New York based AI Data Extraction platform, Daloopa secured $20 Million in Funding

Daloopa, a New York based provider of artificial intelligence (AI) driven data extraction services for financial institutions, has now secured $20 million in a Series A funding round.

Daloopa is founded by Thomas Li. The AI startup offers high-quality Artificial Intelligence (AI)-powered data extraction services to banking services, hedge funds, and some other financial institutions. 

The artificial intelligence-powered technology processes data extraction from millions of highly complex financial documents, extracting financial data across the unstructured and structured documents in the frozen languages.

According to an Allied Market Research report, the global data extraction market is expected to reach $4.90 billion by 2027.

Thomas Li, Daloopa's CEO, said, "We are at a turning point in technology where software and AI can automate some of the most mundane work for a financial analyst. At Daloopa we are focused on providing levels of data accuracy surpassing 99.9%, at unprecedented speeds and scale, for some of the most complicated document types and data structures in the financial sector."

Talal Khan, Director at NEXT Investors in Credit Suisse Asset Management, said, "We are thrilled to be working with Daloopa which has a distinct understanding of what it takes to automate data extraction and solve one of the oldest and most prevalent problems in finance".

The Credit Suisse Asset Management’s NEXT Investors led the latest funding round. Other participating investors include Hack VC, Uncorrelated Ventures, and Nexus Venture Partners. To date, the AI startup has raised a total of $24 million in funding.

Both the Nexus Venture Partners and Credit Suisse Asset Management's NEXT Investors will occupy a position on the Board of Directors

The AI powered data extraction platform provider now intends to leverage newly raised funds to accelerate the expansion of its offerings among the financial institutions, notes the announcement.