or biased data can lead to inaccurate conclusions and potential claim mishandling. Moreover, ethics and fairness are a concern. AI models, including Generative AI, have the potential to reinforce biases in the underwriting and claims process, resulting in unfair treatment and discrimination.


Another area of caution is the potential for AI "hallucination," wherein the Generative AI may provide information that isn't grounded in the input data. This could lead to misinformation, misinterpretation, and inaccuracies in claim evaluation and handling. Lastly, privacy and security must be upheld as AI requires access to sensitive data. Insurance companies need to implement robust data security measures to protect policyholder information and ensure compliance with privacy laws.

Due to the impact of Generative AI on the heavily regulated insurance industry, regulators are focusing more on risk mitigation strategies. Laws and regulations are being developed to keep up with the fast-evolving technology. Regulators are particularly interested in avoiding bias and bad decisions that affect the policyholder/applicant and could also lead to reputational risk for the carrier.


Boards and key decision-makers should actively participate in setting rules and parameters to ensure compliance for their businesses. It is also essential for insurance companies to have transparency and be able to explain the process of generative AI. Lack of transparency can lead to issues regarding black box AI systems, which are AI programs that don’t show the user how the AI came to a decision. This can increase potential risks for businesses of all sizes. Furthermore, insurers need to consider new types of risks emerging as a result of AI technology, such as cyber risks, and may need to consider insuring against those risks.


Creating New Jobs


New jobs are also being created thanks to the introduction of AI. Companies are already hiring Heads of Generative AI for their technology departments, signaling the growing importance of this field. Another potential emerging role is the Prompt Engineer, a professional who can understand human processes and assemble complex systems. Overall, the rise of these careers and the need for specialized solutions highlight the demand for new skills in the engineering field.


When it comes to implementing these technologies, companies have to decide whether to build or buy the necessary systems. Building can be a viable option for simple and easily understood processes with few interrelated parts. However, for complex processes such as medical documentation, it may be more convenient to purchase existing solutions.


This trend is creating room for niche players in the market. Instead of focusing on general solutions, companies will need to delve into the intricate details of specific processes, such as life underwriting, insurance claims, or legal documentation. Having industry-specific expertise and previous experience in the field can be highly beneficial in such cases.


Tips on Getting Started with AI


If you're considering diving into the world of AI, then the time is now. Here are some essential steps to help get you started.


  • Experiment with AI: Explore free tools and resources, take online courses, or participate in AI events to gain experience.

  • Clarify the Use Case: Have a clear understanding of your use case and the intended outcome, and be sure to identify specific problems or tasks AI can help address.

  • Challenge Tech Vendors: Challenge AI solution providers by asking detailed questions about their technology, capabilities, and track record.

  • Explore New Vendors: Look beyond established tech vendors and consider exploring startups and innovative AI companies.

  • Implement Controls and Frameworks: As AI involves dealing with vast amounts of data, it's crucial to have controls, guardrails, and frameworks in place.

  • Ensure Reliability: Devote resources to ensure data accuracy and model performance through regular monitoring, validation, and testing.

  • Train Employees: Invest in AI training programs that help develop AI talent within your workforce.


In order to successfully implement these technologies, businesses need to bring various departments together. It's crucial to integrate the expertise of professionals in business, legal, information technology (IT), and other relevant fields. This collaboration will ensure a comprehensive approach to addressing the challenges of incorporating AI systems.


By taking these steps, you can position your company to harness the benefits of AI strategically. Remember that the field of AI is rapidly evolving, so ongoing learning and being adaptable to new advancements will be critical for long-term success. The next two years will shape the future of this technology, and it is an exciting time for innovation.


Conclusion


Overall, Generative AI holds significant promise for the insurance industry, offering opportunities for efficiency, accuracy, and customer service improvement. With proper precautions and adherence to regulations, insurers can leverage AI to deliver faster, more accurate claims handling and underwriting while providing a better customer experience. As the field continues to evolve, insurers need to explore and navigate the possibilities and risks associated with Generative AI to stay competitive and innovative in today's digital landscape.


If you have any questions about DigitalOwl’s advanced AI solutions, or if you’d like to schedule your free demo with us, then make sure to contact us today! Be sure to follow DigitalOwl on LinkedIn to stay up to date on all things Generative AI in the Insurance industry.

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