Enter Large Language Models (or commonly known as Generative AI), which are not just a stepping stone but a giant leap, bringing a new era of optimized underwriting. By rapidly analysing complex and voluminous data points, it can proactively identify risk and deviations with a high degree of accuracy.
In the Insurance sector, underwriting isn't just a process-it's the cornerstone of risk evaluation. As we look towards a market projected to surge to $222 billion by 2026, underwriting's role in shaping premiums and coverages has never been more pivotal. Traditional underwriting has been a complex and timeconsuming process involving hundreds of data points (medical history, financial stability, occupation risk, lifestyle factors, etc.) and multiple guidelines that must be carefully reviewed.
Enter Large Language Models (or commonly known as Generative AI), which are not just a stepping stone but a giant leap, bringing a new era of optimized underwriting. By rapidly analysing complex and voluminous data points, it can proactively identify risk and deviations with a high degree of accuracy.
The result? A streamlined process that not only accelerates decision-making but also reduces bias and improves standardization, paving the way for accurately tailored premiums and coverages.
Leading the wave of innovation, we, at LUMIQ, have developed a Gen AI enabled Underwriting Co-pilot that embeds LLM models into the existing Underwriting process of the Customer. It doesn't replace but enhances the current underwriting workflow with the sharp acumen of Generative AI. The application is designed to create a symbiotic framework where technology works in tandem with human expertise, ensuring that UWs have an extensive support without any disruption to their familiar workflow.
Denne historien er fra July 2024-utgaven av THE INSURANCE TIMES.
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Denne historien er fra July 2024-utgaven av THE INSURANCE TIMES.
Start din 7-dagers gratis prøveperiode på Magzter GOLD for å få tilgang til tusenvis av utvalgte premiumhistorier og 9000+ magasiner og aviser.
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