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Leveraging Predictive Analytics for Finance: The why and how.

No one can capture and analyse data from the future. However, there is a way to predict the future using data from the past. It’s called predictive analytics, and organisations do it every day.

What is predictive analytics?

Predictive analytics can help CFO’s to use the existing data and identify trends for more accurate planning, forecasting and decision making. By using predictive analytics your organisation can predict outcomes, identify untapped opportunities, expose hidden risks, anticipate the future and act quickly.

Every company wants to see into the future. How much will a product sell next month or will the demand drop off? How much will the business have to spend on manufacturing, distribution and other overheads? Does the business have a “next best offer” for a product or an estimated revenue for a newly launched product? Predictive analytics techniques are used to help answer all these questions and to create a better understanding of possible variables to aid smarter decisions.

Gain valuable insights

One of the biggest developments in SAP’s Predictive Analysis Tool has been its integration with SAP HANA. HANA provides remarkable output possibilities by using complex and heavy algorithms run on the in-memory platform.

Big Data which is the large volume of unstructured data from data sources such as external financial reporting systems, RFID sensors, Twitter, Facebook and other social media, can now be used to its advantage by using this powerful tool to forecast future performance and drive strategic decision making.

There are several routine processes that can be improved or enhanced using predictive analytics, including:

  • Target more profitable customers: By analysing the customers it is possible to identify small customer segments which are highly profitable.
  • Cash forecasting: Cash flow management is a key aspect of business to plan its future cash requirements to avoid a liquidity crisis. Leveraging data insights, financial professionals can look at trends to identify slow payers, detect and address system issues and improve receivable management.
  • Detection of financial risks: Financial departments can leverage predictive analytics to establish baseline criteria that makes it easier to identify outliers before they can damage overall company performance.
  • Demand planning: Predictive analytics can be used to forecast the sales over a period determining the demand of the product. This will help reduce returns from the customer and scrapping of the product, increasing the profitability of the company.
  • Company performance risk management: Predictive analytics can also help finance professionals get a forecasted “sneak preview” into the financial mid-period to avoid surprises.
  • Receivables aging: Finance professionals can optimise receivables aging processes and collect overdue amounts faster by setting alerts when customers deviate from past payment patterns.