A decade ago, when Rachel Carpenter and Joseph French founded Intrinio, the fintech revolution was just beginning. But they see an opportunity to apply machine learning to a fixed number of financial documents to create alternative data providers among giants.
The startup is based in St. St. Petersburg, Florida, provides financial data to hedge funds, proprietary trading stores, retail brokers, fintech developers, and more. Intrinio runs machine learning on AWS instances or NVIDIA GPUs to parse large amounts of publicly available financial data.
Carpenter and French realized early on that such data was sold at a premium, and machine learning provided a way to classify free financial documents for new offerings.
The company provides information on stocks, options, estimates and ETFs, as well as environmental, social and governance data. Its most popular product is stock fundamental data.
Intrinio took a spin-off approach to the legacy product, creating the a la carte data service now used in some 450 fintech applications.
“GPUs help us unlock data that is expensive and manually acquired,” said Carpenter, the company’s CEO. “We’ve built a lot of technology and we want to unlock data for innovators in financial services.”
Intrinio is a member of NVIDIA Inception, a free global program designed to support cutting-edge startups.
Partnering with FinTech
With the lower overhead of financial data provided by GPU-powered machine learning, Intrinio is able to offer products that are attractive to startups at lower prices.
“We have a smaller, more agile team because a small team — a combination of NVIDIA GPUs, TensorFlow, PyTorch and everything else we’re using — automates our work,” she said.
Its clients include fintech companies such as Robinhood, FTX, Domain Money, MarketBeat and Alpaca. Another Aiera uses its own NVIDIA GPU-powered automatic speech recognition model to transcribe earnings calls in real time and relies on Intrinio for financial data.
“Our use of GPUs makes our data packages affordable and easy to use with Aiera, so the company is integrating Intrinio financial data into its platform,” Carpenter said.
Aiera needed financial data cleaning services to get consistent information about company earnings and more. Using Intrinio’s application programming interface, Aiera can access standardized, instantaneous company financial data.
“GPUs are a key component of Intrinio’s underlying technology — without them, we would not be able to apply machine learning techniques to cleaning and normalizing underlying and financial statement data,” Carpenter said.
Serving stocks, options, ESG
For stock pricing, Intrinio’s machine learning technology can resolve pricing discrepancies in milliseconds. According to Carpenter, this results in higher data quality and reliability for users. With an equity base, Intrinio automates several key processes, such as entity recognition. Intrinio uses machine learning to identify company names or other key information from unstructured text to ensure proper classification of data.
In other cases, Intrinio applies machine learning to reconcile line items in financial statements into standardized buckets, so you can clearly compare revenue between companies, for example.
Using GPUs and machine learning in both cases produces higher quality data than manual methods. According to the company, using Intrinio has proven to reduce the number of errors that need to be corrected by 88% compared to manual sorting.
For options, Intrinio takes the original Option Price Reporting Agency (OPRA) feed and applies cutting edge filtering, algorithms and server architecture to provide its options API
ESG data is also an area of current investor interest. Institutions are also feeling the pressure to invest responsibly as retail investors begin to pay more attention to the environment, and they want to see how companies can hold this information.
As regulation around ESG disclosure continues to improve, Intrinio said it will be able to unlock these datasets for its users using its automated XBRL standardization technology. XBRL is a standardized business digital information interchange format.
“On the retail side, app developers need to expose this information to their users because people want to see it — making this data accessible is critical to the growth of the financial industry,” Carpenter said.
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