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Practical Considerations for Leveraging Machine Learning to Boost Financial Performance, Part II

June 22, 2017
by Terence Leung | Continued from Part I In carrying out Digital and Financial Transformation, it is prudent to evaluate what areas can be addressed very well by predictive analytics and state-of-the-art machine learning. I was pleasantly surprised when McKinsey Consulting published a report this year mentioning that Germany alone can raise its GDP “by 0.8% to 1.4% annually” by leveraging artificial intelligence (“Smartening up with artificial intelligence”). Across industries, in my observation, further automation in business support functions using artificial intelligence and machine learning seems to be an irreversible trend. For manufacturing oriented industries, AI-enhanced predictive maintenance and yield enhancement are gaining a lot of attention. What else are having higher likelihood to yield high benefits? What about business management? Fruitful Initiatives I am seeing the increasing openness for intelligent/predictive analytics and continuous monitoring for financial performance and risk (including regulatory, cyber and insider), for our customers in manufacturing (process and discrete), consumer package goods, media, banking, natural resources, and utility industries. As part of Finance Transformation, decision-makers in these companies are hungry for relevant information that will enable them to open new markets, refine processes and make better use of resources. They would like to monitor progress, balance risks and make adjustments along the way too. To reduce set-up time for these organizations and to enable them to gain momentum as quickly as possible, our philosophy is to enable them to leverage their existing investment in ERP, business intelligence and big data systems for the data. Our ability to do so is hinged upon our platform for deep integration and industrial scale continuous monitoring. Furthermore, we empower finance executives and business decision-makers to achieve more ambitious business goals for operations and business processes with intelligent scenarios, Balance Sheet of the Future™ and recommended actions, supported by machine learning. For example, executives can readily select strategic suppliers worldwide; consider currency effects in daily operations in Treasury and Procurement; and streamline Order-to-Cash, Procure-to-Pay and other processes worldwide, with unprecedented results. It is an exciting world! In the next conversations, I will introduce some essential elements that will speed up organizations’ performance improvement and Financial Transformation. A note about the author: Terence is designing analytics solutions to enable Pathlock Technologies to serve the increasing needs of customers on decision-making related to processes, operations and transformations. He was previously at Deloitte Consulting’s Finance, Operations and Strategy practice and at solution providers including i2 Technologies that optimize company performance and processes. He really enjoys interacting with industry practitioners on topics such as business models, value, technology and especially analytics.