Should You Use Predictive Analytics For Financial Forecasting?

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Predictive analytics for financial forecasting has resulted in much discussion within the world of business planning and growth. However, which is right for your company? 

In order for companies to move forward, having an understanding of market progression can help them expand to the best of their potential. For example, tracking consumer behaviour can help businesses plan to meet changes in demand, etc. 

While there are many analytics models that can help organisations gain insight into their operations, there are two in particular that can help you get the valuable information your company needs to plan for its future. These are forecasting and predictive analytics. 

Let’s talk about whether you should be using predictive analytics for financial forecasting

  • Predictive analytics vs financial forecasting
  • How is predictive analysis used in finance?
  • The problem with using predictive analytics for financial forecasting
  • Why SMEs should do their own forecasting?

Predictive analytics vs financial forecasting

What is predictive analytics?

Predictive analytics is the prediction of future trends and events using historical data. This data is used to help drive strategic decisions by forecasting potential scenarios, whether in the near or long-term future. 

What is financial forecasting?

Financial forecasting is the process of making predictions about how your business will perform over the course of an extended period of time. These predictions are derived from all the activities your business undertakes and their impact on your financial position and business goals.  It uses both historical and present data to form estimates for the future and has great benefits for companies of any size. 


How is predictive analysis used in finance?

Using predictive analytics for budgeting and resource allocation

Predictive analytics technology uses data from multiple sources to identify patterns and trends that will help you identify if your budget is likely to deliver your desired ROI (return on investment). This is done by identifying patterns in historical data to advise the best possible resource allocations to achieve the desired outcomes. 

Forecasting revenue and cash flow using predictive analytics

Predictive analytics uses invoice data, past payment trends, cash position, and more to help you gain better visibility into cash inflows and outflows. It can help you better predict the timing of inflows and outflows, better plan for investments, the likelihood of customer payments, and more. 

Using predictive analytics to manage credit risk

Credit risk management apps that help score customers and identify their risk each time they make a credit purchase is generally powered by an AI engine. This type of predictive analytics collects information from sources like credit reports, market data, etc. to minimize payment risks and predict blocked orders based on customer payment history, limits, credit utilization, and more.

Working capital management using predictive analytics for accounts receivable

Insight into account receivable can provide you with timely insights into risk and receivables that may put a strain on your working capital. Predictive analytics can be used here to give you a snapshot of your ageing accounts, days sales outstanding, percentage overdue, and more. Here, companies use predictive analytics to classify accounts and predict available working capital.

Using predictive analytics for risk management 

Another use case for predictive analytics is to help with predicting the risk associated with different tasks. This is often done by classifying them according to the impact on the business and used to prevent potential fraud.

Predicting customer payments using predictive analytics

A common use case for predictive analytics is that of customer payment prediction. This generally allows you to track past payment trends and prioritise accounts to predict possible payment issues and find possible actions to take. 


The problem with using predictive analytics for financial forecasting

Using machine learning, historical data, and AI, predictive analytics has become more prevalent over the past few years. However, when it comes to solutions with complex capabilities, they come with their own set of complex challenges. 


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Often, if a company tries to take a traditional approach to this type of analytics, it will hit numerous roadblocks. For example, predictive analytics solutions are typically designed for those with a deeper understanding of statistical modelling, such as data scientists. While new predictive analytics solutions are emerging, many are still too complex for an everyday business owner or employee to leverage fully. 

Additionally, predictive analytics solutions have notoriously been noted as difficult to use. And when a solution is too difficult to use, fewer people will use it. Generally, when implementing this type of solution, many users need to migrate from existing solutions, and these tools are often difficult to scale and deploy. 

On the one hand, many would argue that as financial forecasting uses data from the past as well as the present to estimate potential future trends, that it is more accurate than predictive analysis. On the other hand, predictive analytics can be said to be more accurate due to its use of advanced analytics algorithms to leverage historical and current data.

When it comes to planning for your company’s financial future, however, nobody knows your business better than you do. Using last year’s numbers is no longer enough to see your way through today’s volatile market. When using predictive analytics, you cannot account for anomalies like heating, electrical hikes, once-off orders, currency exchange, etc. 

In today’s world, it takes intuition. It takes knowing your company, your target market, and a myriad of possible scenarios that you can plan for using forecasting tools like Brixx, which can integrate into your existing tools. 


Why SMEs should do their own forecasting

As a small business owner, doing your own forecasting is crucial as it not only allows you deeper insight into your current and future financial state, but it affords you the opportunity to gain a true understanding of your company from a granular level. This is simply not possible with predictive analytics.

By conducting regular financial forecasting, you can get a better handle on how and where your company can grow. You can plan for multiple scenarios based on information that algorithms simply cannot predict or plan for and save yourself plenty of time and valuable resources while doing so. 

By conducting your own financial forecasting, you are more likely to understand how you can plan to accomplish long-term growth goals and keep your company from going underwater. 

Luckily, there is a wealth of resources available online to help SMEs conduct effective and accurate financial forecasts. The Brixx Blog has a large collection of articles on financial forecasting, cash flow modelling, 3-way forecasting, and more to get you started. 


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