How AI And Machine Learning Can Support The CFO’s Digital Evolution

277626 277626 h ergb s gl e1498158614248 How AI And Machine Learning Can Support The CFO’s Digital Evolution

Digital transformation’s prevalence in everything we read is a signifier that the way we live and work is going to continue to evolve over the next decade. In this blog, I want to highlight a few use cases showing how disruptive technologies such as artificial intelligence (AI) and machine learning will be used in the office of the CFO to increase productivity, simplify processes, and support decision-making.

Analytics: Since I have a background in financial planning and analysis (FP&A), I have been thinking about how digital assistants could impact analytics. Today, almost everybody in FP&A receives countless calls asking for information like, “What was our revenue in Q3 last year for this product? What has our growth been over the last three years for this line of business?”

Smart assistants like Amazon’s Alexa and Apple’s Siri can already answer questions on weather forecasts, stock quotes, and so forth, but what if they could provide the latest financial results and give decision makers instant access to information? A CFO could have a conversation with his or her ERP system using a digital assistant to get an immediate response or a clarifying question, without having to open a dashboard or dig into a database.

Risk assessments: When we assess commercial proposals for our services projects, we evaluate each project individually based on the customer characteristics – maturity, industry, size, current system landscape, and so on – as well as the complexity of the products to be implemented. To qualify this assessment, we depend on managers who have previously worked on similar projects. That can limit us to the individual perspective of those managers.

Machine learning could give finance teams the power to access decades worth of projects, around the world, at the touch of a button. In levering these insights, teams could then develop a better-informed risk assessment, mapping the project against a much larger database of historical projects.

Invoice clearing: In finance departments today, accounts receivable or treasury clerks can often be challenged in clearing invoice payments, as customers often combine invoices in one payment, pay incorrect amounts, or forget to include invoice numbers with their payments. To clear the invoice, the employee then has two options: manually add up various invoices that could possibly match the payment amount, or reach out to the customer to clarify. In the case of short payment, the employee either has to ask for approvals to accept the short payment or request the remaining amount from the customer.

What if an intelligent system could help streamline this process by suggesting invoices in real time that might match the paid amount and, based on established thresholds, automatically clear the short payments or automatically generate a delta invoice?

Expense-claim auditing: Expense-claim auditing is another routine, transactional finance task. Finance teams are tasked with ensuring that receipts are genuine, match claimed amounts, and are in line with company policy. While state-of-the-art travel-and-expense solutions can simplify the process, a manual audit still needs to be performed.

Machine learning and AI technologies could improve this process, auditing 100% of all claims, and sending only questionable claims to a manager for approval.  The machine could read receipts – regardless of language –  to ensure that they are genuine, and match them against the policy.

Accruals: Artificial intelligence and machine learning also offer promise when it comes to determining bonus accruals. Today, teams have a myriad of factors to consider when determining bonus accruals. CFO teams look at current headcount salaries and bonus plans, and try to forecast all KPIs in compensation plans. From there, teams try to calculate the most accurate accrual (likely adding a buffer, to be safe). However, oftentimes, accuracy ends of being a matter of luck more than anything else.

By applying machine learning to these calculations, predictive analytics could serve as a valuable tool to generate unbiased accrual figures, leaving finance teams more time during closing periods for other activities that require human review and judgment.

The overall impact on jobs in finance: As these advanced technologies continue to penetrate the finance function, a new crop of skills are rising to the forefront when it comes to hiring finance talent. Routine, transactional roles will become less prevalent, while the need for strategic thinkers with cross-functional knowledge and technology prowess will be critical. Additionally, while transactional tasks will be fewer, digital transformation will require additional finance resources to be developed and supported, creating an opportunity to redefine processes and roles.

As with the digitalization of the economy this change in inevitable, we at SAP are trying to anticipate the impact, and are striving to develop a learning culture so that employees can stay ahead of what is coming. I can only advise my CFO peers to educate and prepare themselves on this topic – and don’t leave it to your IT team.

Learn more

Automation is the priority for global business organizations that want to drive costs down. Join Randy Garrison of SAP and Weston Jones of Ernst & Young, LLP on July 17 at 1 p.m. EDT to understand what robotics process automation can offer you today and in the future. Register now!

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