Mediaboss Marketing

Equipping Financial 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 companys accounts but also drive strategic performance. Such change in 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 to upskill on how advanced analytics, AI/ML techniques be leveraged to become an effective business partner driving performance in an organization.

Use-cases of AI/ML in Finance

Here a a number of use cases of how data science and ML techniques can be used in the business context to drive productivity and performance in the organization:

1)Identifying and preventing Revenue leakages: Revenue leakage is a major issue with many large enterprise and A/R leaders spent a substantial time and effort in preventing them. This could be due to multiple reasons viz. 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. Here, advanced analytics can play a significant role the root cause of such leakages and provide insights to the A/R team on actions that can be taken to prevent such instances.

For example, there have been instances where few customers use low dollar value deductions as a strategy to strengthen their cash flow. In such situations, it is difficult to track low-dollar value deductions as it is really a small number and is below the acceptable tolerance / threshold. This becomes a scenario of 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 seehow certaingroup 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 provide which customers are consistently using this strategy and can help the A/R team to go and recover them.

2)Identifying high risk customers and undertaking recommended actions for faster collection:For organizationshaving thousands of transactions across a large customer, it is really a difficult task to understand the behavior and financial stability of its customer due to which there are late payments or sometimes the receivables are written off. To avoid such scenarios, advanced classification algorithms can help detect such customer at risk and help organization to take pro-active steps to not only identify the customers but also reduce exposures to them over a period of time. In order to implement such smart solutions, it is really important to have the finance leader defining the key variables or data points needed to develop such classification algorithm which then the data scientist will use in its modelling. In other words, it needs a close co-operation between the finance leaders and Data scientist to model the key variables and scenarios.

3)Inventory Management: Inventory management is a major challenge in an 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 (SKU) 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)Improving Cash Conversion / working capital: One of the significant benefits of AI/ML is the optimization of cash conversion cycles by optimizing the management of receivables, inventory and payables. This, in turn, helps the company to perform well on the cash conversion and significantly improve its performance on accounts receivables.

5)Intelligent Root cause Analysis: The use of AI/ML offers profoundly important information on various business scenarios that could possibly spring in the future as a result of changing business environments. Whether it is the use of predictive analysis, scenario modeling, or descriptive root cause, AI/ML can help financial controllers in understanding the main reasons why some of the product gained immense popularity while others fail to find favor with consumers.

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, financial controllers should opt for holistic and comprehensive solutions so that the benefits of AI/ML solutions can be realized in a holistic manner.

Views expressed above are the author's own.

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Read more here:
How advanced analytics and machine learning are transforming the role of Finance Controllers - Times of India