Predictive analytics in cash flow forecasting

Cash flow forecasting is critical for treasury departments as it helps them plan and manage their financial resources effectively. Traditionally, this has been done through manual processes, which can be time-consuming, error-prone, and may not provide the level of detail required. However, machine learning and predictive analytics have the potential to revolutionize cash flow forecasting by providing more accurate, relevant, and automated predictions. This article will examine how forecasting and predictive analytics work in practice to provide accurate cash flow forecasts.

What are predictive analytics?

According to the definition of the Harvard Business School, predictive analytics is the use of data in a way that can help to predict future trends and events using historical data. Predictive analytics can help forecast different scenarios that can help with decision-making. The same applies to cash flow forecasting; by analyzing past cash flow data and applying predictive analytics models, businesses can generate future scenarios to guide strategic decisions.

Beyond cash flow forecasting, forecasting and predictive analytics are widely used across industries. In entertainment and hospitality, they help determine staffing needs; in marketing, they enable behavioral targeting and sales trend forecasting to align campaigns accordingly. In manufacturing, they assist in predicting equipment malfunctions, while in healthcare, they support the early detection of allergic reactions.

How to get started with predictive analytics for cash flow forecasting?

To start a machine learning project for predictive analytics and cash flow forecasting, a treasury department can begin with a small prototype to prove the viability of this approach. This will provide valuable insights into the potential benefits and limitations of machine learning and help identify areas for improvement. With the correct data and the right approach, machine learning has the potential to revolutionize cash flow, providing more accurate, relevant, and automated predictions that can support better decision-making and financial planning.

In every successful project, the following steps need to be addressed:

• Define your goals and objectives: Determine what you hope to achieve with your cash flow forecasting project and set clear and measurable goals.
• Choose the right data: Select the most relevant data to your cash flow forecasting, and make sure it is in a format that can be used for machine learning.
• Choose the right method: Select the machine learning method that is best suited for your data and goals and takes trends and patterns into account.
• Train and evaluate your model (and avoid common errors): Use the training data to train your machine learning model and assess its performance using the testing data.
• Integrate the selected approach into your regular forecasting routines and automate the re-evaluation of the methods chosen based on the updated date at regular intervals. 

Benefits to be gained compared to manual cash flow forecasting:

• Increased accuracy: Machine learning models can analyze large amounts of data and identify patterns that are difficult for humans to see, leading to more accurate predictions.
• Faster processing speeds: Machine learning models can process large amounts of data quickly and efficiently, reducing the time it takes to produce cash flow forecasts.
• Increased automation: Machine learning models can automate many of the manual processes involved in cash flow forecasting, reducing the risk of errors and freeing up time for other tasks.

This approach can also be combined with manual forecasting, using machine learning to generate initial predictions that treasury departments can then review and adjust based on their expertise.

The key to predictive analytics for forecasting: historical data

To start with predictive analytics, you must identify which data is relevant for your cash flow forecasts to get meaningful insights. Using accurate data is essential, so data quality should be prioritized.

• Collect all available financial data: This includes past cashflows but also macroeconomic predictions of the future.
• Clean and pre-process the data: This involves removing missing or inconsistent data, normalizing the data to a standard format, and transforming the data into a format suitable for machine learning algorithms.
• Split the data into training and testing sets: This allows you to evaluate the accuracy of your machine-learning models and make improvements based on the results.

How to choose a suitable statistical model for predictive analytics?

Determining the correct statistical model for every forecasting category is essential to succeed with predictive analytics.

