– Case Study –

Data Extraction for Mergers & Acquisitions Analytics

Leading financial intelligence company requires automation to provide hourly updates on deals

– Achieved 90% Accuracy In 12 Weeks –


A leading financial intelligence company offers a comprehensive database of information on M&A, IPO, private equity, and venture capital. They collect structured and unstructured data comprised of 84 fields of interest within news items from 5 sources. Because manually processing the unstructured data is both resource- and time-intensive, they sought an elegant solution to automate this process.


Innodata’s Data Transformation API provides seamless access to a machine learning model trained by in-house subject matter experts that facilitates an automated approach to extracting and structuring relevant information. To ensure speed, quality, and agility this project was set up in two phases. Phase 1: Develop & train 10-20 data points ML model with 4,000+ deal records. Phase 2: Offer continuous training and automation for 500+ deal records per day.

Innodata Automated Data Extraction Plan


This leading financial intelligence company is able to offer hourly updates on M&A, IPO, private equity, and venture capital, making their product a world class financial resource. Innodata’s Data Transformation API not only aids creating and updating deal records in the database by automating repetitive manual efforts, it also improves scalability across data sources.

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(NASDAQ: INOD) Innodata is a leading data engineering company. Prestigious companies across the globe turn to Innodata for help with their biggest data challenges. By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of over 3,000 subject matter experts, and a high-security infrastructure, we’re helping usher in the promise of digital data and ubiquitous AI.


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