Banking governance of external AML evidence: UK ownership, crypto benchmarking and temporal signals

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Type of the article: Research Article

Abstract
Banks rely on ownership, transaction, and institutional information to support anti-money-laundering decision-making, yet these sources differ in decision proximity, uncertainty, and evidentiary strength. This multi-study article evaluates three external anti-money-laundering signals within a decision-governance framework emphasizing provenance, auditability, and bounded interpretation. Study 1 examines United Kingdom Persons with Significant Control records as ownership-verification workload signals. Of 6.2 million active companies, 745,200 (12.0%) met at least one verification-priority rule. Among flagged companies, 58.3% were assigned to multi-layered control structures, 25.0% to non-United Kingdom or foreign-linked controllers, 11.2% to high officer/controller turnover, and 5.5% to circular ownership patterns. Study 2 compares extreme gradient boosting, graph convolutional networks, Graph Sample and Aggregate, and graph attention networks on the Elliptic transaction-network benchmark. Graph attention networks recorded the highest point estimates (AUROC = 0.960; AUPRC = 0.740), followed by Graph Sample and Aggregate (0.930; 0.610), graph convolutional networks (0.910; 0.560), and extreme gradient boosting (0.820; 0.370). Study 3 identifies a reported −6.8% posterior mean deviation in the Persons with Significant Control Anomaly Index after the late-June 2020 filing-regime boundary (95% credible interval: −10.9% to −2.4%), interpreted descriptively rather than causally. Overall, the findings establish an evidentiary hierarchy for governing heterogeneous anti-money-laundering signals while preserving boundaries between decision support, adjudication, and causal inference. The empirical objects are companies, transaction nodes, and register observations rather than banks.

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    • Table 1. Data overview and AML relevance
    • Table 2. Methods and compliance purpose
    • Table 3. Descriptive statistics of external AML data layers
    • Table 4. PSC verification-priority signals
    • Table 5. Elliptic benchmark model performance
    • Table 6. Exploratory PSC-index posterior temporal deviation
    • Table 7. Minimum robustness and model-governance records
    • Table 8. Hypothesis-evidence summary
    • Table A1. Result-to-evidence audit trail
    • Table A2. Reconciled PSC count logic for banking AML interpretation
    • Table A3. Minimum supplementary verification package
    • Conceptualization
      Ayman Bader
    • Formal Analysis
      Ayman Bader, Raed Alqirem, Atala Alqtish, Ayman Mansour Khalaf Alkhazaleh
    • Funding acquisition
      Ayman Bader
    • Investigation
      Ayman Bader, Atala Alqtish, Ayman Mansour Khalaf Alkhazaleh
    • Methodology
      Ayman Bader, Raed Alqirem, Atala Alqtish, Ayman Mansour Khalaf Alkhazaleh
    • Resources
      Ayman Bader
    • Supervision
      Ayman Bader
    • Validation
      Ayman Bader, Atala Alqtish
    • Writing – review & editing
      Ayman Bader
    • Data curation
      Raed Alqirem, Atala Alqtish
    • Software
      Raed Alqirem
    • Writing – original draft
      Raed Alqirem, Atala Alqtish, Ayman Mansour Khalaf Alkhazaleh
    • Visualization
      Atala Alqtish, Ayman Mansour Khalaf Alkhazaleh