Machine Learning Applied to Profit Forecasting: a Practical Solution for Accountants and Consultants Serving Small and Medium-Sized Enterprises

Authors

DOI:

https://doi.org/10.9771/rcufba.v19i2.69667

Keywords:

profit forecasting, machine learning, predictive analytics

Abstract

This article presents a practical and accessible solution for profit forecasting in small and medium-sized enterprises (SMEs) using no-code Machine Learning tools. The proposal is intended for accountants, analysts, and consultants who seek to apply predictive intelligence using existing data — even when fragmented or incomplete. Through a structured and replicable pipeline, exemplified using platforms such as BigML and Google AutoML (Vertex AI), the study demonstrates how to organize and process financial data, integrating model-generated estimates with business rules that support decision-making. The model enables organizations to begin with simple spreadsheets and gradually advance their analytical maturity, promoting financial predictability even in environments with limited technological infrastructure. As a limitation, the study highlights the absence of longitudinal empirical validation in real operational contexts, which reinforces the importance of human supervision and managerial contextualization of the results. The proposal aligns with the principles of progressive digital transformation and seeks to democratize the strategic use of Machine Learning in financial management.

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Published

2026-02-18

Issue

Vol. 19 No. 2 (2025): Chamada Especial - Artigos Tecnológicos

Section

Artigos Tecnológicos

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