Can AI readiness and strong institutions curb AML risk? Cross-country evidence from panel data

Type of the article: Research ArticleAbstractAs emphasized by the FATF, IMF, and World Bank, technological readiness and institutional quality are increasingly decisive in shaping AML effectiveness. By mitigating money-laundering vulnerabilities, strengthening AI readiness, and enhancing institutional quality, tax collection efficiency can be improved and fiscal leakages reduced. These improvements expand the fiscal space available for national budgets, strengthening the financial foundations of public administration. The study aims to examine the impact of Government AI Readiness on AML risk, measured by the Basel AML Index, and the moderating role of institutional quality as captured by the Rule of Law Index. An unbalanced panel dataset that covers up to 168 countries for 2020–2024 was analyzed using fixed effects and random effects models, with variable transformations applied where necessary. All estimations were performed in R Studio. The results indicate that a one-point increase in the Government AI Readiness Index is associated with a 0.048–0.040 point reduction in the Basel AML Index, while a one-unit increase in log GDP per capita decreases the Basel AML Index by 0.54–0.34 points, holding other factors constant. The interaction term between AI readiness and the Rule of Law Index is positive (0.067–0.072), confirming that the risk-reducing effect of AI readiness diminishes as institutional quality strengthens. These findings support the hypotheses and confirm the complementary roles of technological preparedness and institutional integrity in shaping AML outcomes. Fixed effects analysis reveals structural vulnerabilities in AML in Gabon and China, while Sweden exhibits the lowest residual risk after accounting for AI readiness and institutional strength.AcknowledgmentThis article was supported by the Ministry of Education and Science of Ukraine (project No. 0123U101945 – National security of Ukraine through prevention of financial fraud and money laundering: war and post-war challenges).

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Publication Details

Journal
Public and Municipal Finance
Published
2025-12-17
DOI
https://doi.org/10.21511/pmf.14(4).2025.05
Citations
1
Primary Topic
Corruption and Economic Development
Type
article
Field-Weighted Citation Impact
4.20
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article

Can AI readiness and strong institutions curb AML risk? Cross-country evidence from panel data

Oxana Kirichok, Serhiy Lyeonov, Victoria Yuriivna Dudchenko, Viktoriya Hurochkina et al.
1 citations
Public and Municipal Finance
Corruption and Economic Development
4.20
article

Can AI readiness and strong institutions curb AML risk? Cross-country evidence from panel data

Oxana Kirichok, Serhiy Lyeonov, Victoria Yuriivna Dudchenko, Viktoriya Hurochkina, Pavlo Mykolaiovych Rubanov, Gulnara Zhanseitova, Denys Babaiev
article en
1 citations

Abstract

Type of the article: Research ArticleAbstractAs emphasized by the FATF, IMF, and World Bank, technological readiness and institutional quality are increasingly decisive in shaping AML effectiveness. By mitigating money-laundering vulnerabilities, strengthening AI readiness, and enhancing institutional quality, tax collection efficiency can be improved and fiscal leakages reduced. These improvements expand the fiscal space available for national budgets, strengthening the financial foundations of public administration. The study aims to examine the impact of Government AI Readiness on AML risk, measured by the Basel AML Index, and the moderating role of institutional quality as captured by the Rule of Law Index. An unbalanced panel dataset that covers up to 168 countries for 2020–2024 was analyzed using fixed effects and random effects models, with variable transformations applied where necessary. All estimations were performed in R Studio. The results indicate that a one-point increase in the Government AI Readiness Index is associated with a 0.048–0.040 point reduction in the Basel AML Index, while a one-unit increase in log GDP per capita decreases the Basel AML Index by 0.54–0.34 points, holding other factors constant. The interaction term between AI readiness and the Rule of Law Index is positive (0.067–0.072), confirming that the risk-reducing effect of AI readiness diminishes as institutional quality strengthens. These findings support the hypotheses and confirm the complementary roles of technological preparedness and institutional integrity in shaping AML outcomes. Fixed effects analysis reveals structural vulnerabilities in AML in Gabon and China, while Sweden exhibits the lowest residual risk after accounting for AI readiness and institutional strength.AcknowledgmentThis article was supported by the Ministry of Education and Science of Ukraine (project No. 0123U101945 – National security of Ukraine through prevention of financial fraud and money laundering: war and post-war challenges).

Public and Municipal FinanceVol. 14(4)
L. N. Gumilyov Eurasian National University (KZ), Sumy State University (UA), Resources for Human Development (US), Caspian University (KZ), Research Institute of Ukrainian Studies (UA)
Openalex Percentile: Top 5%
Corruption and Economic Development
4.20
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