Equipping financial controllers with predictive capabilities, advanced analytics, and ML will help them transition from back-office support to business partnership.
The role of the financial controller is changing. Controllers are expected to not only take ownership of corporate accounts, but also drive strategic performance. Such role shifting is further accentuated with the explosion in the volume and variety of data available within an organization. Additionally, the data landscape in organizations is becoming increasingly siled, complex and distributed. Given this shift in business dynamics, it becomes critically important to hone skills on how advanced analytics, AI/ML techniques can be leveraged to become an effective business partner that drives an organization’s performance.
Use cases of AI/ML in finance
Here are a number of use cases of how data science and ML techniques can be used in the business context to boost organizational productivity and performance:
1) Identify and prevent revenue leakage: Revenue leaks are a major problem for many large companies, and accounts receivable executives have spent a lot of time and effort preventing them. This can be due to several reasons viz. a process issue with disjointed systems, poor customer experience, disputes, invalid customer deductions with relatively high volume and low value, auto-approved write-offs, etc. Here, advanced analytics can play an important role in the root cause of these leaks and provide insights to the A/R team on actions that can be taken to prevent such occurrences.
For example, there have been instances where few clients have used low value deductions as a strategy to bolster their cash flow. In such situations, it is difficult to track low value deductions because it is really a small number and is below the acceptable tolerance/threshold. It becomes a needle in a haystack scenario. However, when such deductions are aggregated at a customer level over a period of time, it can be truly amazing how certain groups of customers actually use this strategy to cause a significant cash drain for the business. To track such events, there are advanced clustering algorithms that can tell which clients are regularly using this strategy and can help the A/R team go after them.
2) Identify high-risk customers and take recommended actions for faster collection: For organizations having thousands of transactions on a big client, it is really difficult to understand his client’s behavior and financial stability due to which there are late payments or sometimes receivables are written off. To avoid such scenarios, advanced classification algorithms can help detect these risky customers and help the organization take proactive steps to not only identify customers, but also reduce exposures to them over a period of time. time. In order to implement such smart solutions, it is really important for the finance manager to define the key variables or data points needed to develop such a classification algorithm which the data scientist will then use in their modeling. In other words, it takes close cooperation between finance managers and data scientists to model key variables and scenarios.
3) Inventory management: Inventory management is a major issue in an organization. There are different categories of inventory – finished goods, semi-finished goods, raw materials, etc. and within each of these categories there may be different types, viz. slow moving, fast moving, etc. Using AI/ML can help manage inventory by revealing relevant 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 keep inventory issues such as poor supply management, dead animals, and waste under tight control.
4) Improved cash/working capital conversion: 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 business perform well on cash conversion and significantly improve its performance on accounts receivable.
5) Intelligent Root Cause Analysis: The use of AI/ML offers extremely important insights into various business scenarios that could possibly arise in the future due to changing business environments. Whether it’s the use of predictive analytics, scenario modeling, or descriptive root cause, AI/ML can help financial controllers understand the top reasons why certain products have gained traction. immense popularity while others do not find favor with consumers.
There is little doubt about the transformative power of AI/ML. These solutions can transform the role of financial controllers and propel their positions towards a strategic position for the company. That said, with a plethora of choices, financial controllers should opt for holistic and comprehensive solutions so that the benefits of AI/ML solutions can be holistically realized.
The opinions expressed above are those of the author.
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