4 Must-Know Best Practices for Enterprises to Reduce Variance in Cash Forecasts with AI

Introduction

One of the key tools at a corporate treasury leader’s disposal is variance analysis, which is a quantitative method for determining the gap between budget estimates and actuals. This analysis can help you identify potential risks and take proactive measures to mitigate them, as well as make informed decisions about changes required in business strategies.

In cash forecasting, variance refers to the difference between the forecasted cash flow for a given period and the actual cash flow that was generated. By analyzing these variances, you can gain insights into your organization’s financial performance and identify areas where improvements can be made.

Variance analysis involves comparing actual results to budgeted or forecasted results, and then analyzing the differences to determine the root causes of the variances. This can help you identify areas where spending may be higher than expected or revenue may be lower than anticipated. By understanding these variances, you can take corrective action to bring actual results in line with budgeted or forecasted results. Moreover, by leveraging variance analysis, you can gain greater visibility into your organization’s financial performance and take proactive measures to mitigate risks and improve results.

Traditional methods enterprises use for variance analysis

Here are some traditional methods of variance analysis:

Previous year actuals vs. current year actuals

Enterprises develop budgets in spreadsheets by comparing past year actuals to current year actuals.

Templated-based variance analysis

A spreadsheet variance analysis template is generally used to calculate the variances between forecasts and actuals.

Performed in single duration

Enterprises perform variance analysis for a single duration at a global level or an entity and cash category level according to their cash forecasting maturity model.

Bottlenecks in traditional variance analysis

Enterprises typically have a lot of cash flow data, which makes it challenging for treasurers to create low-variance forecasts, especially with manual methods like spreadsheets. The disadvantage of manual variance reduction approaches is that they frequently produce a variance between 20-25% and require a significant amount of time, effort, and money.

Here are some bottlenecks of the traditional variance analysis:

Inability to identify variance sources

There are different complex cash flow categories like A/R, A/P, CAPEX at regional and company levels. With the traditional procedure, determining the actual reason for the deviation is difficult. Moreover, sometimes some variances aren’t documented in the accounting records.

High variance and low accuracy

Large gaps between forecast and actuals result in erroneous forecasting, which can lead to significant cash buffers, which can lead to lower business investments or higher borrowing rates.

Time-consuming and error-prone process

It takes time for the treasury team to perform variance analysis and send reports to the treasurers. Sometimes treasurers require immediate feedback to make critical decisions. As a result, variance analysis is done on the spot or for short periods of time which are error-prone.

Best practices to get low forecast variances

Here are the following best practices for variance analysis:

Make adjustments to existing forecast templates

Existing forecast templates can be tweaked by changing formulas or adding more relevant data once the assumptions are made by considering all the expected payments and receipts.

Make data-driven assumptions

Treasurers need to extract historical data and make proactive assumptions based on extrapolating receipts and payments from their previous analysis to find the fundamental cause of the variance utilizing the AI cash forecasting model.

Create forecasts frequently across multiple time horizons

Frequent forecasting across multiple time horizons such as daily, weekly, monthly can aid in comprehending market movements such as raw material price variations, interest rate changes, and commodity rate fluctuations, as well as providing the clearest, most up-to-date picture of short-term cash balances and anticipated liquidity requirements.

Use the feedback loop model

AI feedback loop model allows the system to learn from past mistakes and understand variance drivers to improve performance. It supports reusing the AI model’s predicted outputs to train new versions of the model. Additionally, using AI cash forecasts enterprises can adjust their parameters to perform better in the future.

How to reduce variance for an enterprise with AI cash forecasts?

To spot, report, and fix the reasons for forecast variances, cash flow forecasting software drills down into forecast variances across complicated cash flow categories like A/R and A/P, regional, and company level. Additionally, AI further revises the forecast by evaluating historical and current forecasts and the high variance categories across various horizons such as monthly, quarterly, and yearly to raise the accuracy of the cash forecast by 90-95%.

Benefits of utilizing cash flow forecasting software

Low variance and high accuracy 

Enterprises achieve low variance and high accuracy by leveraging cash forecasting automation to spots, reporting, addressing the reasons for the deviations, and testing the accuracy of the current forecast.

Better liquidity management

AI-powered Cash Flow Forecasting Software identifies variance and learns from industry-wide seasonal fluctuations in order to fine-tune forecasting for better liquidity management.

Proactive decision making

Borrowing, investing, mergers and acquisitions, and working capital decisions can all be made with confidence using real-time cash flow forecasting software. Treasurers can make better financial decisions with the help of cash forecasting automation, which provides reliable information on cash requirements.

Full transparency at the global level

Stay ahead of the curve by improving cash repatriation, pooling, and hedging at a global level with retrospective variance numbers to validate the accuracy of the current forecast.