How Machine Learning Can Transform The Financial Forecasting Process

Budgeting, Planning, and forecasting process is a key element in the financial performance management of an organisation. The process seeks to produce insightful and accurate future revenues, expenses, and cash flow. It equips a company with information that is useful in budgeting and strategic planning.

All top organisations use financial forecasting to identify and estimate future revenue and expenditure patterns. These fiscal trends usually have an immediate or long-term influence on company goals and objective strategies. This process makes use of historical data and financial records from accounts, sales, and external market or economic indicators.

Most companies have for long been using fiscal management tools that analyse past and current information and project future fiscal trends and conditions for a given period. The identified patterns help guide policies, pragmatic decision-making and cashflow management.

The Financial Forecasting Process

The Financial Forecasting process starts with defining assumptions and drivers of the forecast. After which you identify the methodology most appropriate to develop future outcomes over some time. Financial forecasting involves the preparation of statements or reports that contain estimates of the company's future fiscal outcomes. 

The process examines relevant records, market research findings, economic surveys, and industrial economic conditions. It looks into the current policies, laws, goals, and fundamental issues that impact financial decisions and the fiscal environment. 

The financial forecasting process aims to develop a fiscal qualitative model. The model consists of forecasts in the form of statements of financial position (or balance sheet), statements of changes in owners' equity or stockholders' equity, income, and cash flow statements. 

Financial forecasting helps management teams know whether the company is heading in the right direction. This information allows them to make necessary adjustments and develop strategic business plans. In most cases, financial forecasts are useful in budgeting and improving business performance management. 

Big Data and Other Challenges

As companies grow in terms of technology and customer-base, they usually face one challenge vis-à-vis big data. Traditional financial forecasting methods work very well with small data. A financial forecasting process that uses traditional methods to analyse big data consumes more time and resources.

Complex techniques require data, expertise, and effort to generate more accurate answers. When it comes to big datasets, you’ll need smarter and intelligent data science models to extract value from them.

Also, developing accurate and effective financial forecasts proves to be a challenge for most management teams. The best solution to help your finance team reduce noise and develop simple financial models is Machine Learning.

Machine Learning Solution

Machine Learning (ML) as a subset of AI (Artificial Intelligence) focuses on algorithms and statistical models. It is a field in computer science that gives computers or machines the ability to access data and learn without human intervention. Data scientists use ML as a tool to extract meaning and insights from massive data sets.

Leading Management teams and senior finance executives use ML to transform their Financial forecasting processes. Through ML, data analysts in the finance industry can now transition from the statistical background into the tech industry.

Machine Learning tools, techniques, and systems help forecasters and analysts to predict future trends and outcomes from a big set of historical data. Besides gaining deeper insights from the raw data, it allows them to focus on value-added activities that require judgment and decision-making.

Machine Learning Techniques and Tools

A well-implemented Machine Learning solution can be leveraged to automate the labour-intensive components of the financial forecasting process. ML can help remove or validate the impact of human bias on an organisation's financial forecast process.

Financial Forecasting with Machine Learning makes use of algorithms such as Quantile Regression Forest, Support Vector Regression, recurrent neural network, linear regression, logistic regression, clustering, random forest, and among others.

Finance Transformation

Machine Learning is currently one of the most effective tools for Predictive analytics. You can use ML techniques to analyse data and identify common financial patterns or trends that is the basis for building predictive models.

Machine Learning or predictive analytics can solve a range of simple and complex problems through fast and more accurate forecasts. It takes less time to collect data, perform descriptive or prescriptive analytics, identify patterns, and develop reliable financial predictions.

With Machine Learning solutions, you can manage data of varying complexity and transform it into actionable knowledge. It provides simple to use, robust and realistic models with high degrees of detail and precision. ML can also perform and Variance analysis, which is an investigative comparison between planned and actual numbers for labour, material or overheads.

Since the forecast should be regularly monitored and periodically updated, you will need machine learning to let the computers or machines do the work for you. Machine Learning responds quickly to changes in financial conditions and business environments. Frequently updated data makes the forecasts more accurate.

Discover machine learning to solve complex challenges using current evolutionary techniques. The experts at Neevista will help you select the appropriate machine learning approaches or techniques that suitable for your performance management goals and achieve true finance transformation.