This article investigates the effectiveness of various classification techniques in predicting financial distress for Albanian firms. The dataset includes 16 financial ratios from the financial statements of 187 of the largest non-financial businesses operating in Albania, covering the period from 2011 up to 2014, and ranked by 2014 revenues. The methods used in predicting financial distress are logistic regression, Ada Boost, Naïve Bayes, decision trees, support vector machine (SVM), neural network, and random forest. To compare the effectiveness of the models applied we used Classification Accuracy (CA), confusion matrix, and area under the curve (AUC) as evaluation criteria. The results demonstrate the superior predictive ability of ensemble methods, with random forest achieving more accurate forecasts than other methods, followed by Ada Boost. The research contributes to the literature by showing the added value of machine learning models in emerging markets with unique practice and economic conditions and proposing an alternative classification approach for the classification of financial distress when lacking bankruptcy data. Finally, the empirical findings evidence that the strengths of ensemble learning methods are reinforced in unbalanced not-big datasets of a unique emerging economy. These insights are relevant for lending institutions and researchers aiming to refine credit risk models in unique markets where access to relevant data is a challenge.

Objective: This research examines the cointegration and causality between inbound tourism, economic growth following Sustainable Development Goals, and financial development in the Western Balkans from 2000 to 2020. The study aims to determine the direction and strength of these relationships to provide insights for policymakers. Theoretical Framework: The study builds upon the theories of financial development and tourism-led growth, as explored by Rasool et al. (2021). It considers the interdependence of tourism, economic growth, and financial development within the broader context of regional economic policies. Method: We employ the Panel Autoregressive Distributed Lag (ARDL) model to identify long-term relationships between the variables. The Dumitrescu and Hurlin test is applied to verify causality among inbound tourism, financial development, and economic growth. Data is sourced from the World Development Indicators provided by the World Bank. Results and Discussion: The analysis confirms a long-term relationship among the three variables, aligning with previous research. The causality analysis indicates strong bidirectional causality between inbound tourism, financial development, and economic growth. These findings highlight the interdependent nature of these factors in the Western Balkans. Research Implications: The study suggests that policymakers in the Western Balkans should promote a well-dispersed and efficient financial development system to stimulate both economic growth and inbound tourism. Effective financial policies can enhance tourism-driven economic expansion. Originality/Value: This research contributes to the literature by applying panel ARDL and causality tests to investigate the relationship between tourism, financial development, and economic growth in the Western Balkans. The study provides new insights into how financial sector efficiency can drive sustainable tourism-led growth in the region.