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The 5 Biggest Challenges of Generative AI in Insurance

Published On
October 19, 2023
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Artificial Intelligence (AI) has emerged as a transformative force across countless industries, revolutionizing processes and decision-making. In the world of insurance, AI holds tremendous promise in improving efficiency, accuracy, and risk assessment. However, like any groundbreaking technology, AI faces its share of challenges. In this blog, we will explore the five universal challenges of Generative AI in insurance and how DigitalOwl's cutting-edge technology addresses them — empowering insurance professionals to navigate the evolving landscape of the industry with confidence.

Understanding the Universal Challenges of AI in Insurance

As AI continues to revolutionize the insurance industry, it brings a host of transformative opportunities. However, with these opportunities come inherent challenges that demand attention and innovative solutions. As covered in our webinar The Future of Medical Record Summarization with Generative Text Models, these challenges include:

  • Accuracy: Inaccuracies manifested in different ways by an AI
  • Coverage: The effectiveness of AI systems
  • Coherence: Ability of an AI system to generate outputs
  • Black Box Logic: Opaque decision making by an AI system
  • Privacy Concerns: Data privacy and digital security

AI Challenge #1: Accuracy

AI systems, particularly Generative AI, are not immune to errors. In terms of AI in general, inaccuracies can manifest in different ways depending on the system being used. For Generative AI systems, inaccuracies usually manifest in the form of “hallucinations” — which are instances where the AI system generates inaccurate or false information. When underwriters are presented with these possible inaccuracies, this can lead to flawed predictions and other issues — exposing insurance companies and their customers to potential risks. The implications of hallucinations in Generative AI underscore the importance of developing robust and reliable AI models.

More common and popular AI systems can use as few as a single data set to come up with answers for their users. While this can provide results, accuracy on these types of systems may always be questioned. To bolster accuracy and eliminate hallucinations, DigitalOwl bolsters our Generative AI by leveraging our other unique AI technology.


Our Entity Recognition engine extracts data and information from medical reports, which is then summarized by our Generative AI. This allows our state-of-the-art algorithms and extensive data analysis to significantly reduce inaccuracies, providing insurers with dependable insights for making informed decisions. Sporting more than 97% accuracy, we fine tune and improve our models every two months in order to keep our AI solutions running as efficiently and accurately as possible.

AI Challenge #2: Coverage

Coverage refers to the effectiveness of AI systems to analyze a wide range of information, inputs, documents, and data. Major challenges arise when AI lacks the ability to comprehend data, leading to blind spots and incomplete assessments. In the insurance industry, inadequate Generative AI coverage means insurance professionals can’t upload and analyze essential documents, which in turn can lead to gaps in essential data and suboptimal decisions.


Coverage is important for any successful AI system to operate reliably. To address the challenge of coverage that many other AIs face, DigitalOwl employs a multifaceted approach. Our Generative AI system and other AI engines are designed to read, comprehend, and accurately summarize a wide variety of input types, ranging from scanned documents to images and EHRs. This ensures that our solutions can ingest and understand diverse data sources, leaving no entity behind. Furthermore, we’ve tailored our AI solutions to fit your business. No matter what industry you’re in, our systems will provide relevant data that fits your needs.

In essence, our approach to coverage is twofold: ensuring our AI can understand any input it encounters and making sure it includes all relevant information in its output. This comprehensive approach is fundamental to our commitment to delivering reliable and accurate insights for insurance professionals.

AI Challenge #3: Coherence

Coherence is the ability of a Generative AI system to generate outputs or responses that are consistent, logical, and contextually relevant. This is essential in ensuring that AI-generated insights make sense and are on topic. When AI fails to produce coherent output, it can erode the trust of insurance professionals in the technology they use. Incoherent AI-generated data can also lead to confusion and uncertainty in decision-making.


In order to address the challenge that many other AIs have faced, we’ve meticulously designed our AI to deliver coherent and consistent information. Our platform's advanced algorithms and context-aware processing ensure that the insights provided by DigitalOwl's AI are reliable and easily interpretable, fostering trust in the technology among insurance professionals.

AI Challenge #4:  Black Box Logic

Some AI systems operate as "black boxes," which means the decision-making process of the AI remains opaque and difficult to understand. A black-box AI can provide information without providing context or reasons why it came up with that conclusion, which can cause problems when the information isn’t reviewed. In the insurance sector, this "black box logic" challenge presents significant hurdles, particularly concerning regulatory compliance and accountability. Insurance professionals need to understand how and why AI presents certain information in order to justify and explain outcomes.


Looking to create one of the best AI solutions for insurance professionals, we had to put ourselves in their shoes. Quite literally, as our AI systems were created by insurance professionals for insurance professionals. Our AI prioritizes transparency and explainability, providing clear explanations and reasoning for every AI-driven decision. When you use DigitalOwl’s AI solutions, you’ll be able to sift through data and see why our AI is presenting it to you at the click of a button. All data points in our solution are clickable and will take you directly to the source document where we extract the data point. We also incorporated a technology that allows similar clickability in our generative AI summaries.

AI Challenge #5: Privacy Concerns

In today's data-centric landscape, privacy is of utmost importance, especially when dealing with sensitive information like medical records. Data privacy is a primary concern, particularly in fields as critical as insurance. Insurance professionals deal with a vast amount of confidential data daily, ranging from medical histories and financial records to personal identifiers. Any breach of this sensitive information could have far-reaching consequences, affecting both individuals and organizations alike. It's not just a matter of regulatory compliance; it's about maintaining the trust of customers and ensuring the integrity of the insurance process.


