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Case Study

Automate Manual Trade Finance and Commercial Banking Processes


When you think of international trade, it is mostly underpinned by transactions that takes place between importers and exporters of goods. The banking system plays a key role here as an arbitration channel that helps manage both the parties to conduct business within acceptable terms. It therefore involves international trade regulations, foreign exchange rates, and more.

This requires procedures such as opening an account and securing Letters of Credit (LOC) whereby the buyer’s bank provides the seller a guarantee to make the agreed payment on behalf of the buyer. The Bank therefore needs to assess Contract Documents, KYC Documents, Mortgage Documents, etc., to evaluate the creditworthiness liquidity/guarantee seekers. Timely evaluation of the risks can help the bank’s treasury management team to perform their tasks faster.

New-age Banks therefore need Contract AI assistants and digital worker bots to help their time-deficient Treasury Management teams. It will help them capture data accurately from multiple documents and input them accurately in tranches where they need to be. 

Our Solution

  • Build a ranking engine using Machine Learning to rank and analyze large volumes of trade transactions
  • Get a unified view of risks involving – Outstanding Advances, CLP Commitments, Collateral Availability, Unsecured Credits, etc.
  • Automate document processing, text analytics, and third-party data
  • Automate ingesting ID & income verification documents
  • Automate data extraction from unstructured texts like mortgage papers, bonds, security guarantees
  • Built a centralized interface can save 20 working days worth of man hours per month
  • Automate amortization of borrowing costs, amortization schedules
  • Automate the posting of treasury accounting data entry
  • Automate review of +100000 transactions every month via ML models
  • Cover regional and country-specific compliance requirements


  • Missing or incorrectly prepared documents pose risk for both buyers and sellers
  • Manual data extraction and validation can cause shipment delays and ultimately delayed payments
  • Manual Treasury Management may require 20+ accountants a day, per month to extract data manually.
  • More team members are required to process Bank Guarantees and Letters of Credits for multiple fund categories (can go up to 25 for large banks)
  • Treasury calculations such as accruals on debt, derivatives, and fees, and amortization can be erroneous
  • The manual process can require producing of over 5,000 lines of GL journals per month

How we Can Helped

  • Integrates a document parser solution within the Bank’s workflows
  • Creates a pulling trigger to fetch parsed data from new documents
  • Triggers automation to start a workflow anytime a new document is parsed e.g., a collateral bond
  • Triggers automation of all processes which are expecting data from e.g., a PDF document
  • Fetches parsed data from and adds new rows in Google Sheets or in any System used by employees
  • Enables automation to fetch documents from a shared DropBox or a Google Drive
  • Automates selecting parsing rules to process the document and search for the required information

The Impact

  • Gain a 500% Increase in Contract Scanning Speed
  • Enable accurate data capture with 95%+ Straight-Through Processing (STP)
  • Ensure the Treasury Team does not have to look at risk assessment documents 95 out of 100 times

BFSI Case Studies: Discover the Top Applications of Contract AI in Banking and Insurance Companies

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