Global Financial Services

Achieving seamless bulk data extraction despite a tight deadline.

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The global provider of financial service technology had to deal with more than 50,000 product contract documents. Data had to be extracted from these documents in a usable format and migrated into their Contract Management system. The client also needed Dashboards on the extracted data for ongoing maintenance. They were in need of an AI/ML consulting company that would pave a path for them.

Bulk data
Unusable data
Data migration
Data management
  • Extract data from 50,000 product contract documents
  • Extract data in a usable format
  • Migrate usable-structured data to the client’s contact management system
  • Develop dashboards on extracted data for ongoing maintenance
Our Solution

Extracting data from 50,000 documents was no mean task. Additionally, we needed to extract them in a usable format and then migrate them to their contract management system while improving the quality. The next task at hand was to develop dashboards on the extracted data for ongoing maintenance.

Solution Impact


Data entities extracted

From 50,000 contract documents (with 98 distinct doc types)


Accuracy and regrouping

For entities exported in a client-provided format

NPS improved

Increased customer satisfaction

Our Approach

As part of an advanced analytics solution powered by artificial intelligence and machine learning, we sorted the documents and grouped them into client folders. Our team used a cognitive extractor and a web application document extractor from the source files to extract data and create dashboards and PowerBI reports. We followed the process with an audit check for quality.

Our Approach
Collated and sorted the contract documents

The Fintech giant had more than 50,000 product contract documents that we needed to extract data from. As business intelligence consultants, we were required to extract data in a usable format and then migrate it to their contract management system. Additionally, the client wanted us to develop big data analytics dashboards for maintenance as part of their ongoing process.

We began the process by sorting the files. The client teams shared all the contract documents through secured FTP. Then, the offshore teams downloaded the documents on a US server. The cognitive extraction team read the documents and converted them from image to text formats. Once the documents were sorted, we grouped them into client folders.

Collated and sorted the contract documents
Used flexible and agile data extraction methodologies

Once we sorted the contract documents, we moved on to the extraction. For read-easy entities, we used cognitive extraction. Our team created a document extractor (web application) to render the documents for manual users and the complex entities were manually extracted.

Next, our QA team performed an audit check for data correctness and completeness. They also conducted a spot check on the final reviewed documents to uncover any errors. Errors, wherever present, were corrected, and the updates were saved. We also created dashboards and PowerBI reports on the data extracted as part of our advanced analytics solution.

We repeated this process for each of the 50,000 contract documents. Once we had uploaded the final extracted documents, we asked our client for a review and signoff.

Used flexible and agile data extraction methodologies
Business Impact

We successfully extracted 22 data entities from 50,000 contract documents with 98 distinct document types and we did it within ten weeks. Our AI-driven solutions achieved 100% accuracy and regrouping. Also, we developed standard PowerBI reports for the client’s ongoing maintenance.


PowerBI reports delivered within four weeks


Accuracy achieved with over 70% automation in data extraction


Weeks to complete entity extraction for all 50,000 documents

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