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This study aims to analyze the performance of quality indices to continuously validate a predictive model focused on the control chart classification.

The research method used analytical statistical methods to propose a classification model. The project science research concepts were integrated with the statistical process monitoring (SPM) concepts using the modeling methods applied in the data science (DS) area. For the integration development, SPM Phases I and II were associated, generating models with a structured data analysis process, creating a continuous validation approach.

Validation was performed by simulation and analytical techniques applied to the Cohen’s Kappa index, supported by voluntary comparisons in the Matthews correlation coefficient (MCC) and the Youden index, generating prescriptive criteria for the classification. Kappa-based control charts performed well for m = 5 sample amounts and n = 500 sizes when Pe is less than 0.8. The simulations also showed that Kappa control requires fewer samples than the other indices studied.

The main contributions of this study to both theory and practitioners is summarized as follows: (1) it proposes DS and SPM integration; (2) it develops a tool for continuous predictive classification models validation; (3) it compares different indices for model quality, indicating their advantages and disadvantages; (4) it defines sampling criteria and procedure for SPM application considering the technique’s Phases I and II and (5) the validated approach serves as a basis for various analyses, enabling an objective comparison among all alternative designs.

The objective of this research is to determine the influence of solvency and liquidity on the profitability [return on assets (ROA)] of Tunisian banks from Q2-2020 to Q3-2022 by considering asset quality as a moderating variable.

This study uses data on liquidity, solvency, ROA and asset quality for 12 banks. It also considers bank size, gross domestic product (GDP) growth and inflation as control variables. The methodology is based on panel data with generalized least squares (GLS) estimation to assess the moderate influence of the asset quality on solvency, liquidity and ROA. Also, the generalized method of moments (GMM) estimation is used as a robustness test.

The results of the GLS model estimation indicated a negatively significant moderating correlation between the liquidity and the solvency. The data from the GMM model indicate that the liquidity variable predicted by the liquidity has a positively significant influence on a bank's ROA as well as for the solvency variable, which is predicted by the capital capacity. Therefore, we conclude that these two variables had a positively significant impact on the ROA.

The studies have many implications for banks and their management in addition to the industry regulators. The results of this study will enable political decision-makers to determine the banks' profits based on their liquidity and solvency.

This analysis provides financial explanations and recommendations for stakeholders in Tunisian banks. Furthermore, these banks must also be able to maintain their liquidity and solvency to ensure their profits in times of COVID-19.