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SCIENTIFIC RESEARCH - 2026
DOI: https://doi.org/10.36719/2789-6919/64/41-59
Algorithmic Financial Governance and Diagnostic Transparency: A Comparative Study of the Pharmaceutical Sector Using DuPont Analysis and Python (2020–2024)
Sihem Ouaddane 
Abstract. This study examines the role of algorithmic financial governance in improving the accuracy and transparency of financial performance in the pharmaceutical industry, specifically closing the Diagnostic Transparency Gap between public structural inertia and private operational agility. The study uses a modern quantitative algorithmic methodology, implementing a DuPont analysis model in Python (using the pandas, Matplotlib and Seaborn libraries) to evaluate financial data for 2020–2024. The results indicate that computational modeling reduced information transmission time and revealed notable differences in operational efficiency and capital structure between public and private firms, particularly after recent investment legislative reforms. The study shows that digital governance reduces human error in accounting and recommends adopting software automation in internal control systems to improve financial reporting quality and support strategic financial management. Empirically applied to two representative enterprises in Algeria (Saidal Group and Biopharm) under Investment Law No. 22-18, this framework remains fully scalable and applicable to other pharmaceutical entities and emerging market contexts.
Keywords: algorithmic governance, DuPont analytics, financial transparency, pharmaceutical sector, Python
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