Unleashing The Power Of Intelligent Finance: Exigence Beyond Intelligent Technologies

Matthias Grabellus

So I’m sitting on a Deutsche Bahn train and, while checking the railway’s app on my phone, I see I can check in online. It’s easy, just like checking into a flight using an app. The train staff won’t disturb me to verify my ticket so I can relax, and the app gives me some peace of mind by notifying me of any delays, even suggesting alternative routes if needed. I can enjoy the tranquility, watch the trees fly past the window, and let my mind wander.

How wonderful it would be if finance people could follow their work and be able to accept “no news is good news” – that as long as they aren’t receiving any alerts, everything is running smoothly? How incredible would it be if they could focus their energies on innovative business solutions – if they could steer their business via an app that is at once easy to use and smart enough to offer intelligent alternative recommendations for upcoming decisions?

Intelligent technologies

In the past, finance focused on running the business and executing operational tasks. But this isn’t what’s expected anymore. Now, finance is expected to deliver the necessary insights for informed, incisive decision-making. This requires time, but basic operational tasks can’t just be abandoned; they still need to be carried out. This makes the integration of business networks, simplified processes, and intelligent technologies essential. In a word, it means automation: Routine financial operational tasks can be automated by using machine learning.

And if standard software doesn’t enable this – for example, processes across heterogeneous systems – then intelligent robotic process automation offers additional automation potential. Nevertheless, each automation might create business situations that require the specific attention of a finance expert. To ensure swift resolution of critical issues, the software needs to proactively notify the user, provide a fast and easy-to-understand description, and, maybe most importantly, recommend a solution.

The insights delivered today are mostly backward-looking and reported based on actuals. But the past cannot be changed, and since we live in a fast-changing economic environment, business experts will increasingly need to make better predictions. Forward-looking simulations offer the possibility to base a decision on what will happen, not on what has already happened. Predictive accounting is based on the logic that one event follows another; the fact that a certain event has already occurred in a prior process indicates that a successor event in finance may reasonably follow.

For example, finance experts will be able to predict how much revenue will be made based on incoming sales-order data, even if no goods receipt or invoice is booked. Machine learning evaluates historical data to calculate a pattern that can be used to anticipate the future. Manual clearing tasks can be enriched with intelligent recommendation services or even automated completely.

Embedded intelligence and automation cases are attracting a great deal of attention, and I am fully convinced that they can maximize business value. Nevertheless, we need to think in even broader terms to strengthen our finance software for the future. My question is, what else makes software intelligent?

Intelligent enablers

To take software to the next digital age, a platform that fully supports these intelligent technologies is indispensable. This platform needs to run in-memory, which significantly increases the speed of operations and analytics, permitting users to move from a batch-oriented to a continuous, real-time close. To take full advantage of the in-memory technology, the data model needs to be simplified. Data redundancies must be drastically reduced, if not eliminated altogether. And all finance applications need to access the same repository. This allows a unique combination of transactional and analytical data, enabling finance experts to accelerate their decision-making based on data-driven insights at the point of action.

The value for finance organizations is substantial: Data consistency across finance applications, unlimited drill-down analysis on the fly, and reduced effort in error-handling during the closing ensure faster insights and simplified multi-book reporting. The need to deliver accurate, up-to-date financial information continuously is not limited to the level of an individual entity. Finance software must also enable organizations to run a continuous financial close on the group level. It needs to provide unprecedented transparency by delivering a complete flow of information collection, processing, analysis, and publication.

With all these strategic development priorities in mind, an app that helps finance experts better steer their business with less effort, thereby winning the flexibility to concentrate on value-adding tasks, will revolutionize their role. If they can rely on fast and flexible software, they will have the confidence to focus on strategic tasks. By combining emergent technologies and intelligent enablers, new potential in the finance department can be unlocked. Now, I ask you, fellow finance experts: What will revolutionize your work and unlock your potential?

For more on this topic, please read the “Continuous Accounting Action Plan” series.

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This article originally appeared on LinkedIn Pulse and is republished by permission.

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Matthias Grabellus

About Matthias Grabellus

Matthias Grabellus is head of Finance Software Development, a unit within SAP Product Engineering. Finance development innovates the core business of SAP to build the future finance portfolio for our customers. Before taking on this position in September 2016, Matthias was the head of the Finance Accounting Development, where he and his team developed SAP core business innovations such as the Universal Journal. The Universal Journal stands for one of the most important innovations for the SAP finance world in the last decade, influencing financial, management and asset accounting, as well as profitability analysis.