Creating Reusable Data: Noldor Aggregation Technology

“One thing we’re trying to do here … is not serve any one stakeholder unconditionally, but make sure we can go to any of those stakeholders – on and off the platform – and provide a useful service to the actual customer,” Horneff explained.

New York-based Noldor launched in late 2021 and has grown to more than 12 employees and growing.

As a data aggregation company, Noldor’s core platform interacts with MGAs, Delegated Authorities, Lloyd’s Holders and more. The company does this, Horneff said, to access “structured, unstructured, or pseudo-structured” exposure claim data, which is then ingested, normalized and turned into something “more verifiable and robust.” This enables easier use of data among stakeholders in the delegated authority system, including carriers, reinsurance brokers, Lloyd’s syndicates or suppliers who may need data to provide their services to MGAs and policyholders.

The technology is designed to integrate with any entity that has delegated signing authority, regardless of their existing technology stack. It enables Noldor’s platform to use artificial intelligence and machine learning to aggregate data, uncover hidden causes of loss ratios and automate back-office functions such as reporting.

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

The Elephant and Reusable Data

Horneff turns to the parable to explain the company’s technological approach.

“You are probably familiar with the parable of the blind Indians who catch the elephant. One grabs a tusk. One catches the tail and they all break different things. The problem I’ve seen firsthand … is that data access requests to MGAs and coverage holders are just like that parable,” Horneff said. “Carriers take a lot of care in modeling their cats. Reinsurance brokers care much more about the generation of the reinsurance submission, and each has its own specific needs for how that data is deployed.

The Noldor’s job, he said, is to “sit over the battle” and create reusable data later that can fill many of these cases. Its integration with MGA is for border 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 requests for reinsurance brokerage.

“It requires us to get more data and make sure that we’re reconciling and validating the data every day,” Horneff said. “[We’re] making sure we’re flagging things that might break and trying to do everything we can to allow that pipeline of data ingestion to data analytics to go on and on without a hitch.”

Put another way, the Noldor help streamline the data exchange with the MGA.

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

Key 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’re just going super high,” Horneff said. “We can use AI and machine learning to train ourselves on how we mine data and start to automate some of the human steps that are required to validate that data.”

Additionally, Horneff explained, Nordor can help reduce costs through internal tools that allow it to collect data without having to rely on an engineer to code the data.

“We need to build an internal technology stack that allows this to be done with a business analyst,” Horneff said. “I can reduce the costs required to do the [data] mapping while gaining the benefit of the expertise of someone who may have spent 20 years in the industry and knows how to actually dictate but may not know how to code how the data should be translated.”

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