AI in Treasury: Beyond Spreadsheets to Real-Time Strategy
Ditching spreadsheets for AI in treasury management is all about real-time data. But the implementation story is messier than it seems.
Many corporate treasuries are still stuck manual spreadsheets. That's a problem. In a world where market volatility is the norm, companies need real-time data to stay afloat.
Infosys' Ashish Kumar and IBS FinTech's CM Grover recently highlighted this gap. Despite being a leader in finance tech for nearly two decades, Grover notes that treasury departments still cling to Excel like it's a lifeline.
The Spreadsheet Problem
Let's face it, manually entering data from trading platforms like Bloomberg or Reuters into spreadsheets isn't just tedious. It's a liability. These manual processes create bottlenecks that slow things down. If your treasury team is stuck in this cycle, you're likely missing out on critical risk management opportunities.
Grover points out that companies need a smooth data flow from treasury management systems to their enterprise resource planning (ERP) platforms. That's the first step to getting a handle on liquidity and risk in real time.
Building the Right Foundation
Implementing AI isn't about flipping a switch. It's about building a solid data foundation. IBS FinTech has been doing this, integrating their treasury management systems with Oracle Cloud and other major platforms.
This integration allows for real-time data flow, eliminating the errors and delays caused by manual entries. Sounds like a no-brainer, right?
Here's where it gets practical: If your finance team is still using spreadsheets as their main tool, AI won't save you. You need to audit your current workflows and start integrating your systems.
The Future of Treasury in an Uncertain World
With global volatility on the rise, having real-time data isn't just nice. It's essential. Kumar believes that modernizing treasury management with AI is a way to build financial resilience. But, if your data is poor, AI initiatives will flop.
So what's the catch? Well, implementing these systems requires a commitment to overhaul existing workflows. It's not just about adopting new tech. It's about changing the way you operate.
In production, this looks different. There's more to deploying AI than just setting it up. The real test is always the edge cases. In an ever-shifting financial landscape, those edge cases could make or break you.