Innovation needs to be applied wherever there are pain points.
This is especially true when it comes to corporate finance functions, where the technology stack is becoming more sophisticated than ever before.
Today’s finance professionals must become more agile in their decision-making. Claudia Villasis WallrafHead of Data-Driven Finance Deutsche Banksaid PYMNTS.
“Companies need to adopt new technologies,” she said. “And by new technologies I don’t just mean adopting API connectivity, but also cloud capabilities and artificial intelligence.”
While financial management systems (TMS) and enterprise resource planning (ERP) systems have traditionally focused on operational tasks such as accounting and valuing financial instruments, a dynamic macro environment and continuous rate environment have changed the calculus around effective financial management, opening up new horizons of opportunity and investment for businesses.
Existing financial management programs often fall short in supporting the financial decisions needed to capture the growth opportunities associated with today’s environment.
As Villasis-Wallraff pointed out, onboarding a legal entity or bank account in a TMS requires cross-departmental effort and often leads to decisions based on incomplete information. This scenario highlights the need for modern treasurers to embrace new technologies that can provide comprehensive, real-time data and insights.
How AI is impacting the finance function
Yet for treasurers, more data doesn’t necessarily translate to better cash flow forecasting and decision-making, and that’s where AI comes in.
One of the most important applications of AI in finance is cash flow forecasting, specifically direct forecasting, explained Villasis-Wallraff, noting that by analyzing past behavior and market variables, AI-driven models can predict when a customer is likely to make a payment.
This capability goes beyond just prediction: AI can also help treasurers deploy recommendation models to make more efficient financing, hedging and investment decisions, she added. These models align with a company’s risk tolerance and policy parameters to provide customized recommendations that enhance the decision-making process.
But that’s not all AI can do. Villasis-Warraf cited another important, yet less discussed, use case: classifying banking transactions. AI can automatically identify transactions as operational or non-operational, payroll, tax, or vendor payments. This automation streamlines treasury operations and allows treasurers to focus on more strategic tasks.
The road ahead for finance teams
While AI offers great benefits, integrating it into finance workflows is not without challenges. One of the main hurdles is data quality and the engineering and technical resources to leverage that data. As Villasis-Wallraff said about data quality, companies need to realize that “garbage in, garbage out.”
Finance professionals will need to start structuring and collecting high-quality data to ensure accurate and reliable AI model outputs in the future. This data-driven approach is likely to require a cultural change within the organization, where both finance and technology teams will need to work together.
For AI to truly transform the finance function, C-level executives must recognize the potential return on investment these technologies can provide. Villasis-Wallraff explained that as interest rates change and instant payments become more prevalent, demands on finance teams will increase. Companies need to get ahead of this by investing in education and improving collaboration between technology and finance teams.
“Shareholders and C-suite executives will start to ask and demand more from finance teams,” she said, adding that going forward, the ability to create operational cash flow forecasts without manual intervention will be a game-changer for finance teams.
Villasis-Warrakh said Deutsche Bank is at the forefront of creating the future by working closely with clients to develop and implement AI-driven solutions for cash flow forecasting and deal classification.
