Unsupervised Machine Learning for Financial Behavior Profiling of Tourism Firms in Barranquilla, Colombia

This study aims to identify and characterize the financial profiles of tourism-sector firms in Barranquilla through the application of unsupervised Machine Learning techniques, with the purpose of analyzing patterns of financial behavior based on profitability, capital structure, and liquidity. The research adopts a quantitative and descriptive design, using secondary financial data for fiscal year 2024 obtained from the Barranquilla Chamber of Commerce. The initial sample comprised 563 active tourism firms. Based on basic accounting variables, normalized financial indicators were constructed through a feature engineering process that included correlation analysis, variable selection, and robust scaling. A range of clustering algorithms representing different methodological paradigms as partitional, hierarchical, density-based, and probabilistic, were evaluated using a multicriteria validation framework combining internal cluster quality metrics and cluster size balance. The OPTICS algorithm was selected as the most suitable method for the final segmentation. The results revealed two regular financial clusters and a group of atypical firms. One cluster corresponds to firms with no observable financial activity, characterized by zero profitability, absence of leverage, and exclusive reliance on equity financing. The second cluster groups financially active firms exhibiting high indebtedness, low equity participation, negative profitability, and liquidity constraints, reflecting conditions of financial distress. Non-parametric statistical tests confirmed significant differences between clusters, primarily in indicators related to capital structure and profitability, while firm size did not account for the observed segmentation. Overall, the findings demonstrate that behavior-based financial segmentation supported by unsupervised Machine Learning and normalized financial ratios enables the identification of robust and interpretable financial archetypes, with capital structure and profitability emerging as the main differentiating factors.