Predictive analytics: The future of cash flow prediction

Making cash flow predictions is a crucial aspect of liquidity planning and working capital management. While firms have traditionally relied on spreadsheets to undertake cash flow forecasting, they are prone to errors in addition to being time-consuming.

The recent advances in big data, artificial intelligence, machine learning, and data analytics have thrown the doors open to unique ways of automating the process of cash flow forecasting. So, what is the future of cash flow prediction?

Cash flow prediction model

A cash flow prediction model is a mathematical model used by companies to conduct their cash flow forecasting exercises. The model takes historical data and other relevant variables into account to generate projections of cash inflows and cash outflows for a firm.

Generally, companies use time series-based projection techniques, which have been improved further with the deployment of machine learning algorithms in their prediction models. Predictive analytics in cash forecasting (PA-CF) is one such approach to generate accurate projections, enabling firms to stay ahead of the game.

Types of prediction models

Several types of prediction models are available to organizations to project cash flows. Below, we mention some of the most commonly used predictive modeling types and techniques.

• Decision tree: As the name suggests, a decision tree involves several branches that are indicative of a choice between different input variables and events and the corresponding potential outcome. This model’s simplicity, flexibility, adaptability, and ability to handle missing data make it a popular choice for forecasting.
• Regression modeling: Regression enables an estimation of the relationship between a dependent variable (cash flows) and independent variables, such as receivables, payables, and inventory management. It is an important tool for identifying the major patterns in large data sources.
• Time series analysis: By combining conventional data mining with projection techniques, time series analysis makes cash flow forecasts by analyzing the historical data collected at certain intervals over a specific period of time, such as daily, monthly, or yearly basis. It also accounts for seasonality, attitude changes, and market dynamics.
• Artificial neural network: Simulating a human brain, neural networks represent a network of artificial neurons capable of processing complex data and detecting plausible patterns. Its popular subtypes include cluster models and backpropagation.

Why predict future cash flows?

Predicting future cash flows is important in order to have a complete understanding of the cash on hand position of a business over a certain period of time. Exact estimates of cash flows help companies avoid stifling cash shortages that impede the pursuit of growth strategies while enabling them to earn additional returns on cash surpluses. It is typically performed by the firm’s finance team after acquiring inputs from multiple stakeholders.

Cash flow predictive analytics

Predictive analytics involves the application of stochastic mathematical techniques to historical financial information to generate future predictions of liquidity-related items—cash flows in this case. It standardises the underlying assumptions of your cash flow prediction model after capturing important trends from your previous years’ data.

Cash flow projections using predictive analytics may involve the deployment of additive regression, neural networks, or autoregressive models (ARIMA). The final choice of a cash flow prediction model would depend on factors such as company management, data availability, business model, cash flow frequency, and system complexity.

How do you use predictive analytics for cash flow prediction?

Predictive analytics, a subset of data mining, leverages a host of technologies, including artificial intelligence and machine learning, in conjunction with statistical and computational methods to create cash flow forecasts.

Cash flow prediction machine learning can be used to make projections about cash balance, operating cash flow, free cash flow, total cash flow, cash from sales, and more.

It analyses historical data to capture past trends and patterns, along with any seasonal discrepancies and extrapolates them to the current data for creating cash flow forecasts. Consequently, remedial steps can be taken to address concerns about imminent liquidity issues.

• Data mapping: Predictive cash flow analysis begins by setting parameters, such as the data aggregation level and the forecasting period, and feeding internal and external data sources into the model. The longer the data frame (3 years+), the more granular the analysis.

• Model key drivers: At this stage, additional information in the form of general factors, such as public holidays and seasonal factors, is keyed into the model. Other specific factors, namely payment deadlines, extraordinary events, the economic outlook, market risks, and trade restrictions, are also fed into the model. A huge benefit of predictive analysis is that conducting automated analysis on existing, cleaned data results in the pre-selection of an adequate cash flow prediction model.

• Deployment and optimisation: The model provides forecasts that inform liquidity planning, and necessary action is taken. In the event of discrepancies between forecasts and actuals, the model is further tweaked on a continual basis.

