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Are Finance Leaders Beginning to Embrace AI-assisted Decision Making? Featured

Are Finance Leaders Beginning to Embrace AI-assisted Decision Making? depth of field photography of man playing chess

According to Deloitte and KPMG, more than two in three executives are “not comfortable” using data from advanced analytic systems, and instead prefer to make decisions based on their own intuition. But this state may be changing. A look at Prophix’s recent survey of over 200 chief financial officers, reveals that 82% are planning substantial technology upgrades to their financial planning and analysis (FP&A) processes this year. These include automated FP&A solutions, such as corporate performance management (CPM) software, which are transforming the way finance offices budget, plan and report. That in turn frees up finance teams to draw insights, faster, and play a larger role in strategic decisions. That’s a great start. But if companies truly intend to be data-driven in their decision making, artificial intelligence (AI) and machine learning must be part of that digital transformation.

Some examples of how AI is transforming Finance: 

Close the Books Faster and more Accurately with Anomaly Detection

AI can quickly surface anomalies and risky transactions with greater speed and accuracy than any human could effectively deliver. In fact, anomaly detection powered by machine learning is 10-30 times more effective at identifying risk than manual, rules-based processes. This is especially helpful to speed up processes like the financial close, where most companies will agree they spend too much time looking back and not enough time looking forward. Significant time is wasted during the close cycle trying to root out all of the anomalies in the transactional (GL, AP & AR) data in order to balance the books.

Using AI-powered Anomaly Detection allows finance to quickly audit 100% of their transactions and present them with a list and explanation of any and all anomalies and risks hiding within the data. This empowers finance teams to expedite the investigation and resolution of these transactions to achieve a clean set of data to close the books. With greater confidence in the financial data, leaders are able to dramatically shorten reporting windows and focus on developing more strategic insights that ultimately benefit the business.

A side benefit of leveraging machine learning for anomaly detection is in fraud detection. When running high-volume transactions through it, in essence one is pre-auditing transactions and identifying possible fraud. Anomalies and risk are identified and ranked by risk (High, Medium and Low) ratings, allowing the Office of Finance to focus only on the transactions that are flagged, adding peace of mind to every reporting cycle. Who wouldn’t want to close the books faster, more accurately and with greater assurance?

Trend Spotting and Insights

With the ever-increasing amount of data companies are generating today, it’s getting harder to manually gather, format and analyze that data to help drive decisions. Thanks to AI’s ability to quickly spot not-obvious relationships, patterns, correlations and trends in large volumes of data, finance teams that apply this technology are empowered with a deeper level of analysis and intel, which can be extremely valuable in today’s competitive and increasingly volatile business environment. With AI-based CPM solutions, teams can automatically visualize trends through time and generate insights for things such as variance reports or add the line-item narrative to a monthly report. Obviously, one gains a competitive advantage when data can be presented and acted on faster. Allowing technology to do the legwork to expedite important insights, identify trends and present variance analysis in near real-time increases the agility of finance leaders to act more quickly and drive additional value.

Automation Powered by Natural Language

Another significant and unique advantage AI-powered CPM solutions provide finance teams is the ability for users to have a conversation with the data, just as we interact with Siri and Alexa™ in our daily lives. Through the power of natural language processing (NLP) users can ask the CPM assistant to perform tasks like build or distribute a report and even find a file, saving valuable time. Having tasks automated is one (good) thing, but when presented with a result, NLP can also answer the obvious question – what drove a specific variance – all from a simple voice command.

Other examples of AI in finance can include predictive analytics whereby AI can help build a forecast faster, then apply anomaly detection to ensure the data used to create the forecast is free of anomalies, making baseline forecasts more accurate, faster. This provides the finance executive with convenience and ease of use, while also saving them precious time to focus on more strategic business matters.

Many finance professionals have wondered if AI would replace their jobs. That’s not the role or by-product of AI. The goal of AI is to streamline FP&A by trying to eliminate inefficient, manual processes, provide accurate insights faster and give the CFO and the finance team the agility it requires to face the rapidly changing challenges of today while setting them up for success tomorrow.

AI improves a finance professional’s job. Overburdened and working at 100% utilization or higher, it’s tough just keeping up with their ‘day to day.’ Much of their time is occupied by looking for, exporting, formatting and routing data and not enough time acting on data. We’ve seen research that demonstrates that ratio can be as high as 75/25. That’s not the way to leverage highly skilled and well-remunerated employees.

As we continue to face uncertain business climates, it’s even more essential for financial professionals to adopt innovative CPM tools powered by AI and machine learning. Finance teams that embrace AI and machine learning, rather than fear it, will find themselves in a greater position of strategic business performance and competitive advantage.


Wayne Slater, Director, Product Marketing, Prophix Software

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