Four Ways To Improve Cash Application With Machine Learning

Gina McNamara

As CFOs operating in the digital age, we must apply new technology to improve old processes. Here in the financial offices of SAP, our shared services team in Singapore has been improving our processes through machine learning. This technology teaches accounting programs how to perform tasks without being programmed, using sophisticated algorithms to learn by analyzing enormous amounts of data.

One process we’ve improved with machine learning is cash application. Traditionally, accountants receivable teams would spend hours analyzing data to resolve discrepancies in digital payments. Our shared services team overcame this problem by applying machine learning to the task. Specifically, we developed and implemented cash application across our accounts receivable department.

In doing so, we discovered four key ways machine learning can improve the cash application process.

Reduce sales outstanding with accountant behavior analysis

Machine learning software can study the behavior of accountants and apply it to future payments. At the same time, it can improve arduous processes by analyzing areas of improvement. This can reduce your day’s sales outstanding, saving CFOs the pressure of a prolonged DSO.

In addition, AI’s ability to improve itself saves the time it would otherwise take to optimize processes.

Use automation to save human skills for more important aspects of the process

A question that I am asked over and over is: What will accountants do when they are replaced by machines? Accountants are highly skilled in many aspects of a business and often get buried by manual processes. Automation gives our teams the chance to become more integrated into our business, acting as a transformation agent to drive better business outcomes by enabling more time to partner and utilize the information that is analyzed.

I’ve referenced millennials in previous blogs. They have grown up with technology and are eager to bring their ideas to the table. Millennials are very hard to retain when you bring them into old/traditional environments.

Eliminate reprogramming for changes to the process

As the way we pay for goods and services evolves, so too should our cash application process. Machine learning is the most effective way to manage this evolution. To explore this, let’s use the evolution of digital payments as an example.

New forms of digital payment are being established at a dizzying rate. To retain customers, your company must keep up with each of them. Ordinarily, this would require extensive reprogramming of your accounts receivable system. Thankfully, machine learning programs can recognize new forms of payment and adjust their clearing accordingly.

Enhance decision-making with AI-driven insights

While it’s important to embrace risk as a CFO, it’s our responsibility to reduce it. To that end, imagine if you could simulate new clearing models without costly trials. Imagine if you could know for certain if suspicious invoicing was a sign of fraud.

Machine-learning programs can give your team access to advantageous insights by analyzing patterns and running predictive simulations. Instead of worrying about “what-ifs,” your team can work with confidence in their AI-driven insights.

Machine learning is one of the most effective ways to manage your cash application process. Explore SAP Cash Application today to accelerate this arduous accounts receivable process.

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Gina McNamara

About Gina McNamara

Gina McNamara is the CFO for SAP Australia and New Zealand, leading a team of approximately 40 staff across core finance, legal and contracts, facilities, purchasing, and information technology. She has been with SAP since May 2007. Before taking on the CFO position, Gina worked in Commercial Finance Business Support for SAP Australia and New Zealand, where she supported sales and consulting teams with revenue recognition and deal support. Gina is a strong advocate for demonstrating how SAP runs SAP and technology to improve operations for the office of the CFO, particularly around moving from an on-premise environment to the cloud.