A Complete Guide on Variance Analysis

There are numerous applications of variance analysis such as:

• Comparing budget vs. actuals: Budget is an estimate of revenues and expenses for a fiscal year, whereas actuals reflect how much revenue has been actually generated or how much money has been paid out in expenditures at a given point in time during a fiscal year. Variance analysis helps in monitoring the budgeted numbers by measuring the deviation between them and the actual costs. The comparison is important for businesses to adjust their future cash forecasts based on the monthly reports’ numbers.
• Determining relationships between variables and correlated variables: Variance analysis aids in identifying correlations between variables. Correlations are important because analysts and investors use them for forecasting future trends and managing risks. It also helps in pricing derivatives such as stocks, bonds, interest rates, commodities, etc., and complex financial instruments. Correlations can be either positive or negative.
• Cash forecasting: Variance analysis is useful in cash forecasting. It helps identify factors such as seasonality and irregularities that might increase the variance and measure the accuracy in cash forecasts. By determining the accuracy and the causes of variances, the CFOs can shift their focus towards remedial actions to improve forecast accuracy and their potential impacts on the cash flow.

Usually, most companies perform variance analysis for business planning and meeting their financial commitments. The finance/ treasury team produces variance reports for certain types of variance and presents them to the treasurers. Some companies only focus on unfavorable variances, while others focus on both favorable (where actual costs are lower than the standard/ projected costs) and unfavorable variances (where actual costs are higher than the standard/ projected costs).

Common methods to compare variances are:

• Previous-year actual results vs. current year’s budget: Comparing these
  helps firms in budgeting.
• Existing budget for the current fiscal year vs. current-year actuals: Variance is compared at regular intervals throughout the year, such as quarter-end or year-end, to ensure that the firm can meet its financial obligations.
• Previous-year actuals vs. current-year actuals: These are compared at the year-end to measure the liquidity growth of a company.

This is a ready-to-use template to calculate variance.

According to the cash forecasting maturity model:

• Laggard firms often overlook variance analysis due to the lack of proper data or tools.
• Proactive firms perform it for a single duration at a global level.
• Strategic firms perform it for a single duration at an entity and cash category level.
• Best-in-class firms perform it for multiple durations at the entity, region, and cash category levels.

Disadvantages due to common methods of variance analysis:

• Poor timeliness: It takes time for your treasury team to perform variance analysis and send reports to the treasurers, which is time-consuming. Sometimes, the treasurers need feedback faster for making important decisions. As a result, variance analysis is performed on the spot or restricted for limited time periods.
• Improper variance sources: Most of the time, some variances aren’t captured in the accounting records. Thus the treasury team needs to track additional information such as labor routings, material bills, etc., for determining the causes for such variances to come up with an accurate analysis. The manual process often leads to delays and errors.
• Subpar budgeting: If budgeting isn’t done properly by considering all the factors, it will lead to a broader deviation between budget and actuals. Analyzing variances in such cases may not be so fruitful.

Given the advantages of variance analysis, it is essential to perform it as frequently as possible to increase the cash forecast accuracy and improve cash management.

The best practices for variance analysis are:

• Sales & Payments Data Analysis: This helps to build forecasts that are low in variance. A complete analysis of at least a year’s worth of sales and payments data helps treasurers identify customer behavior and payment patterns.
• Gain global cash flow visibility: It is useful to gain visibility into all the global enterprise cash flows. Due to better visibility, treasury is able to estimate the receipts and payments across various locations and categories.
• Make data-driven assumptions: Assumptions are important for cash forecasts and variance analysis since they help reduce cash buffers and debt. Treasurers can make informed and accurate assumptions by extrapolating receipts and payments from their historical analysis.
• Make adjustments to existing forecast templates or build newer ones: Once the assumptions are made by considering all the expected payments and receipts, the next step is to make adjustments to the existing forecast templates. This can be done by either revising formulae and adding more useful data into a forecast template. If there are no existing templates, the treasurers might need to build a new forecast template to predict future cash positions accurately.
• Revise forecasts regularly: After building a forecast template or revising an existing template, treasurers need to revise forecasts regularly. For regular revisions, a treasurer needs to compare the designed cash forecast with the actual cash flow, analyze the root causes behind variance, and revise the template for an accurate cash forecast.

Large enterprises usually have loads of cash flow data, making it difficult for treasurers to build low variance forecasts, especially with manual tools such as spreadsheets. The drawback of the manual methods of variance reduction is that they often result in variance with a range of 20-25% and consume a lot of time, effort, and resources. Due to the manual process, the forecasts generated might lose relevance by the time they are sent out to the CFOs since the actual cash position in the bank might be far lower than the projected cash position.

Thus treasurers need to look for robust technologies such as automation solutions to optimize their processes, such as Treasury Management System, Robotic Process Automation, Application Programming Interface, and Artificial intelligence.

Learn about the new treasury technologies and their role in finance management.

Reducing forecast variance with Artificial Intelligence:

AI helps generate low variance forecasts since it automates data gathering by integrating readily with most financial systems. Once the data is extracted, the solution performs a historical analysis and a regression analysis to understand the historical patterns associated with complex cash flow categories such as A/R and A/P and to understand the payment patterns. After the patterns are understood, the system picks an algorithm that can present an accurate cash forecast. To increase the accuracy of the cash forecast by 90-95%, the AI-based application further revises the forecast by:

1. Comparing the forecast to the actuals to check for variance

2. Checking if the AI-designed forecast aligns with the forecasts of other horizons such as monthly, quarterly & yearly

3. Checking if the forecast is accurate across various scenarios

4. Analyzing the accuracy of the cash forecast by doing a line item analysis across multiple horizons

5. Making tweaks into the algorithm through an AI-assisted review process

6. Fine-tune forecast model and enhance data as and when required

7. Rinse and repeat until the forecast accuracy reaches the desired level

AI-based cash forecasting solution supports drilling down into variances across various cash flow categories, geographies, and entity-level variances and performing a root cause analysis. It uses the closed-loop feedback model to reduce the variance eventually by analyzing the historical forecasts and understanding what caused the spike in variance, and improving accordingly. This helps CFOs to get actionable insights to make investment, borrowing, and funding decisions confidently.

About HighRadius:

HighRadius is a Fintech enterprise Software-as-a-Service (SaaS) company that leverages Artificial Intelligence-based Autonomous Systems to help 600+ industry-leading companies automate their Accounts Receivable and Treasury processes.

The HighRadius® Integrated Receivables platform reduces cycle times in your Order to Cash process through automation of receivables and payments processes across credit, electronic billing and payment processing, cash application, deductions, and collections. The HighRadius® RadiusOne A/R Suite offers a pocket-friendly platform for hundreds of midsized businesses to enable faster A/R processing and enhance their working capital. HighRadius® Treasury Management Applications help teams achieve touchless cash management and accurate cash forecasting.

Powered by the Rivana^TM Artificial Intelligence Engine and Freeda^TM Digital Assistant for Order to Cash teams, HighRadius enables teams to leverage machine learning to predict future outcomes and automate routine labor-intensive tasks.

Processing over $2.23 Trillion in receivables transactions annually, HighRadius solutions have a proven track record of optimizing cash flow, reducing days sales outstanding (DSO) and bad debt, and increasing operational efficiency so that companies may achieve strong ROI in just a few months. HighRadius is the industry’s most preferred solution for Accounts Receivable & Treasury and has been named a Leader by IDC MarketScape twice in a row.
To learn more, please visit www.highradius.com