Data-Driven Treasury Intelligence Eliminates ‘Spreadsheet Pandemonium’

In firms large and small, across all manner of verticals, spreadsheet pandemonium rages.

Raj Seshadri, president of data and services at Mastercard, told Karen Webster that to operate effectively, companies need high-quality data, collected and analyzed in real time, shared throughout the organization.

No easy task, given the sheer volume of back-office tasks that are still handled manually. Data remain siloed, noted Seshadri — which is especially in larger companies that have taken shape through several waves of mergers and acquisitions. Legacy processes and technology remain in place, and departments don’t talk to one another.

As a result, the purchasing team doesn’t know what the treasury team knows.

The fragmented data flows have had negative ripple effects up and down supply chains, Far-flung departments within an organization have, over the decades, embraced point solutions that, even if cloud based, have made it difficult to integrate data from suppliers and vendors. Those point solutions have been hobbled by the fact that they’re focused on gathering data rather than generating insights for users.

Optimally, Seshadri said, “viewing all the payment flows across suppliers, across commodities and across lines of business gives you real time insight that helps liquidity and cash management goals.”

That visibility is urgently needed as an overwhelming majority of treasury professionals — more than 90% as surveyed by PYMNTS and Deluxe — said they still use Excel and similar programs to track spending and corporate performance.

The conversation came against the backdrop where Mastercard earlier this month launched Global Treasury Intelligence, a cloud-based platform designed to give businesses more visibility into how and where they spend money.

Automating Data Collection — and Cleaning it Too

In terms of functionality, the Global Treasury Intelligence Platform automates data ingestion from clients’ enterprise resource planning (ERP) systems to give customers a view of all payment flows across suppliers, commodities and lines of business, integrating this information with relevant third-party data.

At a high level, said Seshadri, the Mastercard offering integrates with ERP systems, in order to ingest information, fence it, organize it and mix it with third-party data to create insights that are shared among various users.

“It essentially takes in all of the information — and cleans it,” she told Webster. “Point solutions don’t do what this platform does.”

With that data in hand, she said, “you can get to efficiencies and effectiveness that can enable organizations to take actions in ways they couldn’t before — and reduces [operational] loss and leakage.”  Collaboration between departments becomes the norm, as there’s a shared understanding of objectives, actions and outcomes.

“The platform’s flexible,” said Seshadri — and depending on corporates’ needs, can replace existing client systems or run “parallel” to them — in a plug-and-play manner.

With granular insight into expenses — touching on everything from department to geography to whether the money’s being spent on office equipment, inventory or even ink — Mastercard’s clients can make better data-driven decisions, even when unplanned spending becomes necessary (life, as we’ve learned through the past few years, is full of surprises).

Better data visibility also means that companies can make sure that contracted terms with suppliers are satisfied – and even put  a stop to maverick spend, where employees might conceivably be buying goods or services from unapproved vendors.

Beyond the internal analysis that is improved as data silos are broken down and departments communicate more efficiently, there’s the value gleaned by applying machine learning and AI to gauge supplier risk, and even in serving a company’s larger ESG and governance goals. The platform, she told Webster, can help firms examine everything from carbon footprints to generating reports on the timeliness of payments to governments (such as in Australia, where that level of detail is mandatory), and improves supply chains’ financial health.

For Mastercard in particular, said Seshadri, there’s the expectation that the insights gleaned through the platform will help the payment network’s customers further embrace and use offerings such as Track BPS and Mastercard Instant Pay — solidifying the network’s ecosystem.

As she told Webster, of applying machine learning and AI to processes and supply chains, “there’s a lot of value to be realized – in terms of efficiency, effectiveness and, also, risk mitigation.”