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Does Financial Inclusion and Digitalization Reduce Corruption? Cross-Country Evidence and Sectoral Insights

Year: 2026

Type: article

Abstract: This study investigates the impact of key economic and financial variables on the Corruption Perception Index (CPI) across countries. The objective is to identify the most significant predictors of perceived corruption. The analysis employs a variety of quantitative methods, including Linear Regression, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Attention-RNN, and Random Forest models. The results demonstrate that GDP per capita is the most influential factor in explaining variations in corruption perceptions. Among financial sector indicators, variables reflecting active banking operations (such as outstanding loans and deposits) showed stronger predictive power than digitalization metrics (e.g., number of bank branches and ATMs). This suggests that the intensity of financial activity plays a more critical role than the mere availability of banking infrastructure. The study concludes that fostering economic growth and stimulating active engagement with the formal financial system are effective strategies for reducing corruption perceptions. Policy recommendations include addressing unemployment and promoting the formalization of remittance flows.

Source: International Journal of Economics and Financial Issues

DOI: 10.32479/ijefi.22620

Authors: Latifa Ouis, Nadjib Allah Hakmi, Nadjet Ouis

Institutions: University of Algiers 3, Centre de Recherche en Économie Appliquée pour le Développement

Language: English

Topic: Corruption and Economic Development

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

SDGs: Peace, justice, and strong institutions, Decent work and economic growth

Open Access status: gold

Estimated APC paid: $300