AI Powered Forecasting for Effective Business Finance Management

Effective financial management is imperative to the success and longevity of a company. When the business is new or relatively small, it’s usually possible to keep track of budgets, expenses and cashflow through spreadsheets and legacy ERP (Enterprise Resource Planning) systems. However, as businesses grow, it’s getting more challenging to manage financial processes and operations without important information being left out. At time, this leads to critical mistakes. This is one of the main reasons why business finance management software market has been growing extensively during the last years.

Introducing Business Finance Management Software Tools

Software tools for business finance management enable accounting departments and higher-level management to:

  • Have a holistic view of a business financial health.
  • Keep control of budgets from different departments, to avoid unplanned expenditure.
  • Monitor debt and arrange for timely payments for proper financial control.
  • Register expenses and forecast future ones, to build resilience and financial stability.
  • Track cash flow in micro and macro level, so that business stays afloat and prospers.

In recent years the big range of available options can make the process of selecting a business finance management software overwhelming. Choosing the right tool, depends on a company’s magnitude, processes, goals, and infrastructure. Nevertheless, as a rule of thumb business finance management software packages should be able to provide some of the following functionalities:

  • : Integrate budgets from different parts of the company (e.g., departments, business units, divisions), maintain historical data, allow for multiple templates that could better suit different types of budgets.
  • Cash flow management: Full control of expenses, investments and debt payment, insights on how certain functions affect business financial health,
  • Forecasting: Use budgeting history to forecast future expenses, simulate scenarios to predict possible budget changes, help decision makers to plan data-driven strategies.

From Time-series Forecasting to Machine Learning and Artificial Intelligence

Forecasting functionalities are usually a valuable add-on to Business Finance Management software tools. Most of the business forecasting software products use time-series (TS) data to analyse financial information, track the performance of existing forecasts, detect changes, and observe evolving patterns. Over the years, forecasting models have been providing more and more capabilities for critical data synthesis and identification of previously unnoticed interdependence.

Traditional, one-dimensional time-series forecasting is a tough skill to master. It requires the calibration of a single model to every time-series, which is then used to project the time-series into the future. To achieve proper data evaluation, the prior timestep outputs need to be fed as inputs to the next timestep, as well as arrange for covariates to be manually integrated. Therefore, the traditional forecasting models’ effectiveness relies heavily on subject matter knowledge and expertise on time-series modelling techniques.

The above listed short-comings can be overcome using advanced forecasting software tools that leverage deep learning methods to enable the automatic handling of multiple time-series and time-related structures, such as trends and seasonality. Machine learning algorithms (including deep learning) have proven to be more accurate and easier-to-build alternatives, when compared to the traditional time-series forecasting models. For instance, one of the market’s most efficient tools is the Deep Autoregressive model (DeepAR), which is among the built-in algorithm in popular environments like Amazon Sagemaker cloud-based, machine learning service. DeepAR is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN), such as Long Short-Term Memory (LSTM). The latter provide effective handling for input data comprised of sequences of observations. Based on its input dataset, these algorithms train a model that learns an approximation of this process and uses it to predict how the target time series evolves.

In practice, what differentiates DeepAR forecasting model from other similar products is:

  • : DeepAR handles a big volume of time-series data by using a single, universal model.
  • : It is possible to tune the time-series model to get the best possible results, without the need to understand the layering infrastructure or use special coding.
  • Out-of-the-box seasonal dependency integration: It is possible to introduce covariates and learning the trends and seasonality of similar time-series, by applying ready-built functions and minimal fine-tuning.
  • Cold-start forecast capability: DeepAR solutions generate forecasts for new time-series, similar to the ones used for the training process. Most importantly, they can do this without a need for large volumes of historical data.
  • Probabilistic forecasting: They can provide results as probability distributions in the form of Monte Carlo samples. This enables optimal decision making, even under the uncertainty of real-life scenarios,.

H2020 INFINITECH’s Machine Learning Developments for Advanced Financial Forecasting

INFINITECH’s rich pool of machine learning developments includes deep learning models for business finance management, including models for forecasting cashflows of Small Medium Enterprises (SMEs) and providing them with effective finance management recommendations. Specifically, two of the project partners (namely University of Piraeus Research Center and Bank Of Cyprus) have closely collaborated in the development of a business financial management application for SMEs. In this direction, they have integrated and used DeepAR, in order to build a reliable cashflow forecasting model, which can predict – in a probabilistic way – a company’s future outflows and inflows based on historical transactions. Although this model is applied on small-to-medium enterprises’ (SME) data, it can be modified, to serve more diverse cashflow prediction for effective personal and business financial management.

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