In Nomentia, we rely on eight statistical models for predictive analytics in cash flow forecasting. The eight statistical models are the following:

1. Bayesian Structural Time Series: A probabilistic time series model that uses Bayesian inference to estimate components like trends, seasonality, and regressors
2. Linear Regression Models: A fundamental statistical model that describes the relationship between a dependent variable and one or more independent variables using a linear equation.
3. Robust Linear Regression Models: A variation of linear regression that is less sensitive to outliers, often using methods like Huber loss or least absolute deviations (LAD) to improve stability.
4. TBATS (Trigonometric, Box-Cox, ARMA, Trend, Seasonal, ARIMA-based): An extension of ARIMA that handles complex seasonality, including multiple seasonal patterns and long seasonal cycles.
5. Feedforward Neural Networks: A type of artificial neural network where information moves in one direction (from input to output) through multiple layers used for pattern recognition and regression tasks.
6. Prophet: A forecasting model developed by Facebook that handles seasonality, holidays, and trends using an additive model, designed for business and economic time series.
7. Support Vector Machine: A supervised learning algorithm used for classification and regression, which finds the optimal hyperplane to separate data points while maximizing the margin.
8. XGBoost: A powerful machine learning algorithm based on gradient boosting, optimized for speed and performance, widely used in structured data problems for classification and regression.

Strengths and weaknesses of individual methods

• Linear regression: This is a simple and widely used method that is good for predicting a continuous variable. However, it may not always provide accurate results for complex or non-linear relationships.
• Artificial neural networks: These are powerful and flexible models that can handle a wide range of data types, but can be difficult to understand and interpret, and require large amounts of training data.
• Random forests: This is a type of decision tree that can handle non-linear relationships and handle missing data. However, it can be computationally expensive, and the results may not be easy to interpret.

Common errors to be avoided

• Overfitting: This occurs when the machine learning model is too complex and fits the training data too closely, which can lead to poor performance on new data.
• Underfitting: This occurs when the machine learning model is too simple and does not capture the underlying relationships in the data, which can also lead to poor performance.
• Ignoring data quality: This is a common pitfall in machine learning projects, where the focus is on the algorithm and not on the quality of the data. This can lead to poor results, even with the best machine-learning models.

To decide which model suits the best your specific orecasting and predictive analytics use case, it’s necessary to examine your data and identify which model(s) should be used. Once the ideal models have been selected, Nomentia calibrates them and uses the influencing factors to run a test forecast for the 12 months based on the historical data (data from the past three years is the minimum requirement for this). Once the test runs are completed, we can select the best model to work with in the future.

Which knowledge treasury can contribute?

Understanding the group’s business model is essential for developing predictive analytics for forecasting. Treasury teams should carefully consider the primary external and internal influencing factors that could impact the cash flow forecasts. Statistical methods can only deliver meaningful results when the analytics is done with the correct data set.

What are the general influencing factors?

General influencing factors can differ in every company and every industry—for example, public holidays – international and national impact the retail sector differently. The same applies to seasonal trends. For instance, holidays are more straightforward to model than seasonal trends.

Almost every company has some general influencing factors that can impact cash flow developments, and if there is available historical data, it’s possible to model them using predictive analytics.

Using Nomentia Predictive Analytics for forecasting, based on historical data, cash flows can be developed based on seasonality, trend shifts, or holidays.

What are the specific influencing factors?

Specific influencing factors impact only certain industries, and these factors should be considered when identifying the suitable statistical method for analyzing the data. For example, with Nomentia, it’s possible to model external factors specific to certain companies, such as using general economic data, e.g., foreign exchange rates sourced from market data providers. 

Ongoing optimization to improve cash forecasts

Running the first forecast using a predictive analytics model will be the basis for optimizing future projections. The calculations will become increasingly accurate over time as more and more historical data becomes available. Before creating any new cash forecasts, the system automatically checks whether the model selected is still the best option or whether another model would fit the scenario better based on the latest data. It’s possible to calibrate the model with additional or even better-influencing factors at any time to improve the predictions. 

Conclusions

While predictive analytics is complex, with an excellent solution, combining a suitable statistical model with high-quality data, treasury teams can get immense advantages from using predictive analytics for cash flow forecasting. Treasury teams can discover new insights that can improve decision-making, and the process can be automated to get the most recent predictions.

Incorporating machine learning into cash flow forecasting is a step towards a more efficient and effective financial management process for treasury departments of large corporations. By following the steps outlined above, avoiding common pitfalls, and combining machine learning with manual forecasting, treasury departments can reap the benefits of this innovative approach and support better decision-making and financial planning.