Purpose: This study develops and empirically examines a dual-cognition auditing framework for enhanc-ing fraud and money-laundering detection in digitally transformed banking environments. The framework structurally integrates human audit cognition with digitally-enabled adaptive analytics to address the growing complexity of technology-driven financial crimes in emerging economies.

Methodology: The study adopts a comparative empirical design based on audit data, digital transaction rec-ords, and anti–money laundering (AML) indicators collected from Egyptian banking institutions. Quantitative techniques are combined with analytical risk-modeling procedures to evaluate detec-tion effectiveness under dual-cognition conditions.

Design /Approach: A dual-layer auditing design is employed. The first layer captures professional judgment, professional skep-ticism, and behavioral interpretation, while the second layer applies adaptive analytics capable of learning dy-namically from evolving fraud and laundering typologies within digital banking systems.

Approach: The analysis relies on documentary evidence, regulatory texts, standard-setting materials, and institutional role mapping. Comparative insights are used illustratively to contextualize Egypt’s experience without assuming full international convergence.

Findings: The results demonstrate that dual-cognition auditing significantly outperforms traditional judgment-based auditing in detecting complex fraud schemes and structured money-laundering networks. Digitally-enabled adaptive analytics substantially enhance anomaly detection accuracy, dynamic risk prioritization, and cross-transactional pattern recognition across digital financial channels.

Originality & Value: This study provides one of the first empirically validated dual-cognition auditing models for financial crime detection within digitally transformed banking systems in emerging-market con-texts, offering a novel structural integration between professional cognition and adaptive analyt-ics.

Theoretical, Implications: The study extends behavioral auditing and analytical assurance literature by reconceptualizing audit cogni-tion as a hybrid human–digital cognitive system rather than a purely professional judgment mechanism.

Practical Implications: The framework offers regulators, auditors, and banks a structured model for strengthening AML systems, digital fraud detection, and audit risk management in data-intensive banking envi-ronments.

Social Implications: Enhanced detection of fraud and money laundering directly supports financial integrity, public trust in banking systems, and the containment of illicit financial flows in emerging economies.

Dual-Cognition Auditing, Fraud Detection, Money Laundering, Audit Data Analytics, Digital Banking, AML Governance, Emerging Banking Systems

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