Creating reusable data: Noldor’s aggregation technology

“One of the things we are trying to do here… is not to unconditionally serve any one stakeholder, but make sure we can go to any one of those stakeholders – and of the platform – and the actual customers. can provide a beneficial service to them,” Hornff explained.

Noldor’s, based in New York, launched in late 2021 and gradually scaled up to more than 12 employees and counting.

As a data aggregation company, Noldor’s core platform interacts with the MGA, delegated authorities, Lloyd’s coverholders, and others. The company does this, Hornf said, to access “structured, unstructured or pseudo-structured” risk exposure data, which is then ingested, normalized and made into something “more valid and robust.” This allows for easy data consumption between stakeholders in the delegated authorization system including carriers, reinsurance brokers, Lloyd’s syndicates or vendors who may require the data to deliver their services to the MGA and coverholders.

The technology is designed to be integrated with any entity that has delegated underwriting authority, regardless of its existing technology stack. It lets Nolder’s platform employ artificial intelligence and machine learning to automate back-office tasks such as gathering data, uncovering hidden drivers of loss ratios, and reporting.

In July, Noldor announced that it raised a $10 million seed funding round led by the DESCOvery Group at Dee Shaw, a global investment and technology development firm based in New York City, and other strategic investors participated. The founders of Noldor launched the company at DESCOvery Venture Studio.

Elephants and Reusable Data

Horneff turns to an illustration to explain the company’s technical approach.

“You’re probably familiar with the parable of the blind Indians catching an elephant. One grabs the tooth. One grabs the tail, and they all describe different things. The problem I saw for the first time … MGA and coverholders Data access requests for are like that illustration,” Hornff said. “Carriers care a lot about modeling their cat. Reinsurance brokers care a lot about generating reinsurance submissions, and everyone has their own specific needs for how that data is going to be deployed.

Noldor’s job, he said, is to “sit above the fray” and subsequently create reusable data that can fill many of those cases. Its integration with MGA is for frontline reporting [a report prepared by an insurer for a reinsurer listing assets covered or actual claims paid]But it can also be used to help generate reinsurance brokerage submissions.

“For this we need to get more data and make sure that we are matching and validating the data every single day,” Horneff said. ,[We’re] Making sure we’re flagging things that might break and doing our best to allow that assembly line of data ingestion to continue unhindered for data analysis. ,

Put another way, Nolder helps streamline data exchanges with the MGA.

“These MGAs are sending six different things to different people,” he said. “We’ve allowed them to send to one person and distribute all six to other people … it’s a single point of connectivity so that we can act as a data clearinghouse for MGA’s access to data.”

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Data extraction technologies, optical character recognition (OCR) and web crawling (a computer program that automatically searches web pages for certain keywords) helped advance the Noldor platform.

“We just go to the super high level,” Hornff said. “We can leverage AI and machine learning to train ourselves how we’re extracting data and start automating some of the human steps needed to validate that data.”

In addition, Hornuff explained, Nord can help reduce expenses through internal tooling that allows the data to be hammered in without relying on an engineer to code the data.

“We are building an internal technology stack that allows this to be done with a business analyst,” Hornff said. “I can reduce the cost that it takes to do [data] Doing mapping while gaining the expertise of someone who has spent 20 years in the industry and really knows how to dictate, but doesn’t know how to translate the data.


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