Abstract
Financial fraud continues to pose a significant and persistent challenge for Pakistan’s banking sector, particularly amid rapid digitalization and the expansion of online financial services. Artificial intelligence (AI) has emerged as a critical technological response to these evolving risks. This study examines how AI-based fraud detection and prevention systems are implemented in Pakistani banks and evaluates the extent to which strategic objectives align with operational practices. Adopting an exploratory qualitative design, the study integrates a systematic literature review with in-depth interviews involving branch managers, fraud officers, IT specialists, senior executives, and customers across multiple banking institutions. The findings reveal limited awareness and understanding of AI-driven fraud management among branch-level staff and customers, with responsibility for fraud monitoring remaining highly centralized among senior management. Though advanced techniques such as machine learning, anomaly detection, deep learning, and natural language processing are technically available, their operational utilization remains constrained. Grounded in the Technology Acceptance Model (TAM) and the Resource-Based View (RBV), the current study demonstrates that user acceptance, workforce capability, and organizational readiness critically shape AI effectiveness. The study highlights the urgent need for enhanced FinTech literacy, decentralized AI deployment, and stronger governance mechanisms to bridge the strategic-operational divide. These measures are essential for aligning operational practices with strategic intent and for unlocking AI’s full potential to enhance fraud resilience, regulatory compliance, and customer trust.
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Ethical Approval
This study involved human participants through semi-structured interviews. Ethical approval was obtained from the relevant institutional ethics committee, approval number IRB/2025/123, dated 30 March 2025. All procedures were conducted in accordance with the relevant institutional ethical requirements and applicable national guidelines and regulations.
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Informed consent was obtained from all participants before the interviews. Participants were informed about the purpose of the study, the voluntary nature of participation, confidentiality protections, and their right to withdraw at any stage without penalty.
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Appendix
Appendix
Appendix A: Technical Details on AI-Algorithms
Machine Learning Algorithms. Description: Taxonomy of supervised, unsupervised, and reinforcement learning models commonly applied in fraud detection and prevention.
Machine Learning Algorithms with Sub-Classifications. Description: Detailed breakdown of supervised (e.g., decision trees, logistic regression), unsupervised (e.g., clustering, isolation forests), and deep learning models (e.g., CNNs, RNNs, autoencoders) relevant to fraud management.
The PRISMA flow diagram illustrates the identification, screening, eligibility, and inclusion of studies for the systematic literature review.
Appendix Table A1 appears here and reports the comparative performance of supervised learning algorithms used in fraud detection.
Appendix Table A2 appears here and summarizes the results of unsupervised learning methods relevant to fraud detection.
Appendix Table A3 appears here and presents the reported performance of deep learning approaches used in fraud detection.
Appendix B: Additional Technical Challenges in AI Fraud Detection
In addition to the algorithmic details and performance metrics presented above, the literature and interview findings highlight several technical challenges that complicate the implementation of AI-driven fraud detection in banking:
• Data Quality and Availability: Incomplete, imbalanced, or noisy datasets limit the effectiveness of machine learning models, especially in environments where fraudulent cases are under-reported.
• Model Reliability and Accuracy: Even high-performing algorithms produce false positives and false negatives, which can erode user confidence and increase operational costs.
• System Integration Issues: Many banks lack the IT infrastructure to integrate AI tools with legacy systems, creating bottlenecks in real-time fraud monitoring.
• Interpretability and Transparency: Deep learning and ensemble methods often act as “black boxes,” making it difficult for managers to understand how decisions are generated.
• Compliance and Governance: Regulatory frameworks demand explainability, auditability, and accountability, which opaque AI models do not easily meet
• Workforce Skills Gap: Branch managers and frontline staff often lack training in AI systems, which prevents effective use and limits proactive fraud prevention.
These challenges overlap with the ethical and regulatory concerns summarized in Table 3 of the main text. While technical improvements (e.g., better feature engineering, hybrid models, real-time analytics) may address some of these issues, organizational readiness and regulatory clarity remain equally critical for effective fraud management.
Appendix C: PRISMA Flow Diagram
Appendix D: Summary of Interview Themes and Illustrative Quotations
This appendix provides a concise overview of the main themes derived from the qualitative interviews, along with illustrative quotations to enhance transparency and analytical credibility.
Theme | Description | Illustrative Quotation |
|---|---|---|
Centralization of AI Usage | AI-based fraud detection systems are primarily managed at the head-office level, with limited branch-level autonomy. | “We rely on instructions from the central monitoring unit; AI tools are not something we directly operate at the branch.” (Branch Manager) |
Knowledge and Training Gaps | Limited AI literacy among operational staff constrains the effective use of AI outputs. | “Most staff do not fully understand how AI flags transactions, so they hesitate to rely on it.” (Compliance Officer) |
Regulatory and Ethical Concerns | Uncertainty around accountability, explainability, and data privacy reduces trust in AI systems. | “If an AI system blocks a customer, it is not always clear who is responsible.” (Senior Manager) |
Technology–Organization Misalignment | Weak system integration limits real-time fraud prevention. | “Detection works, but prevention is difficult because systems don’t fully talk to each other.” (IT Specialist) |
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Rahim, A., Ullah Jan, S., Ali, S. et al. AI-driven financial fraud detection in Pakistan’s banking sector: bridging strategic intent and operational implementation. Humanit Soc Sci Commun (2026). https://doi.org/10.1057/s41599-026-07406-6
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DOI: https://doi.org/10.1057/s41599-026-07406-6
