Indico Data is mentioned as a Sample Vendor for Digitally Engineered Underwriting and IDP in Gartner Hype Cycle for Digital Life and P&C Insurance, 2023
Read Now
  Everest Group IDP
             PEAK Matrix® 2022  
Indico Named as Major Contender and Star Performer in Everest Group's PEAK Matrix® for Intelligent Document Processing (IDP)
Access the Report


Transforming underwriting: Challenges, opportunities, and the AI advantage

September 14, 2023 | Artificial Intelligence, Unstructured Unlocked

Back to Blog

In the ever-evolving landscape of the insurance industry, the adoption of advanced technologies is crucial to enhance efficiency, accuracy, and customer satisfaction. One area that has garnered significant attention is underwriting, a complex process that determines risk and sets premium rates.

In the latest episode of Indico’s Unstructured Unlocked Podcast, special guest Alan Ringvald, Co-Founder and CEO of Relativity6, shared with our hosts the potential of AI to revolutionize underwriting processes. The discussion shed light on the challenges and opportunities presented by this technological shift, the impact of advanced AI models, and the strategic considerations carriers need to make regarding in-house development and outsourcing for AI solutions.

Listen to the full podcast here: Unstructured Unlocked episode 28 with Alan Ringvald, CEO at Relativity6


Challenges and opportunities in underwriting automation


Underwriting, traditionally a time-consuming and manual process, is ripe for disruption. AI brings a wealth of opportunities to the table, promising increased efficiency, improved accuracy, and enhanced decision-making. Ringvald highlighted, “AI-driven solutions have the potential to be incredibly transformative for the underwriting process. Tasks that were once manual, such as identifying businesses’ actual activities and classifying them correctly, can now be automated.”

However, embracing underwriting automation is not without its challenges. Data quality remains a paramount concern. Garbage in, garbage out – this adage holds true in the AI era. To achieve meaningful insights, accurate and relevant data is imperative. Many insurers struggle to ensure that the data they possess is clean, structured, and up-to-date. Additionally, the interpretability of AI models is a key consideration. As AI systems grow more complex, making their decisions understandable to human underwriters becomes a critical factor in building trust and ensuring compliance with regulatory requirements.



Related content: How automating claims intake streamlines settlements, saves time, and boosts customer satisfaction


Impact of advanced AI models


The conversation turned toward the remarkable impact of advanced AI models, particularly the introduction of Generative Pre-trained Transformers (GPTs) like GPT-4. These models, renowned for their ability to generate human-like text and process vast amounts of data, have the potential to reshape the underwriting landscape. However, Ringvald cautioned that, “their lack of domain-specific expertise poses challenges for insurance. Applying such models to an industry as specialized as ours presents difficulties in terms of legality, ethics, and transparency.”

Despite the immense possibilities, integrating AI like GPT-4 into underwriting processes is not a straightforward endeavor. The legal and ethical implications of using AI in decisions with significant financial impact must be carefully navigated. Transparency, accountability, and explainability become essential when utilizing AI in contexts where human livelihoods are at stake.


Related content: Real-world insights on the benefits and challenges of underwriting modernization


Balancing in-house development and outsourcing


As the insurance industry races to capitalize on the benefits of AI, a critical question emerges: should carriers focus on in-house AI development or opt for outsourcing solutions from established tech partners? Ringvald stated that, “the decision-making process between in-house development and outsourcing depends on various factors. Insurers must carefully evaluate their long-term strategies, available resources, and risk tolerance.”

Building in-house AI solutions demands substantial resources, including talent, infrastructure, and time. The technology landscape evolves at an astonishing pace, making it challenging for insurers to stay at the cutting edge while juggling their core business functions. However, in-house development can offer customized solutions tailored to a carrier’s specific needs and domain expertise.

On the other hand, outsourcing AI solutions offers a more rapid path to adoption. Tech partners with domain knowledge can provide ready-made solutions that accelerate underwriting automation. While outsourcing may lack the customization of in-house development, it enables carriers to leverage the expertise of AI specialists who are dedicated to staying ahead of the technology curve.

In the end, the decision between in-house development and outsourcing hinges on an insurer’s strategic goals, resources, and risk appetite. A hybrid approach that combines both strategies may also hold promise, allowing carriers to benefit from external expertise while maintaining control over certain aspects of development.

In the dynamic world of insurance, AI is poised to be a game-changer in underwriting. This episode underscores the pivotal role AI can play in revolutionizing underwriting processes, from automating manual tasks to redefining decision-making with advanced models. As carriers navigate the challenges and opportunities presented by AI, the choice between in-house development and outsourcing will be instrumental in shaping the future of underwriting and the insurance industry as a whole.

Find the transcript of the conversation here.

Check out our full conversation on YouTube, or on your favorite podcast platform, including:


Subscribe to our LinkedIn newsletter.

Increase intake capacity. Drive top line revenue growth.

Get started with Indico

1-1 Demo

Monthly Live Demo

Interactive Demo



Gain insights from experts in automation, data, machine learning, and digital transformation.

Unstructured Unlocked

Enterprise leaders discuss how to unlock value from unstructured data.

YouTube Channel

Check out our YouTube channel to see clips from our podcast and more.
Subscribe to our blog

Get our best content on intelligent automation sent to your inbox weekly!