What are the benefits of using predictive analytics for cash flow prediction?

Predictive cash flow analysis outcompetes manually maintained spreadsheets by minimizing the margin for human error and the time consumed in updating the trackers. But that’s not all; leveraging real-time, extensive data, and cash flow predictive analysis makes for better insights and decision-making.

Below, we elaborate on several other benefits of using predictive analytics for cash prediction.

• Generates better forecasts: Prescriptive analysis identifies correlations between key performance indicators (KPIs) at a granular level by tapping into multiple datasets, including sales, capex, transactions, and other relevant customer information. This improves the forecasting process by miles, providing a holistic view of the company’s cash flow management.

To illustrate, cash flow predictive models analyze vendor behavior and payment patterns to provide better visibility into future cash inflows. They provide insights into the accounts that might require collection action. Firms can also use this information to ascertain the terms on which credit will be extended to their clients. Consequently, the collection process can be tweaked across all business units.

• Gain deep insights into cash flow drivers: Predictive analytics does not blindly offer projections out of its black box; instead, it also explains the reasons behind a particular cash flow prediction.

For instance, if the model’s results show a rise in working capital, it will state the reasons for it, such as aggressive collection policies or higher growth rates.

• Improves scenario analysis: Prescriptive analysis provides deeper insights into correlative relationships between KPIs, enabling firms to carry out superior what-if scenario analyzes where they can evaluate the impact of a certain course of action on their cash flows.

For example, if a cash flow analysis using machine learning predicts that the company will face liquidity issues if it pursues all of its capex and financing plans, the company can experiment with different scenarios involving capex reduction or better financing terms to prevent any working capital issues.

• Easy scalability: Unlike spreadsheets that are harder to handle as the company scales its operations, cash flow prediction models can easily accommodate more data (and time series!) without requiring significant adjustments.

To illustrate, if a firm plans to acquire a new company, the model can be expanded to consider changes in revenue streams, expenses, and debt management. It can also account for potential synergies, integration costs, and the impact of the acquisition on market dynamics.

• Customisable reporting: Prediction analytics-based cash forecasting models provide detailed, user-friendly reports that can be personalized per the target audience. This is because the firms can define the aggregation data levels for different groups.

For instance, the information required by a board of directors (BoD) and treasury managers will be vastly different as the former group is more concerned with overall cash flow management. At the same time, the latter needs to undertake operative action. Predictive cash models will render granular data per their specific requirements.

Who should use cash flow prediction models?

When cash flows are highly volatile or involve several outliers, predictive analytical cash models are unable to identify data trends adequately, yielding inappropriate results. As a result, these models are more suitable for companies with a broad clientele that results in steady cash flows without any heavy reliance on one-off payments.

Do you do cash flow forecasts in a business plan?

Financial projections, including cash flow forecasts, are a vital component of a business plan. In addition to describing their business goals, new products and services, marketing strategies, people policies, and market analysis, business plans also detail financial planning, including net income and cash flow projections for a couple of years.

What factors influence the accuracy of your cash flow projections?

Accurately predicting your future cash flows is largely a function of adequate data—at least, the last 3 years’ worth of historical data that factors in seasonality, cyclicality, and emergencies. Additionally, cash flow projections could go awry if the underlying sales and expenses assumptions go wrong, inventories build up due to changes in demand or market trends, and investments don’t pay off, hurting cash inflows into your bank account.

How can a company improve its future cash flow predictions?

To improve their cash flow forecasting, companies must ensure that they feed accurate data into their models and realign the assumptions based on evolving customer behavior and market trends. The models should be tweaked promptly whenever projections are very different from actual performance. Conducting scenario planning and sensitivity analysis also helps in better dealing with potential risks.

Cash flow prediction software - Agicap

Predicting future cash flows is a critical process undertaken by every business to ensure a healthy financial position and liquidity management. By deploying cash flow prediction software, firms can build a reliable, easily modifiable forecasting base that automatically pulls in data from their ERP system, simplifying the analysis of their cash position