Predictive analytics knocks on CFO’s door


Posted on: March 21, 2019 by Robin Zondag

Organizations are becoming data-driven: they are maturing within their analytics capabilities. Moving beyond traditional business intelligence, organizations transform to predictive and even prescriptive analytics to support decision-making in real-time. Predictive analytics is already an inherited part of the primary business activities e.g. by predictive maintenance, real-time sales forecasting or supply chain optimization. Adoption of predictive and prescriptive analytics is moving toward supporting business activities more and more.

Limitations of the human mind in forecasting

Finance departments have the responsibility to forecast KPI’s and continuously control performance relative to outlook. It is business critical to get forecasts right for finance departments, as e.g. wrongly forecasted budgets and order entry pipelines will have large impact on operations and strategy within a business. Business knowledge and understanding are key components to create a financial forecast, thus setting out the direction of an organization. But the human mind is fallible. Research has shown that it can be hard for the human mind to grasp volatility, especially over longer time periods, implying that deviations in forecast are inevitable due to noise, trends and seasonality in data. After all, a human brain can only store and analyze certain amounts of data.

Knock, knock. Who’s there?

Where the human brain has trouble with storing, analyzing and interpreting large amounts of data, predictive analytics can assist financial managers in reducing deviations in forecasting. And it will. Instead forecasting techniques with machine learning can be utilized to produce robust financial predictions. Embracing predictive modelling techniques such as neural networks will qualify decision-making in terms of assessing project win-rates or budgeting accuracy, free up time from reporting and thus reduce cost. Finance departments can further enhance business value of predictive analytics by enabling intelligent automation of manual and time-demeaning processes. A few use cases could be optimization of accounts payables’ invoice processing by combining machine learning algorithms with a software robot or use of AI-driven detection of anomalies in cash flows to detect fraud to alert controllers.

Will CFO’s be replaced by algorithms?

definitely not! while predictive analytics and intelligent automation can improve forecast accuracy and reduce the amount of manual work, it cannot replace business acumen. predictive analytics remain a supporting tool to assist cfo’s and other finance managers in their decision-making process. leveraging intelligent robots will free up time in finance departments to focus on driving strategic initiatives, managing politics and facilitate innovation in the organization. predictive analytics can improve control and precision of planning if adopted in daily financial operations. It requires a mandate from the CFO or senior financial management to integrate various use cases of predictive analytics. But opening the door to predictive analytics will create more efficient ways working in financial departments and is a great step towards making your company more data-driven.

 

This article has been written in collaboration with my colleagues Andreas Bennedsen and Carline Nauta.

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About Robin Zondag

Global head of AI labs
Robin Zondag is the Global Head of Atos AI Labs, responsible for leading the development of AI business as part of the Google Cloud & Atos strategic partnership. He works with customers in the AI labs of London, Paris and Dallas to design new AI use cases and define their AI journey. With Atos, Google and their ecosystem Robin creates next generation business AI services. His aim is to make AI real for all businesses. Previously Robin was a Consulting Partner leading Advanced Analytics globally and Digital Transformation in Benelux & the Nordics. Strongly believing in the disruptive power of digital Robin Zondag works with clients to change the way they interact with customers throughout the customer lifecycle, to adapt their business models and to transform the way they run their business operations. He executes his job by cooperating with clients, Atos colleagues, Atos associates, large and small partners, and universities.

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