Creating reusable data: Noldor’s aggregation technology | Business Insurance

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“Something that we’re trying to do here … is not to serve any of the stakeholders wholeheartedly, but to make sure that we can go to any of those stakeholders, on and off the platform, and provide a beneficial service to the real customer”, Horneff. explained.

New York-based Noldor launched in late 2021 and has gradually worked its way up to over 12 employees and counting.

As a data aggregation company, Noldor’s main platform interfaces with MGAs, Delegated Authorities, Lloyd’s Coverholders and more. The company does this, Horneff said, to access “structured, unstructured, or pseudo-structured” risk exposure claim data that is then incorporated, normalized, and turned into something “more validated and robust.” This allows for easier data consumption between stakeholders in the delegated authority system, including carriers, reinsurance brokers, Lloyd’s syndicates or providers that may need data to provide their services to MGAs and holders. coverage.

The technology is designed to integrate with any entity that has delegated underwriting authority, regardless of their existing technology stack. It enables Noldor’s platform to leverage artificial intelligence and machine learning to aggregate data, uncover hidden drivers of loss rates, and automate administrative functions such as reporting.

In July, Noldor announced that it had raised a $10 million seed funding round led by the DESCOvery group at DE Shaw, a New York City-based global investment and technology development firm, and other strategic investors were involved. The founders of Noldor launched the company in the DESCOvery venture study.

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The elephant and reusable data

Horneff uses a parable to explain the company’s technological focus.

“You are probably familiar with the parable of the blind Indians who catch the elephant. One grabs a fang. One grabs the tail and they all break different things. The problem that I saw firsthand … is that data access requests for MGAs and coverholders are like that parable,” Horneff said. “Carriers care a lot about the modeling of their cats. Reinsurance brokers are much more concerned with generating the reinsurance presentation, and each has their own specific need for how that data is implemented.”

Noldor’s job, he said, is to “sit above the fray” and create reusable data later that can fill many of those cases. Its integration with an MGA is for bordereau 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 presentations.

“That requires us to get more data and make sure that we are, every day, reconciling and validating the data,” Horneff said. “[We’re] making sure we’re flagging things that can go wrong and trying to do everything we can to allow the assembly line of data ingestion to data analytics to continue, unimpeded.”

Put another way, Noldor helps streamline data exchanges with MGAs.

“These MGAs are sending six different things to different people,” he said. “What we allow them to do is send it to one person and give all six to other people…it’s a single point of connectivity so we can act as a data clearinghouse to access the MGA data.”

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Main ingredients

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

“We just got to a super high level,” Horneff said. “We can leverage AI and machine learning to train ourselves on how we extract data and start to automate some of the human steps required to validate that data.”

In addition, Horneff explained, Nordor can help reduce costs through internal tools that allow you to capture data without having to rely on an engineer to code the data.

“We’re building an internal technology stack that allows that to be done with a business analyst,” Horneff said. “I can reduce the cost it takes to make the [data] mapping while getting the benefit of the experience of someone who may have spent 20 years in the industry and knows how to dictate, but may not know how to code how the data should be translated.”