Accern gets $20M for AI that parses financial documents online

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Akcern, which uses AI to analyze online conversations about specific companies, trends and industries, today announced it has raised $20 million in a Series B round led by Mighty Capital along with Tribe Capital, Shasta Ventures, Gaingels and Fusion Fund, among others. CEO Kumesh Arumugan says the new capital will be directed towards “product-driven growth”, expanding into new markets and researching and developing Accern’s artificial intelligence technologies.

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“More than 80% of the data in the world is unstructured. Unstructured data requires a super-manual process to structure data at scale, requiring costly data processing resources across the organization,” Arumugan told TechCrunch via email. “Due to the huge cost of human capital and time, unstructured data is not analyzed effectively and is often left out of historical data decision making. The end result impacts the decision-making capabilities of all organizations and adds additional risk to their respective portfolios and balance sheets.”

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Kumesh Arumugan, a former analyst at Wall Street firms including Citigroup, co-founded Accern with Anshul Vikram Pandey in 2014. to the shares. But the company later expanded its scope to other aspects of corporate finance, such as loan and fraud monitoring and regulatory compliance.

For customers, Accern provides AI-based applications and Natural Language Processing (NLP) models trained to recognize, classify and extract domain-specific financial language. The service can scan public sources, including news publications, blogs, and SEC filings, to, for example, gauge consumer sentiment or predict how supply chain disruptions might impact a business.

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Accern also offers a visual dashboard with which users can create their own AI-powered apps, as well as pre-built taxonomies covering companies, people, places, and topics. “We currently have a vast collection of financial service use cases that our clients create on our platform so they don’t [have to] think about what can be built on our platform, or take the time to build something from scratch versus traditional NLP cloud providers,” Arumugan said. “All of our models are trained on financial content by financial analysts and subject matter experts, ensuring high accuracy for financial service use cases.”

Even the most advanced algorithms are subject to prejudice or, of course, drift when their accuracy decreases over time due to factors such as seasonality and erroneous data. But like many companies whose models are proprietary, Accern keeps the secrets of its development processes. Arumugan did not say when asked what datasets were used to train his models and how the company removes any possible biases.

Akcern

Image credits: Akcern

Instead, Arumugan, by refraining from naming clients, prefers to highlight Accern’s market appeal. While a startup competes with dataminr and to some extent data analysis products from Nugata and Pecan.ai (plus data services like Reuters and Bloomberg), Arumugan claims that Accern’s annual recurring revenue has grown 9 times since 2020.

“Accern’s corporate goal is to accelerate innovation by providing organizations with…models that enable them to more efficiently transform their unstructured data into true business intelligence—while reducing time and costs,” Arumugan said. “Many of our clients use us to enhance their existing models, business intelligence dashboards, and products with new features from text data in a no-code workflow.”

To date, Accern has raised $20 million in capital. The company currently has 80 employees and plans to increase its headcount to 100 by the end of 2022.


Credit: techcrunch.com /

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