I recently had the opportunity to visit with long-time industry veteran Rick Nelson who is now CEO at ClearDox, a US-based provider of intelligent document processing (IDP) solutions. Rick joined the firm almost a year ago and, with his deep experience in the energy and commodities space, has been leading the company’s push into those markets.
As explained by Rick, the company’s ClearDox Spectrum solution helps their clients fully digitalize their businesses by providing the ability to read, consume and translate spreadsheet data, computer generated (digital) documents, handwritten documents, and hybrid documents (those that include handwritten and digitally produced data), and then enrich & integrate that data across their systems. The solution is available as either hosted (in any cloud) or on-premises.
And as with most digitalization efforts, the value proposition for using the ClearDox solution is improved processes efficiencies, reduction in costs associated with man-handling documents, and acceleration of business processes and decision making. This is made possible by rapidly and accurately capturing the myriad of data contained in documents that might have otherwise taken hours or days to manually input (with the attendant “fat finger” errors that inevitably occurs).
Though document digitization has been widely adopted in industries such as health care, commodity trading organizations have trailed behind in its use, primarily because when compared to government mandated coding in medicine, there is little standardization in energy and commodity trading. However, as Rick pointed out, the use cases for the ClearDox Spectrum solution in commodities are wide ranging and offer real-world benefits – including more rapid invoice reconciliation and approval, improved timeliness of inventory tracking/management/valuation via digitalization of handwritten truck tickets & bills of lading, accelerated deal confirmation reconciliation…and the list goes on.
The ability to address these various aspects of energy and commodity trading is made possible by the system’s AI/ML and natural language processing (NLP) capabilities which contextualizes extracted data into standardized and easily consumable data sets that can be integrated across various systems using ClearDox’s prebuilt adapters or the solution’s API.
Though their efforts to address the continuing “paper passing” in commodities are fairly recent, they have found solid success in the market and their growing customer list includes big-name companies like Gulf Oil and Freepoint Commodities along with several others. In fact, according to Rick, in 2021, they processed over 2 million documents and he expects that number to at least double in 2022.
ClearDox is not the first on the scene with document digitalization for commodities. When I asked him why ClearDox will succeed where others have failed to gain traction, he noted a key difference being that their solution does not rely on a network effect – customers don’t have to force their counterparties to conform to their standards and practices. “Our ClearDox Spectrum platform has been designed around our AI/ML and NLP engines to deal with unstructured documents and data. Since you’re not requiring inbound documents to fit standard formats, you can quickly and easy integrate the system into your current processes and gain real benefit almost immediately.” Rick further noted that their typical on-boarding/implementation effort is less than 3 calendar weeks from start to finish.
Though the practice of energy and commodity trading has grown more sophisticated over the years, these are still markets that rely on the passing of untold number of documents (both physical and electronic) between counterparties, service providers, regulators and other third parties. While everyone acknowledges the inefficiencies in these processes, they do and will continue to persist. As companies continue to move toward becoming fully digital enterprises, a solution like that offered by ClearDox will be required to reduce the process friction and inherent errors caused by man-handling the critical business data found in those documents.