How advanced analytics and machine learning are transforming the role of Finance Controllers

Analytics for Finance Controllers
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Equipping Finance Controllers with predictive capabilities, advanced analytics and ML will help them elevate their role from providing back-office support to business partnering. The role of a finance controller is changing. It is expected that controllers will not only take ownership of the company’s accounts but also drive strategic performance. Such change in the role is further accentuated with the explosion in the volume and variety of data available with an organization. Furthermore, data landscape in organizations is becoming more and more siloed, complex and distributed. Given this shift in business dynamics, it is becoming extremely important for businesses to empower their finance controllers with advanced analytics and AI/ML led solutions. This would enable them to become effective business partners, responsible for driving performance in an organization.
Controllership areas that are driven by advanced analytics
Here are a number of use cases of how data science and ML techniques can be used in the interest of the finance controllers to drive productivity and performance in an organization: 1) Identify and prevent revenue leakages: Revenue leakage is a major issue with many large enterprises and happens due to multiple reasons. Some of them being: a process issue with disjointed systems, poor experience of customer, disputes, invalid deductions by customer with a relatively high volume and low value, auto-approved write-offs etc. Preventing revenue leakage using advanced analytics A/R leaders spend a substantial time and effort in preventing the revenue leakages. Advanced analytics can, hereby, play a significant role in providing them the much-needed insights. They can find the root cause of such leakages and get insights on actions that can be taken to prevent such cases. Let’s take an example here. Suppose, there is a customer who uses low dollar value deductions as a strategy to strengthen its cash flow. In such a situation, it is difficult to track low-dollar value deductions as it can really be a small number and below the acceptable tolerance/ threshold. This would be like finding a needle in a haystack. However, when such deductions are aggregated at a customer level over a period of time, it can be truly amazing to see how certain group of customers are actually using this strategy to cause a significant cash flow leakage for the company. To track such events, there are advanced clustering algorithms which can tell about the customers who are consistently using this strategy. These insights can then help the A/R team to work on the revenue recovery in a strategic way. 2) Identify high risk customers and undertake recommended actions for faster collection: There are organizations that have thousands of transactions across a large customer. For them, it is really a difficult task to understand the behaviour and financial stability of the customer. This in turn, leads to late payments or sometimes written-off receivables. To deal with such scenarios, advanced classification algorithms can play a significant role. These can help detect such customers at risk. AI/ML techniques help the finance controllers to take pro-active steps to not only identify such customers but also reduce exposures to them over a period of time. 3) Manage inventory: Inventory management is another major challenge in any organization. There are different categories of inventory – finished goods, semi-finished goods, raw material etc. and within each of these categories, there could be different types viz. slow moving, fast moving etc. The use of AI/ML can help manage inventory by revealing insightful information about stock keeping units (SKUs) and their associated variables such as minimum order quantity, lead times, replenishment frequency, and safety stocks. Using predictive capabilities, advanced classification algorithms can help to keep the inventory issues such as supply mismanagement, deadstock, and wastage under strict control. 4) Gain significant efficiencies in cash conversion/ working capital: One of the significant benefits of AI/ML is the improved cash conversion cycles. This comes as a result of optimizing the management of receivables, inventory and payables. Advanced analytics techniques applied on working capital help the finance controllers perform well on the cash conversion and significantly improve the performance on accounts receivables. 5) Intelligent root cause analysis: The use of AI/ML offers profoundly important information on various business scenarios, that are either affecting the business or could possibly spring in the future as a result of changing business environments. Whether it is the use of predictive analysis, scenario modelling, or descriptive root cause, AI/ML can help financial controllers in understanding the main reasons why some of the products gained immense popularity while others failed to find favour with consumers. A close cooperation required between the finance leaders and the data scientists Now, in order to implement such smart solutions, it is really important to have the finance leader define the key variables or data points needed to develop these classification algorithms which then the data scientist will subsequently employ in its modelling. In other words, it needs a close co-operation between the finance leaders and data scientists to model the key variables and scenarios for AI/ML solutions in the controllership areas. Conclusion AI/ML and advanced analytics hold the power to disrupt the role of finance controllers There is little to doubt about the transformative power of AI/ML. These solutions can transform the role of financial controllers and can catapult their positions to one of strategic relevance to the company. That said, with a plethora of choices around, organizations should opt for holistic and comprehensive solutions for finance controllers, so that the benefits of AI/ML and advanced analytics can be realized in a holistic manner. The article was originally published in Times of India
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