How to Build an Effective AI Cash Flow Model in Azure
An effective AI cash flow model, particularly in Azure can significantly enhance your business's financial health by providing accurate and timely insights into cash flow patterns. By leveraging predictive modelling techniques and technologies, businesses can predict future cash flows, manage liquidity, and make informed financial decisions.
We previously wrote about The Power of AI in Managing Cash Flow, where we introduced 3 scenarios where a model would prove useful:
- Predictive Modeling
- Scenario Planning
- AI Recommendation Engines: Smart Discounts for Faster Payments
This blog takes things a step further and outlines the key steps to building and deploying an effective AI cash flow model using Microsoft Azure. But first, let's take a look at what the right model can provide your organization.
Benefits of an AI Cash Flow Model
Enhanced Accuracy
AI-driven models can analyze vast amounts of historical financial data to forecast cash flows with higher accuracy than traditional methods. It can help find patterns that were not previously observed. This improved precision helps businesses anticipate cash shortages or surpluses, allowing for better financial planning.
Time Efficiency
Automating cash flow analysis with AI reduces the time and effort required for manual calculations. This efficiency allows finance teams to focus on strategic tasks rather than being bogged down by routine forecasting.
Risk Management
AI models can identify potential risks by analyzing trends and anomalies in financial data. Early detection of these risks enables businesses to take proactive measures to mitigate them, ensuring stability and continuity.
Resource Optimization
By providing clear insights into cash flow patterns, AI models help businesses optimize resource allocation. Companies can make informed decisions about investments, expenses, and other financial commitments based on accurate cash flow projections.
Scalability
AI models are scalable, meaning they can handle increasing amounts of data as your business grows. This scalability ensures that your cash flow forecasting remains robust and reliable, regardless of the size of your enterprise.
Building and Deploying an AI Cash Flow Model Using Azure
There are many tools and platforms to build these AI cash flow models, but as a certified Microsoft partner, we prefer the time-tested tools and methods available in Microsoft Azure.
Before building and deploying your Azure AI cash flow model, you will need the following:
- An Azure subscription
- Access to historical financial data
- Basic knowledge of data science and machine learning
- Familiarity with Machine Learning service like Microsoft Fabric’s Data Science Experience, Azure Machine Learning or Azure Databricks.
If you're not familiar with Azure Machine Learning services, you can visit Microsoft's official Azure Machine Learning documentation at [URL]. This resource provides comprehensive tutorials, guides, and reference materials to help you get started. Additionally, consider taking online courses or attending webinars offered by Microsoft Learn, which offers hands-on labs and practical experience with Azure ML services.
Here’s how you can get started building and deploying your own cash flow model.
1. Set the business goal for the modeling exercise
This step often gets overlooked for various reasons but can impact the project significantly. Understanding the objective using predictive analytics to forecast cash flows will lead to efficient delivery. Apart from the obvious reasons like ensuring this project goals align with the company’s overall strategic goals; other reasons might include that the client wants the ability to compare scenarios in the final solution. While developing the model from the start, these scenarios can be included into the code thereby reducing the need to add them later.
Having a clear understanding of business goals before initiating the development of a machine learning model is crucial for several reasons:
- Alignment with Strategic Objectives: Ensuring a model will address a very specific business need, with a business value element attached to it.
- Efficient Resource Allocation: Understanding the problem allows for focusing of efforts on those areas that impact desired outcomes.
- Improved Performance Measurement: Establishing specific goals that can track progress and model effectiveness.
2. Collect and Prepare Your Data
This step is crucial because the quality and accuracy of your data will directly impact the performance of your AI cash flow model on the other end. It will also set the scene in terms of what type of models are possible. Think of it as laying a strong foundation for a building; without it, everything else could crumble. For example, building something that uses neural networks requires many valid data points and not all companies always have an abundance of history available feeding the need to use different modelling techniques.
What happens more practically is often that companies might sit with one product or client that has abundant data, another that has reasonably enough and still others that are new with little usable information. The challenge is to build a solution that can work in all these scenarios.
You’ll want to collect historical financial data relevant to your cash flow analysis. This data should include:
- Invoiced dates and actual paid dates
- Accounts payable and accounts receivable
- Payment terms
- Client and Vendor details
You can pull this data from various sources such as your company's financial statements, accounting software (like Dynamics365 F&O or Business Central, or SAP). Ensure that the data is in a standardized format, and available in an analytics environment like Microsoft Fabric or Azure Databricks.
3. Choosing the Right AI Model
Choosing the right Machine Learning model is a crucial step in building an accurate and reliable cash flow forecasting model.
There are multiple default models available, including tree-based models like random forests and gradient boost, regression models, and ensemble models. Each model has its strengths and is designed to handle different types of data and forecasting scenarios.
Selecting the appropriate model involves evaluating these options based on their ability to handle data and their suitability for capturing the unique characteristics of your financial data.
An appropriate model not only enhances predictive accuracy but also ensures that the forecasts are robust and actionable. By choosing the right model, users can facilitate better financial decision-making and strategic planning, ultimately leading to more effective management of cash flows and resources.
Align the model to your goals
Aligning the right AI model to your specific goals requires a thorough understanding of both the nature of your data and the objectives you aim to achieve. Begin by clearly defining the problem you are trying to solve with cash flow forecasting. Are you focused on short-term liquidity management, long-term financial planning, or identifying potential cash shortages?
