The Fidelis Partnership's Broker Connect uses AI to automate pre-bind submissions, with Howden as first partner. The platform targets fragmented manual processes in complex risk placement.
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The Fidelis Partnership (TFP) has launched Broker Connect, an AI-enabled underwriting platform designed to automate the pre-bind submission process for complex risks. Howden is the first broking partner to go live via ACORD ADEPT, one of the key platforms integrated into TFP's system. The move targets the fragmented, manual workflows that have long slowed placement decisions in specialty insurance.
Broker Connect replaces manual submission processes with structured data and a digital workflow. TFP stated that the platform reduces friction, removes the need for rekeying, and improves data accuracy. It also accuracy. It creates a single governed store for broker messages and documents across both TFP and its specialist MGA platform, Pine Walk.
O’Hare added that the firm is using AI to increase the scale and speed of business with Howden, giving teams greater precision and removing manual processes. The launch reflects TFP's broader commitment to leveraging AI across the business.
ACORD ADEPT is the data exchange standard that enables structured information flow between TFP and Howden. By adopting an industry-standard platform, TFP avoids proprietary lock-in and allows other brokers to connect more easily in the future.
The common mistake is to view AI in insurance as simply faster processing. In reality, the friction comes from fragmented data entry, inconsistent formats, and time spent rekeying information across systems. Broker Connect addresses this by structuring data at the point of submission.
Walton noted that the integration helps address one of the most persistent friction points in the market. The pilot with TFP is designed to prove that structured data can replace email chains and PDF attachments.
Speed is a byproduct, not the goal. The better read framework is data governance. Broker Connect creates a single repository for all broker submissions, enabling underwriting, operations, and claims teams to search historic data at pace. This reduces duplicate work and improves the accuracy of risk assessment.
Broker Connect incorporates AI-assisted search that allows teams to locate and assess historic submission data quickly. Instead of manually digging through folders or emails, an underwriter can query past submissions for similar risks, policy wordings, or claims history.
Consider a broker submitting a bespoke liability risk for a renewable energy project. In the old workflow, the underwriter would spend hours gathering comparable data from multiple sources. With Broker Connect, the AI surfaces relevant past submissions, pricing models, and loss data in seconds. The underwriter can then focus on structuring the deal rather than hunting for information.
The launch signals a shift toward digital workflow in specialty insurance, a sector that has lagged behind retail lines in automation. Howden's participation as the first partner gives the platform credibility and a real-world testing ground.
Broker Connect is initially limited to TFP and Howden. The next catalyst is expansion to other broking partners. If TFP can demonstrate measurable reductions in submission-to-quote time and error rates, adoption could accelerate. Conversely, if integration with other brokers proves difficult or if data governance concerns arise, the rollout may stall.
TFP plans to extend Broker Connect to pricing and actuarial capabilities in a subsequent phase. The AI will transform unstructured data into formats required for modelling and pricing systems. This is a longer-term catalyst that could further reduce cycle times and improve loss ratios.
For now, the immediate watch item is Howden's feedback on the pilot. If the platform reduces rekeying and speeds up complex risk placements, other brokers will have a strong incentive to join. The Fidelis Partnership has taken a concrete evidence that AI can move beyond marketing hype and into the insurance market.
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