How to Integrate Machine Learning Solutions for Smarter Budgeting

As artificial intelligence (AI)-powered financial forecasting continues to revolutionize budgeting approaches while offering more accurate, dynamic insights into future financial performance, organizations are identifying the talent, processes, and training necessary to successfully implement solutions. Proper integration of machine learning (ML) tools facilitates historical data analysis and financial trends, offering a pioneering approach to forecasting and potentially uncovering up to 25 percent savings through optimized budgeting processes. By adopting AI-driven forecasting, companies enhance strategic planning while responding swiftly to market changes and accessing financial stability and progress that translates into significant growth.

How ML improves financial forecasting

Traditional financial forecasting simplified the process of looking at data manually from previous years, or even the most recent year, and projecting a growth or loss of a certain percentage. The sophistication of the technology and the profession led to more detailed forecasts with statistical analyses and predictive powers. ML has removed any ceiling on the potential of this practice. The only limit is the amount of available data a system can utilize in uncovering different metrics of profit and loss (P&L) lines, historical accuracy, and even the impact of internal and external events on forecasting and eventual performance.

With access to real-time insight, ML can uncover and recognize patterns, trends, and potential risk factors undetectable to humans, adding another layer of predictive capabilities for professionals to include in their forecasting repertoire. Utilizing advanced ML models enables forecasters to expand scenario planning to encompass a broader range of potential outcomes, aiding marketing decisions by predicting the bottom-line impacts of expenditures across various areas.

Impact of real-time data

 A classic example of ML’s impact is time series-based forecasting, which observes a series of events over any selected period at evenly spaced intervals. Forecasters can examine any type of data—sales figures, weather trends, operational cost, etc.—and ML will allow them to use the outputs in various ways that create competitive advantages in forecasting.

– Prediction of future trends. Companies can anticipate upticks in demand, price fluctuations, or other determining factors that facilitate proactive measures and decision-making.

– Risk analysis. By spotting potential trouble spots or unforeseen risk factors, ML encourages organizations to develop mitigation measures.

– Uncovering patterns or excluding outliers. A sequence of data points over an extended period reveals trends or established market movement patterns while also easing the identification of anomalies or inexplicable events that are unlikely to repeat and should be excluded from future strategic planning.

These forecasts are useful in some predictable fashions, including seasonality (the swell or downturn in business at a specific time of year, before holidays, or even on particular days of the week). It is even more helpful in areas that are challenging for humans to uncover without the aid of ML, such as fluctuations that follow certain events that do not occur regularly. For example, a company that sells luxury items might see an upturn in business months after the end of a recession or economic downturn.

Integration best practices and challenges

The decision to buy or build is the most critical consideration for integrating ML intofinancial forecasting. If buying is the preference, vendor selection is based on what the company hopes to accomplish. Many technology providers offer out-of-the-box machine learning solutions, but the decision to include planning tools or other specialized software may influence the organization’s direction. Other companies, however, may choose to build their models from scratch, which is more time-consuming and labor-intensive. In these cases, combining both approaches may ease the transition—purchasing a turnkey solution and learning from its capabilities and shortcomings to build the organization’s original program.

Organizations that struggle and ultimately fail to successfully incorporate ML often do so because of poor data quality. ML is relatively new to companies and the business world, and many organizations lack the high-quality data required to develop a program that provides accurate, dependable predictive analyses. Managing expectations in the early stages of the process includes expecting and preparing for setbacks while gradually building to a full integration of ML tools in financial forecasting.

Key performance indicators (KPIs) and real-world examples

Calculatingabsolute percent error on a year-over-year or other relevant basis helps organizations evaluate their forecasting strength while identifying areas for improving data. Many companies employ predictive intervals to determine an acceptable range for ML-aided projections and prepare an applicable margin of error.

Walmart has successfully integrated ML into many facets of its business, including simulations to analyze customer trends and potential issues before its biggest annual event, Black Friday. The company also used demand planning and forecasting via different channels (e-commerce, in-store purchases, etc.) to predict the number of units sold by store, channel, and region, resulting in a forecast almost twice as accurate as those performed without the aid of ML.

Machine-augmented planning allows professionals to use their experience and knowledge to adjust the starting points for the machine-generated forecasts. Companies that use advanced ML models can take advantage of this power to fine-tune and improve data outputs. Machine-augmented planning illustrates how humans and machines operate best when working together. Computerized forecasting will not replace professional knowledge but will enhance and accentuate its best qualities.    

As the new era of data-driven AI/ML-powered financial forecasting begins, the onus is on professionals and organizations to incorporate the latest technologies into their business models. AI and ML can enhance the already abundant capabilities of human capital to understand and interpret data while adding a forecasting element. Those organizations willing to embrace innovative technology and motivate their leaders to gradually incorporate those capabilities into their existing practices will realize considerable savings and ultimately greater stability in the ability to forecast their financial futures.

About the Author:

Abhishek Vyas is a product manager with 18 years of experience in enterprise planning, machine learning, generative AI, conversational AI, machine learning, and analytics. He specializes in engineering and product management disciplines and has broad-based experience in retail, e-commerce, banking, financial planning, and workforce planning. Abhishek holds a master’s degree in computer science from Symbiosis International University, Pune India. Connect with Abhishek at vyas.abhish@gmail.com.