Kalepa, the leader in AI-powered underwriting solutions, and AmRisc, the largest catastrophe-focused Managing General Agent (MGA) in the United States, have announced a strategic partnership to enhance core underwriting operations at scale.
The partnership was formed following a rigorous, end-to-end evaluation of AI and automation vendors across the InsurTech landscape.
AmRisc conducted a thorough assessment of performance, integration speed, accuracy, and long-term value, ultimately selecting Kalepa for its unmatched precision, ease of deployment, and proven impact in real-world underwriting environments.
The collaboration will focus on optimising document and submission processing, allowing underwriters at AmRisc to make faster and more informed decisions within a high-volume, complex risk environment.
This integration is expected to significantly improve operational efficiency while maintaining underwriting quality and portfolio performance.
The partnership demonstrates Kalepa’s ability to deliver performance, transparency, and adaptability. Its AI system integrates seamlessly with existing infrastructure, ensuring operational continuity while delivering immediate value and measurable impact.
Kalepa CEO and Co-Founder Paul Monasterio said, “Amrisc has always been a leader in data-driven property underwriting. Their forward-thinking approach to property risk aligns perfectly with our mission to bring innovation and precision to underwriting. We’re proud to support their team with AI technology that delivers both speed and accuracy at scale.”
AmRisc President Laura Beckmann said, “We were looking for more than just another AI vendor. We needed an organization that understood the nuances of our workflows and the importance of precision at scale.”
AmRisc Software Engineering Group Lead Mark Hall said, “We tested numerous solutions and selected Kalepa. Their team moved quickly and delivered a proof of concept with accuracy in the mid-to-high 90s across a wide range of complex submission and document types, well above internal benchmarks.”
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