- Devron is a data-science upstart that just raised its $12 million Series A led by Tiger Global.
- Kartik Chopra, a former undercover CIA technical intelligence officer, launched the startup in 2020.
- The fintech uses AI to solve one of Wall Street’s biggest pain points: moving and extracting data.
Tiger Global, the global hedge fund and venture-investing powerhouse, just led a $12 million Series A investment in Devron, a fintech using artificial intelligence to solve Wall Street’s data woes.
“We believe it is still early innings of the AI and data revolution that will produce trillions of dollars of economic value over the next decade,” John Curtius, partner at Tiger Global wrote to Insider in an email. And Devron’s technology has the “potential to alter the data science and AI landscape,” he added.
Devron allows firms to pull together data from across an organization that might be difficult to otherwise combine.
The startup’s founder and CEO Kartik Chopra is no stranger to wrangling data across different systems.
As a former CIA intelligence officer targeting adversarial technologies in Russia and China, Chopra spent years pouring over large sets of transactional data to sniff out malicious activity, bad actors, and fraud. But analyzing the data wasn’t the hard part — the bigger challenge was harnessing the data that was spread across different systems, departments, geographies, and authorization levels.
An uncanny situation is currently playing out among financial institutions, wherein sharing data between different systems or business lines is time-consuming, costly, and sometimes limited by privacy and regulatory constraints.
As Wells Fargo Group CIO Chintan Mehta recently put it to Insider, “once you move data, there’s a center of gravity. Computers are easy to move. Data is not easy to move.”
A month after leaving the CIA in 2020, Chopra founded Devron after realizing there’s a swath of other firms, both in public and private sectors, that were navigating the same data struggles.
“Our focus is really enabling data science on distributed data, so that you have access to these disparate legacy systems and warehousing technologies, and you can learn more about your consumer spending behavior while, one, remaining privacy-preserving about their data, and then two, not having to worry about moving data around the organization that much,” Chopra told Insider.
The startup has raised a total of $17.5 million, including the Series A that closed in January and saw participation from existing investors FinTech Collective, Afore Capital, and Essence Venture Capital.
Moving algorithms to the data, and not the other way around
The upstart, which launched in the fall of 2020, has “a handful” of financial services clients, Chopra said, declining to disclose specifics. Most of those clients use Devron’s tech for financial spend analysis to proactively offer customers promotional offers or banking products, as well as detecting fraud across separate transactional systems, Chopra said.
The fintech navigates data-privacy regulations and technology headaches stemming from data movement by bringing the algorithms to the data instead of the data to the algorithms, Chopra said.
Most firms will extract or copy data and send it to the application where the algorithm crunches numbers for analysis. Devron’s product relies on sending the algorithm to the application where the data resides, and using AI models that pull in relevant data and create customer profiles.
The newly created customer profiles are then removed from the application where the data resides, and can be combined with similar profiles from other applications, Chopra said. The algorithms are also removed from the databases after creating the profiles.
This ultimately allows Devron clients to create homogeneous customer profiles across disparate applications without the risk of compromising sensitive data that can’t normally be combined.
In addition to sharing data across siloed systems, the upstart also enables firms to share data between different infrastructures, such as, multiple public clouds, private clouds, and on-premise systems. It can run on Amazon Web Services, Google Cloud Platform, and Microsoft Azure, he added.