Energy companies/projects rely on financial forecasting to estimate revenues, costs, and profitability over a specified period. Effective forecasting enables informed decision making and feasibility assessment for project developers, investors, and stakeholders. Harnessing big data from numerous sources like weather, energy, consumer behavior, markets, and regulations, can significantly boost forecast accuracy. Conventional financial forecasting techniques based on periodic reporting and historical data lack the ability to anticipate future volatility risks, integrate multiple data sources, condense data, and generate forward-looking information. Big data deliver a ceaseless flow of structured and unstructured insights from various sources like IoT sensory data, online consumer behaviors, internal transactional records, and project management logs. AI algorithms enable us to discover intricate relationships among numerous data points. Integrating big data and AI improves financial forecasting, thereby accelerating the decision-making process.

Dear Md Mostafizur Rahman ,

Financial forecasting refers to a process that businesses use to predict future revenues, expenses, cash flow, and improve profitability. Much like weather forecasting, the process may appear to resemble gazing into a crystal ball and guessing your company’s financial trajectory. But there is no crystal ball, and the predictions are not guesses but rather the outputs of a sophisticated and often elegant algorithm.

Financial forecasting processes are tied to financial, historical and market data, which reflect and affect the company’s performance. The assumption is that, if nothing changes, then the future is predictable with some degree of certainty.

https://www.netsuite.com/portal/resource/articles/financial-management/financial-forecast-machine-learning.shtml

Regards,

Shafagat

Performing data mining for decision making based on estimated projections.