The more companies become digital and global, the more finance organizations need to step up their game. It’s just not enough to report numbers, pay the bills, and secure steady cash flow. They must also deliver self-explaining, reliable financial information that provides the full story of how the business is performing in real time. But all too often, many financial teams struggle with isolated business systems full of inconsistent—and sometimes conflicting—information.

Finance Positioned to Benefit from Machine Learning

From the perspective of Smitha Chowdavarapu, senior manager at Deloitte Consulting LLP, CFOs should consider process automation to serve the business more efficiently. Finance teams have access to such a massive volume of data that they are well-positioned to use digital technologies such as machine learning to analyze and interpret information more quickly and accurately.

Relieving the Pressure of Goods-Receipt and Invoice-Receipt Clearing

One critical area that can benefit from machine learning is goods-receipt (GR) and invoice-receipt (IR) clearing. During the Americas’ SAP Users’ Group (ASUG) webcast Making the Future of Finance Real with Machine Intelligence: A GR/IR Use Case, Chowdavarapu shared:

“Finance teams are under tremendous pressure to clear GR/IR by month or quarter end to represent the business’s liability correctly. If goods receipts are not consumed by invoices, liabilities are overstated—resulting in inaccurate financials for the organization. A mechanism to automate the clearing process could accelerate the entire close process—whether it’s at month’s end, quarter’s end, or year’s end.”

Automating the Triple Match: Purchase Orders, Goods Receipts, and Invoices

Even though GR/IR clearing is one of the most manual, time-intensive processes in finance, many organizations rarely have the time to determine a way to accelerate it. Finance organizations often spend an extraordinary amount of their resources sifting through data and compiling reports manually—all while pulling together purchase orders (POs), goods receipts, and invoices scattered across a variety of business applications. And the process is further complicated by stored data that may be erroneous, out of date, or incomplete.

Applying machine learning to this process allows finance teams to intelligently automate the matching of more than 75 percent of manual clearing items. “Even with machine learning, finance will still have to undergo semi-automated clearing, when there are exceptions that need to be reviewed and considered,” said Chowdavarapu. “Yet, the volume of that manual work is greatly reduced because POs, GRs, and IRs stay within the processing rules predefined by the business and from which machine learning operates. And it is this benefit that allows CFOs to take finance to the next level of strategic thinking.”

Four Ways Machine Learning Can Enhance Financial Clearing

Machine learning introduces four potential advantages that help not only accelerate, but also improve, GR/IR clearing:

  1. Faster identification of patterns in real-time analysis: Machine learning allows you to analyze your GR/IR clearing information much faster. Depending on the confidence level of the machine learning model, it can also derive insights automatically. Data is not consistent for most business processes, but the algorithm can learn the data more quickly than humans. The machine learning model can better identify patterns and any anomalies that may exist, allowing it to continually improve its prediction rate.
  1. Visibility into trends across periodic cycles: Machine learning equips you to pinpoint trends both large and small that may be hidden deep within your data and sitting undiscovered, due to human limitations. Machine learning also helps eliminate human error, improve the quality of your output, and bolster cybersecurity. For example, it can clear any missed POs, GRs, and invoices. Plus, it improves implementation and execution practices by finding and applying insights discovered about your company’s procurement processes and behaviors driven by data.
  1. The reduced burden of manual analysis: By using machine learning, you can free your finance teams from low-value, repetitive tasks so they can focus on strategic business needs. Machine learning can automate and prioritize routine decision-making to help achieve outcomes sooner. Bringing this level of automation to the GR/IR clearing process also improves accuracy by relying on common scenarios defined in the program. You’ll also see the process completed much faster than the average time it took to complete manually.
  1. Fast and efficient soft closings: When you can speed up periodic financial closings, you’ll be able to give business leaders and other decision-makers the insight they need to make optimal and complementary investments, or allow them to focus on the right partner, vendor, and supplier relationships. Period-end closings are more efficient when clearing takes place in real time and requires only minimal manual interference.

Shifting the Focus to High-Value Work

Machine learning presents a data-driven opportunity for higher efficiency, minimal errors, faster action, and better decision-making. This case can be made for finance, as well as the rest of the business ecosystem. But what makes the implementation of this technology unique to finance is knowing where and how to apply it.

Given the range of exception handling, regulatory requirements, and intense scrutiny that happens within the finance organization daily, some form of human intervention will always be required. Yet, even if the smallest task in a more-extensive process is automated, CFOs can free their talented staff from energy-consuming activities that bring little strategic benefit so they can deliver significant outcomes that can help shape the business.

Listen to our webcast Making the Future of Finance Real with Machine Intelligence: A GR/IR Use Case with Smitha Chowdavarapu, senior manager at Deloitte Consulting LLP to learn how machine learning can enhance your finance processes.