Effectively managing cash flow is one of the most critical skills business treasury leaders must possess to effectively contribute to their organizations’ growth. No amount of comprehensive strategic planning can compensate for the lack of a cash flow forecast that enables you to anticipate cash needs and borrowing levels. Due to its significance, the use case for AI in cash forecasting has become a trending topic to help enhance efficiency and accuracy in building predictive models.
Accurate cash forecasting is a top priority for corporate treasurers as it helps to drive informed decisions, manage liquidity effectively, and mitigate financial risks. But it is also a priority because this key process remains generally highly manual, typically built in spreadsheets. They are complex and take time to build, and even more time to then stress test results.
However, advancements in artificial intelligence (AI) and machine learning (ML) in recent years has created a transformative opportunity to automate the cash forecasting process.
In this blog post, you’ll learn how AI can be leveraged to automate cash forecasting for finance professionals, without sacrificing accuracy.
AI Adoption is Growing in Financial Analysis
The traditional approach to treasury management, characterized by manual processes, is no longer sufficient in today’s complex and dynamic financial environment. As organizations grapple with the challenges of globalization, regulatory compliance, and economic uncertainty, the need for data-driven insights and predictive analytics has become paramount.
Finance leaders are increasingly adopting AI and machine learning to overcome these challenges. In fact, a survey from KPMG found that 65% of finance leaders are already using AI in their functions. Finance leaders who were surveyed listed the following key benefits they believe AI will provide:
- Increased efficiency and reduced staff burden
- More accurate and reliable data
- Better ability to find outliers
With the ability to analyze vast datasets and identify patterns and correlations, machine learning algorithms can generate accurate forecasts, optimize cash flow management, and enhance risk mitigation strategies. However, there is a lack of trust among practitioners in the accuracy of automated forecasting.
Is AI Advanced Enough to Replace Human Expertise?
While the potential benefits of machine learning in cash forecasting are undeniable, its application does not come without concern. One of the primary concerns is the interpretability of machine learning models.
Unlike traditional statistical models, which are often transparent and easy enough to interpret, machine learning models don’t provide insight into the variables these forecasts are built on.
This lack of transparency can make it challenging for treasury professionals to trust the predictions generated by machine learning algorithms and make informed decisions based on them. In fact, finance leaders listed these issues as top concerns for AI and Generative AI:
- AI concerns center on the transparency of models and data privacy while GenAI concerns are focused on accuracy, data management and bias.
- The main barriers to AI adoption are concerns about reliance on algorithms, the pace of changing regulatory guidelines and data quality.
So is AI advanced enough to simply feed it data to generate forecasts that can be relied upon for decision making? While ML algorithms can uncover patterns and relationships in data, blindly applying them without domain expertise can lead to unreliable forecasts. Taking a nuanced approach, that integrates the capabilities of ML with the subject matter expertise of practitioners, is the most effective way to strike a balance between automation and human insight.
As Joseph Drambarean, CTO at Trovata, said in our recent episode of Fintech Corner:
“Don’t trust anyone that says machine learning will solve your problems. And I guess that it’s a very simple reason for why, right? If there’s any software, if there’s any homepage of a website that says “our machine learning AI will predict the outcome of your business”, they’re wrong. There’s no replacing the human operator.”
Treasurers that can learn to copilot cash forecasting alongside automation will be better positioned to enhance efficiency while delivering meaningful insights. AI and ML offer powerful tools for analyzing vast datasets and identifying patterns, but should be leveraged to complement human expertise. Finance professionals can work alongside AI algorithms to refine forecasts, test assumptions, and make informed decisions.
The Essentials of Implementing AI in Cash Forecasting
Data Quality – Achieving synergy between automation and human expertise starts with ensuring that the data used to train machine learning models is accurate, reliable, and comprehensive.
Data Visualization Tools – Organizations must deploy user-friendly analytical tools and platforms that enable treasury professionals to access, analyze, and visualize data effectively.
Scalable and Flexible Tech – Cloud-native solutions with scalable computing resources also play a crucial role in enabling organizations to leverage machine learning effectively. By leveraging cloud infrastructure, organizations can access the computational resources and storage capacity needed to train and deploy machine learning models at scale.
In essence, treasurers seeking to modernize the cash forecasting process, while ensuring accuracy and reliability, need a platform that combines key technologies.
Next-Gen Cash Management Tech for Reliable Automated Forecasting
Bank APIs
Treasury management is riddled with manual processes. Building forecasting models starts with data aggregation and this is largely a very manual time-consuming process. Treasury practitioners can end up spending up to 10 hours a week logging into multiple bank portals to download data, then manually collate that data into a spreadsheet. If that isn’t enough, time is then spent on rigorous quality checks, before beginning the manual process of building reports and forecasts.
Modern cash management software removes this convoluted process and leverages banking APIs to connect your banking portal directly to a centralized platform, like Trovata, and provides a data feed that is updated in real time. This automated process removes the chance of errors vastly reducing financial risk and streamlines the entire process.
Data Visualization in a Single Source of Truth
Trovata’s dashboard offers treasurers a comprehensive, up-to-date view of the company’s cash position, facilitating data-driven strategic decisions. Machine learning then automates cash reporting and forecasting while providing customizable controls to ensure accuracy.
Trovata’s API gathers bank data from various portals, which machine learning then processes within Trovata, ensuring standardization and normalization for comprehensive viewing of accounts, balances, and transactions. Our platform allows treasury teams to set up tags that catalog transactions so you can properly organize data.
Cash inflows and outflows can be categorized into specific datasets, allowing your treasury team to efficiently search and filter transactions based on key vendors and institutions.
Moreover, these tags serve as valuable assets for accurately automating future cash reporting and forecasting endeavors. This level of automation is not just about speeding up processes; it’s about enhancing accuracy and reducing the margin for error.
AI-Powered Cash Forecasting to Help Drive Informed Decisions
With your data normalized through APIs and your transactions tagged and categorized to ensure data quality, you’re in prime position to start building predictive models.
With Trovata, you can run dozens of scenarios and timeline predictions to find the ones that make the most sense for your business. Here’s how:
- Machine learning models establish a forecast baseline, analyze historical trends, and increase the accuracy of your projections.
- You can input whatever variances you know are a part of your business to customize the model and algorithm to fit your specific needs.
- Run any number of scenarios to figure out your best financial forecast.
- Easily run separate forecasts at the subsidiary or business group level to make sure each piece of the puzzle contributes accurately to the overall forecast.
This comprehensive suite of cutting-edge technology empowers you to leverage the power of automation for speed and efficiency. But, most importantly provides a customizable tagging infrastructure to ensure accuracy. These solutions drive cohesion between the applications of AI in cash forecasting and human expertise.
Trovata’s automated cash forecasting system helps to eliminate much of the manual workflow in forecasting. Trovata allows its users to connect to their banks in minutes and provides access to built-in business intelligence tools to visualize, analyze, report, and reconcile cash flows.
Spend less time on manual updates and more time making deliberate business decisions. See Trovata’s forecasting in action – schedule a demo today!
