According to a recent survey, 31% of treasurers found that a significant constraint to cash forecasting was a lack of time and resources. When your team doesn’t have the proper resources, it can decrease team morale and confidence in data.
This is why it’s critical to stay in tune with the latest tools to help you and your team make more informed decisions. Enter machine learning and artificial intelligence.
UPDATE – MAY 11, 2023 – Trovata has launched the first generative AI tool for finance using OpenAI’s GPT-4 technology to automate cash reporting and business intelligence.
What Is Machine Learning and Artificial Intelligence?
Machine learning (ML) empowers computers to learn without explicit programming or intervention once initialized. ML algorithms analyze new bank data as it comes in to find patterns.
Artificial intelligence (AI) leverages machines to mimic human problem-solving capabilities. These algorithms make inferences based on analyzed data and provide recommendations.
Both machine learning and artificial intelligence work together to empower you and your financial team to:
- Categorize data and deliver it where & how it is needed
- Increase opportunities for scenario planning and risk assessment
- Create contingency plans that address economic trends
- Improve cash forecasting accuracy
- Obtain increased confidence in your decision-making
Top 5 Reasons To Use Machine Learning and Artificial Intelligence For Decision-Making
1. Categorize Data And Deliver It Where And How It Is Needed
Machine learning and artificial intelligence were developed to solve constraint issues within data analysis.
For finance teams, a common constraint is the manual practice of accessing balance and transaction data. Typically, teams that rely on spreadsheets still have to search through many bank portals and consolidate data into one common format.
There’s an inherent flaw to a manual data management approach: As hard as we try as financial professionals, we can’t eliminate unconscious biases. Manual data management and analysis often lead people to finesse data to fit their own conclusion.
Banking APIs take humans out of the data consolidation process. These APIs automate the consolidation and normalization of bank data, establishing a single source of truth.
This way, banking data is normalized into an unbiased format. Still, analysis needs to occur to transform data into insights.
That’s where machine learning algorithms are useful. ML empowers cash management platforms to incorporate advanced search tools. Depending on specific keywords and tags, the algorithms analyze your organization’s bank data in seconds and collate the most relevant transaction list into a data set.
2. Increase Opportunities For Scenario Planning and Risk Assessment
Scenario planning helps you understand the liquidity needed to keep operations running. It can help you identify where the ditches are on the road as your organization moves forward. You can ensure you have an adequate line of credit you can use when you need it.
Also, machine learning and artificial intelligence make generating a forecast baseline and other scenarios easier as they help spot trends over time. By implementing user-defined variables, such as a decrease in sales, ML and AI can analyze the data foundational to your forecast and generate new scenarios.
New scenarios can help you understand the risks associated with certain investments and market changes so you can make more informed decisions.
3. Create Contingency Plans That Address Economic Trends
To create contingency plans, you must understand operational and market threats that could affect cash flow, such as:
- Potential operational bottlenecks: These bottlenecks could involve logistic or revenue recognition issues
- Data inaccuracies: If your bank data is not up to date or accurate, you may experience the phenomenon we call GIGO (garbage in, garbage out). If you have garbage data crafting your cash forecast, your forecast is going to provide garbage insights
- Sudden market changes: As we experienced in the pandemic, industries and markets shutting down can have a dramatic impact on cash flow
Machine learning and artificial intelligence can help you formulate contingency plans by analyzing these factors’ effects on future cash flow.
4. Improve Cash Forecasting Accuracy
You can leverage machine learning to establish a forecast baseline. ML analyzes historical data to help forecast potential future results. This historical data, augmented with known events, can give a complete picture of your organization’s health.
Machine learning algorithms and artificial intelligence help spot trends to cyclically improve over time to make more accurate predictions for the future. This way, you can increase confidence in forecasting.
You can back up this confidence through variance analysis. Automated cash management platforms with variance analysis capabilities make it possible to determine your forecast’s accuracy for any given period. Knowing your accuracy rate can help you gauge whether you need to review your assumptions as time goes on.
5. Increase Confident Decision-Making
The bleeding edge of machine learning and artificial intelligence in forecasting is around the mutation of models. These algorithms are always reviewing and cleansing data for forecasting models. Your model can then react to anomalies in that data over time and start to predict more reliably given the input your team applies.
That’s really where the future of forecasting lies. Forecasting with these algorithms addresses that humans can work together with technology to continue to craft and evolve cash flow forecasting models. This is because they can process vastly more information than people can and do it in a way that respects the insights that the person is driving.
By having a guiding hand that increases forecasting accuracy, you can remain confident that you are making decisions with the right data.
Redefine Cash Forecasting with Trovata’s Machine Learning and Artificial Intelligence Technology
Trovata is built on performance and proprietary machine learning and artificial intelligence algorithms that take advantage of various forecasting viewpoints, allowing for models to grow and evolve based on your data’s DNA.