Accelerates Digitalization Across Trading and Operations with Expanded Commodity AI Engine
For commodity-intensive organizations, the digital transformation across trading, operations, and the supply chain has moved to the forefront of today’s conversation. Digitalization of the hundreds of types of documents used across trading networks is helping to unlock more data and accelerate the path to automation and broader organizational efficiencies.
According to McKinsey and Company, digitizing one of these documents, the bill of lading, could not only save $6.5 Billion, but also create $30 Billion to $40 Billion in new opportunities.1
The challenge is that trade documentation exists in many different forms. These include digital, printed and handwritten, and cover a wide-ranging mix of formats, from structured to semi-structured to unstructured. For organizations in the oil and gas industry, this issue is compounded by the complexity of critical trading confirmations where one misplaced number or unforeseen title omission can cost millions of dollars.
“More importantly, teams require access to more data and the time to evaluate risks while also pursuing new revenue opportunities. Manually rekeying trade documents into systems is not only ineffective, but often fraught with errors and results in incomplete data.” – Rick Nelson, CEO, ClearDox
Existing approaches to automate confirmations have proven ineffective due to the broad variance in language combined with the many permutations of contracts. The inability to digitally process key elements, like knowing how to read delivery terminology or distinguish between fixed and floating terms regarding pricing, has remained an industry challenge.
ClearDox is announcing the expansion of its intelligent automation platform with a patent pending approach using Natural Language Processing (NLP) to address this critical imperative. In conjunction with our existing AI and Machine Learning (ML) capabilities, organizations will now be able to automate the processing of long form commodity contracts and confirmations to pinpoint and extract relevant information for improved digital consumption and sharing.
Recognizing that the complexity of these documents was too difficult to solve with one model, ClearDox developed a unique segmentation method using NLP to break these long-form confirmations down into their requisite sections, (i.e., delivery, pricing, quantity). This included stripping out any extraneous details that would reduce the model’s effectiveness.
“Unique NLP models are generated and run for each section intelligently extracting the appropriate data for processing. This overall approach simplifies the training while substantially increasing the level of accuracy.” – Marc Lefebvre, Chief Technology Officer, ClearDox
In addition, organizations will now be able to confidently automate the 3-way matching reconciliation process across this digitized data via our enhanced Reconciliation Manager to:
- Eliminate costly errors: Data is directly captured from source statements and validated against trading and financial systems.
- Drive process efficiencies: Speed adoption of straight through processing while streamlining exception handling.
- Improve resiliency: Rapidly onboard new suppliers, vendors, contracts and terms.
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