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Predictive Analytics – vital tool in supporting finance leaders manage uncertainty

by Mike Jones in Other Cat 14/10/2020

Predictive analytics enhance financial processes by providing key insights into potential problems in a business and possible opportunities.

The pandemic has created a significant period of uncertainty, with many businesses being forced to rethink their strategies. The finance leader, along with the CEO will be the leading force in navigating a business through uncertain times and ensuring the company can continue with its plans. This is a challenging task and will require supportive information in the form of predictive analytics to determine the right path for the future.

The variations in time spent analysing data compared to conventional methods of number crunching greatly differ between nations. A study by Sage indicated that 50% of financial managers are spending more time on innovative data analysis. In contrast, nations such as South Africa, the figure stands at 64%. The report ‘CFO 3.0: Digital transformation beyond financial management” explored how predictive analytics technology could change how finance leaders operate at board level.

Where to begin

For businesses not applying predictive analytics, a good place to begin is assessing the existing state of financial systems and processes within the business. For example, has the business automated processes for reporting and generating financial information? Does the business have a modern integrated solution in place that enables quick access to financial information?

If the answer to this is now, then the finance leader should consider shifting away from inflexible legacy systems and manual platforms to adaptive, cloud-based solutions that provide real-time information and insights.

Providing finance teams with skills and experience

Finance departments and CFOs may need to utilise the skills and expertise outside of the finance market to enable the opportunities available within analytics. 

Prioritising automation for your business

Finance teams can enhance their analytics processes by applying a focus on automated data analytics. Automation provides an effective way of improving the quality of financial data by streamlining data preparation and aggregation.

Developing a culture of automation can enable higher productivity by reducing manual processes and potential errors, and speeding up processing times. Automation can enable faster decision-making processes while also improving regulatory compliance and improving the accuracy of financial information. Technology today can automate a number of standard reporting systems, as well as the development of dashboards. 

Enhancing processing

As business operations continue to change, finance teams can leverage data and analytics to enhance engagement with other businesses and manage overall performance. Finance leaders should focus on ensuring real-time data and analytics are available within stages of operational decisions, speeding up processes and reducing costs via automation.

The study by Sage makes it quite clear that where businesses lack any real cloud-based financial management tools, there is a general lack of strategic decisions in place. In contrast, predictive analytics creates an effective platform for finance processes, providing insights into potential problems in a business, as well as opportunities on offer. There are a number of ways finance leaders are utilising predictive analytics. This includes:

-Predicting revenue: marketing, sales, operation and user behaviour data enable finance teams to forecast revenue streams more accurately and predict future demand for particular products and services.

-Finance leaders are using predictive analytics to enhance efficiency in a number of creative ways, including ranking vendors in terms of fraud vulnerability, to assessing potential equipment failures.

-Financial leaders are using predictive analytics to assess potential trouble areas that may be reducing company revenue. For example, businesses can use predictive analytical modelling to measure indicators of customer loyalty, enabling action plans to identify issues and retain them.

-Fraud detection is something that many finance leaders are very keen to utilise by implementing analytics to discover and detect potential issues of fraud.