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Data Extraction

How Machine Learning Is Changing Accounts Payable Invoice Processing for Third-Party Logistics Companies

One of the biggest challenges of invoice processing for logistics companies concerns the time-consuming, in-house processes that require multiple users to manually extract and enter data into various internal software systems such as CargoWise. 

In these times of great economic uncertainty amidst all the COVID chaos, it’s more important than ever to maintain strong supplier-customer relationships and keep cash flowing throughout the supply chain. 

All invoices need to be received, reviewed and approved before payments can be made and the businesses you work with simply don’t have the breathing room to accommodate lengthy delays – and your survival is dependent on theirs. 

Similarly, in-house, you don’t want to be dedicating already-stretched resources to deal with a mounting backlog of exceptions, disputes and inaccuracies.

Automated machine learning invoice processing and data capture is the solution. 

Automation Boom

Invoice processing can easily become disorganised – even under normal operating conditions, let alone with new remote working policies, distributed teams, furloughs and layoffs to contend with. The beauty of automated accounts payable invoice processing is that it brings order to the chaos – precisely what’s needed at this moment in time. 

A report from PwC finds that almost half of organisations (48%) expect to accelerate automation in response to the crisis – a trend that is forecast to continue even when employees return to the office. 

The reason, as pointed out by Arun Sundararajan, an NYU Stern School of Business professor in a recent Protocol article about the COVID-19-driven automation boom, is that a “crisis can be sort of a catalyst or can speed up changes that are on the way – it almost can serve as an accelerant.”

Mark Muro, a Senior Fellow and Policy Director who researches automation at the Brookings Institution, concurs: “There’s a lay view that automation might slow because the technology is expensive and firms would be hesitant to make capital investments,” he says – adding, crucially, that this view is wrong. “Economic literature over the last decade shows that these investments are made especially during a crisis.” 

Machine Learning led Invoice Processing from Shipamax 

At a time when improving efficiencies and keeping customers happy is of hugely critical importance, manually logging AP invoices is costly, time-consuming and as prone as ever, to human error. 

Automated invoice processing solutions can solve these problems, are easy to implement and offer a quick and substantial return on investment. 

Machine learning led invoice processing technology from Shipamax eliminates the need for staff to conduct labour-intensive manual data entry processes. 

Our technology connects directly to any email inbox or unstructured data source and automatically extracts data from emails and attachments in real-time, outputting a clean, structured feed that is pushed directly into your ERP or TMS system – without any manual intervention whatsoever. 

It has an extremely high degree of accuracy (up to 99%) and can reduce manual data entry costs by more than 80% – benefits that will last long after the current crisis has passed. 

What’s more, there are no implementation fees, easy onboarding and a consumption-based pricing model that can be scaled up and down according to your business needs.

Specialised for the logistics industry, why not join our monthly 15 minute live group demo for more information on our industry leading plug-and-play machine learning invoice processing and document automation platform

Josh BradleyVP Demand Generation
June 2020
4 min read
  • Machine Learning
  • Data Extraction
  • Freight Forwarders
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