DigitalOwl understands the significance of safeguarding this sensitive data, and we've prioritized advanced security measures. Not only are we HIPAA and SOC2/Type2 compliant, but we also boast advanced digital security to make sure our clients’ information is safe and secure. On top of this, DigitalOwl’s AI is already compliant with both NAIC and the Colorado Governance Regulations that were implemented earlier this year.

Our commitment to data privacy and security is unwavering. We've taken meticulous steps to ensure that confidential information remains protected within our platform. By placing security at the forefront, we provide insurance professionals with a trusted environment where they can confidently handle sensitive data. In an era marked by evolving privacy concerns, DigitalOwl remains dedicated to not only optimizing workflow and decision-making processes but also to upholding the highest standards of data protection.

Introducing Our Revolutionary AI Technology

The heart of DigitalOwl's mission lies in the empowerment of professionals in many different industries. In order to combat the challenges we explored above, DigitalOwl developed and trained our own models and engine. This is how we can train our solutions to specifically solve these challenges where other, more general, models are not trained to. Our systems are designed to transform complex medical data into actionable insights, enabling insurance professionals to make well-informed decisions with unprecedented speed and accuracy. To give you the full scope of our solutions, our AI takes in three data sets in order to provide sustained reliability and highly effective summarization services. These sets include our:

  • Generative AI
  • Medical Knowledge Base
  • Entity Recognition Engine

Proprietary Generative AI

DigitalOwl's Generative AI finds application across various critical use cases in the insurance industry. From our Chat feature, where you can engage in dynamic conversations with your summary, to Case Notes, where our AI assists in documenting and summarizing complex medical records, and even in our Underwriting Suggestions, where our AI provides invaluable recommendations for underwriting processes. Embracing the true potential of Generative AI, DigitalOwl revolutionizes decision-making in the insurance industry by enhancing efficiency, accuracy, and risk assessment across these essential functions. With that being said, our Generative AI can’t do everything itself. That’s why we’ve paired two other solutions to create our advanced AI solutions.

Comprehensive Medical Knowledge Base

The backbone of DigitalOwl's solutions lies in its robust Medical Knowledge Base. A vast repository of medical and insurance expertise, this knowledge base equips our Generative AI and our Entity Recognition Engine with an unparalleled understanding of diverse medical scenarios. As we mentioned above, our systems are tailored to adapt to your business’s specific needs. So an underwriter will see information from the medical records they analyze differently than a claims analyst. As a result, our solutions demonstrate exceptional coverage, effortlessly handling a wide range of medical situations.

Entity Recognition Engine

With DigitalOwl's Entity Recognition Engine, information is extracted efficiently and quickly, providing our Generative AI with the information it needs to create reliable summarizations in no time. Beyond traditional entity recognition models, DigitalOwl's AI achieves a level of sophistication that redefines the landscape of AI-powered insights. Our technology surpasses mere recognition by contextualizing intricate medical nuances, providing coherent and logical outputs. This level of transparency and accuracy instills unwavering trust among users, addressing challenges many other AI systems struggle with head-on.

Transforming the InsurTech Industry with AI

With DigitalOwl's AI technology, underwriting processes are revolutionized. Time-consuming tasks are automated, enabling insurers to streamline operations and improve overall efficiency, leading to enhanced decision-making and better risk assessment.

Empowering Underwriters

DigitalOwl's AI accelerates underwriting with cutting-edge algorithms, enabling faster and more accurate risk evaluations. Enhanced efficiency handles higher case volumes, streamlining operations for better decision-making and policy offerings.

Streamlined for Claims

Our solutions optimize the claims processing workflow by swiftly analyzing medical data, resulting in quicker and more precise assessments for claims professionals. This improved claims processing expedites resolutions, upholding compliance standards and minimizing disputes.

Unparalleled Accuracy in Risk Assessment

AI's unparalleled accuracy in risk assessment has the opportunity to completely change the insurance industry. By leveraging advanced technology, like our highly advanced AI, insurers can identify risks with greater precision, leading to improved risk management and more competitive offerings.


As AI continues to shape the future of the insurance industry, it is crucial to acknowledge and address the challenges it presents. The concept of Generative AI technology has emerged as a promising solution to these challenges, offering innovative approaches that empower professionals to make data-driven decisions with confidence.

The integration of transparency, comprehensiveness, and coherence within AI systems is driving transformative change across various sectors. As these technologies evolve, the potential for groundbreaking advancements becomes clearer. Embracing the capabilities of AI can indeed unlock new possibilities, allowing industries to harness its power and navigate uncharted territories.

For those interested in exploring the potential of Generative AI, a closer look at platforms, like the one you can find here at DigitalOwl, can provide insights into the advancements being made. The journey toward harnessing the full potential of AI is ongoing, and as the technology matures, it promises to usher in a new era of growth and innovation in the dynamic landscape of insurance and beyond.

Contact us to answer any questions you may have about our Generative AI, and be sure to schedule your demo with us today! Don’t forget to follow us on LinkedIn to stay up to date on all of our latest news.

About the author

DigitalOwl is the leading InsurTech platform empowering insurance professionals to transform complex medical data into actionable insights with unprecedented speed and accuracy. “View,” “Triage,” “Connect” and “Chat,” with medical data for faster, smarter medical reviews, and create “Workflows” to experience dramatic time savings with fast, flexible decision trees.