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

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Type of the article: Research Article

Abstract
As 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.

Acknowledgment
This 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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    • Table 1. Variables and their sources
    • Table 2. Descriptive statistics of key variables for Basel AML Index (y1) on Government AI Readiness Index (x1) and log GDP per capita (x2)
    • Table 3. Panel regression results for Basel AML Index (y1) on Government AI Readiness Index (x1) and log GDP per capita (x2_log)
    • Table 4. Random effects model with Driscoll–Kraay standard errors for Basel AML Index (y1) influenced by Government AI Readiness Index (x1), and log GDP per capita (x2_log)
    • Table 5. Country-specific fixed effects: Top 10 and bottom 10 baseline AML risk estimates, influenced by Government AI Readiness Index (x1), and log GDP per capita (x2_log)
    • Table 6. Descriptive statistics of key variables: Basel AML Index (y1) on Government AI Readiness Index (x1), log GDP per capita (x2_log), Rule of Law Index (x3)
    • Table 7. Panel regression results for Basel AML Index (y1) on Government AI Readiness Index (x1), log GDP per capita (x2_log), Rule of Law Index (x3), and interaction term (x1 × x3)
    • Table 8. Fixed effects model with Driscoll–Kraay standard errors for Basel AML Index (y1) on Government AI Readiness Index (x1), log GDP per capita (x2_log), Rule of Law Index (x3), and interaction term (x1 × x3)
    • Table 9. Country fixed effects: Top 10 and bottom 10 for Basel AML Index (y1) on Government AI Readiness Index (x1), log GDP per capita (x2_log), Rule of Law Index (x3), and interaction term (x1 × x3)
    • Table A1. List of countries: Basel AML Index (y1) on Government AI Readiness Index (x1) and log GDP per capita (x2_log)
    • Table A2. List of countries: Basel AML Index (y1) on Government AI Readiness Index (x1), log GDP per capita (x2_log), Rule of Law Index (x3), and interaction term (x1 × x3)
    • Table B1. Country-specific fixed effects: AML Risk Estimates, influenced by Government AI Readiness Index (x1), and log GDP per capita (x2_log)
    • Table B2. Country fixed effects: for Basel AML Index (y1) on Government AI Readiness Index (x1), log GDP per capita (x2_log), Rule of Law Index (x3), and interaction term (x1 × x3)
    • Conceptualization
      Oxana Kirichok, Viktoriia Hurochkina, Gulnara Zhanseitova, Viktoria Dudchenko, Pavlo Rubanov, Denys Babaiev, Serhiy Lyeonov
    • Funding acquisition
      Oxana Kirichok, Viktoriia Hurochkina, Gulnara Zhanseitova, Viktoria Dudchenko, Pavlo Rubanov, Denys Babaiev
    • Resources
      Oxana Kirichok, Viktoriia Hurochkina, Gulnara Zhanseitova, Viktoria Dudchenko, Pavlo Rubanov, Denys Babaiev
    • Validation
      Oxana Kirichok, Serhiy Lyeonov
    • Writing – original draft
      Oxana Kirichok, Viktoriia Hurochkina, Gulnara Zhanseitova, Viktoria Dudchenko, Pavlo Rubanov, Denys Babaiev, Serhiy Lyeonov
    • Writing – review & editing
      Oxana Kirichok, Viktoriia Hurochkina, Gulnara Zhanseitova, Viktoria Dudchenko, Pavlo Rubanov, Denys Babaiev, Serhiy Lyeonov
    • Formal Analysis
      Viktoriia Hurochkina, Serhiy Lyeonov
    • Visualization
      Gulnara Zhanseitova, Serhiy Lyeonov
    • Data curation
      Viktoria Dudchenko, Serhiy Lyeonov
    • Software
      Denys Babaiev, Serhiy Lyeonov
    • Investigation
      Serhiy Lyeonov
    • Methodology
      Serhiy Lyeonov
    • Project administration
      Serhiy Lyeonov
    • Supervision
      Serhiy Lyeonov