Assess your data's characteristics
Assess the characteristics of your financial data. Gradient boosting techniques, such as XGBoost, are particularly effective for structured data with complex, non-linear relationships between features and target variables. These models excel in handling datasets with varying feature interactions, missing values, and a mix of categorical and numerical data, making them versatile for diverse prediction tasks.
Ensemble models, which combine the strengths of multiple models, can provide robust predictions by capturing different data patterns and mitigating individual model weaknesses.
Consider the computational complexity and scalability of the models. Some models, like neural networks, may require significant computational resources and expertise, while others, like XGBoost, might be simpler to implement but provide less nuanced predictions.
Conduct a thorough AI model evaluation
Finally, conduct a comparative evaluation of various models using historical data and performance metrics. Metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) will help you quantify the model's predictive accuracy and reliability. By systematically aligning the model selection with your data's characteristics and your forecasting objectives, you ensure that the chosen model will effectively support your decision-making process.
Choosing the right AI model for your cash flow
Focus | Model Type | Data Characteristics | Examples | Benefits |
Invoice pay-date forecasting | Tree-based models (e.g., XGBoost) | Non-linear relationships | Forecasting, Customer churn prediction | Well-suited for data with non-linear trends |
Rebate recommendations | Filtering models (collaborative vs content-based) | User-item interaction patterns | Recommending products, pricing or services | Works well where there are feedback e.g., recommendation given resulting in additional sales, clicks or views. |
Scenario forecasting | Ensemble models | Different data patterns | Combines strengths of multiple models | Provides robust predictions |
4. Building the Model in Azure
Now comes the fun part.
- Create a new workspace in Microsoft Fabric, Azure ML or Azure Databricks.
- Connect your data to the workspace.
- Combine Azure DevOps for managing the project and performing CI/CD deployments.
- Use the data science environments in either of the chosen technologies to build and train your model.
- Evaluate the performance of your model using metrics such as mean absolute error (MAE) and root mean squared error (RMSE). Fine-tune the model to improve its accuracy.
5. Deploying the Model
Once your model is trained and validated, deploy it as a batch pipeline or as a real-time webservice endpoint. This allows you to integrate the model with your business applications and access its predictions in real-time.
- Register the trained model as an endpoint, or;
- Deploy the model inference code as a batch pipeline.
- Create an inference pipeline for the model to handle incoming data and generate predictions.
- Visualize the results as dashboards using Power BI.
6. Integrating the AI Cash flow Model with Your Business Applications
Integrate the deployed model with your existing business applications to automate cash flow forecasting. Use Azure Logic Apps or Azure Functions for example to connect the model's predictions to your financial systems. For example, if the cash flow model goal was to suggest rebates these rebates can be pushed back into the system of record for action.
Other actions could include using Power Automate to kick off workflows, sending notifications for review and more.
7. Monitoring and Maintenance
Monitoring the performance of the deployed model regularly to ensure its accuracy and reliability. Building automated scripts that constantly review the accuracy of the model is critical to ensure the models are still valid. Using MLFlow these metrics can be captured as part of the model re-training exercises that will happen on a periodic basis.
Key Considerations for Monitoring AI Models
1. Model Drift
Model drift occurs when the statistical properties of the target variable change over time, causing the model to become less accurate.
Detection: Regularly compare the model's predictions with actual results and monitor performance metrics like MAE and RMSE. Significant deviations may indicate model drift.
2. Data Quality
Poor quality data can lead to inaccurate predictions and unreliable models.
Detection: Implement data validation checks to ensure the input data is clean and consistent. Monitor for missing values, outliers, and anomalies in the data.
3. Overfitting
Overfitting happens when the model performs well on training data but poorly on new, unseen data.
Detection: Use cross-validation techniques to evaluate the model's performance on different subsets of the data. A wide gap between training and validation performance indicates overfitting.
4. Bias and Fairness
Explanation: AI models can inadvertently learn and perpetuate biases present in the training data.
Detection: Analyze model outputs across different demographic groups to ensure fairness. Use fairness metrics and tools to identify and mitigate biases.
5. Performance Degradation
Over time, the performance of AI models can degrade due to changes in the underlying data or environment.
Detection: Continuously monitor key performance indicators (KPIs) and set up alerts for significant drops in performance. Regularly retrain the model with updated data.
Monitoring the performance of the deployed model regularly is crucial to ensure its accuracy and reliability. Continuously update the model with new data to keep it relevant and accurate.
Conclusion
Building and deploying an AI cash flow model using Azure can transform your business's financial management by providing accurate, real-time insights into cash flow patterns. By following the outlined steps, you can leverage the power of AI to enhance accuracy, efficiency, and risk management in your financial operations. Embrace this technology to optimize resources and drive your business toward sustained growth and success.
Though you can build some of your own models, our pre-built AI models can get you most of the way there. Leveraging these models can save valuable time and resources, allowing you to focus on fine-tuning and customizing them to better suit your specific business needs.

Director of AI
As the Director of AI for OmniData, Hennie is a value-driven Thought Leader, Senior Manager and Product Owner with more than ten years’ leadership experience delivering real-time data analytics, predictive modelling, core decision management platforms and digital transformations for multi R-BN Tier 1 South African Retail, Commercial and Private Wealth Banks.
Hennie advises C-suite and senior business stakeholders and translate vision into world-class solutions capable of handling over three billion decisions and generating insights and quantifiable business benefits to drive revenue growth, customer satisfaction and profitability.