Blockchain-enabled RegTech for Anti-Fraud Compliance in Retail Banking

Thesis Abstract

This study investigates how blockchain-enabled RegTech solutions can strengthen anti-fraud compliance in retail banking by providing transparent, auditable, and real-time regulatory monitoring across customer onboarding, transaction monitoring, and fraud investigations. The problem addressed is the persistent gap between regulatory expectations for robust anti-fraud controls and the fragmented, opaque data flows in traditional banking infrastructures, which hinder timely detection, investigation, and reporting of suspected fraud and AML/CFT violations. The aim is to develop and empirically validate an integrated blockchain-enabled RegTech framework that enhances data integrity, traceability, and compliance efficacy while maintaining operational efficiency and customer privacy. Specific objectives are (1) to identify regulatory requirements and fraud typologies most impacted by RegTech-enabled blockchain architectures; (2) to design a reference architecture integrating distributed ledger technology, smart contracts, and continuous AML screening with existing core banking systems; (3) to evaluate the framework’s impact on detection accuracy, false-positive rates, and investigation cycle times; (4) to assess governance, data privacy, and interoperability considerations; and (5) to provide actionable guidelines for financial institutions deploying blockchain-based RegTech solutions. The methodology adopts a mixed-methods research design anchored in the Technology–Organization–Environment (TOE) framework and the Information Systems Success Model. The study population comprises 20 retail banks with active RegTech initiatives across North America and Europe, and a cross-sectional sample of 200 fraud/AML compliance staff and 40 internal auditors selected through stratified random sampling. Data collection instruments include (i) structured surveys measuring perceived usefulness, ease of use, data quality, decision support, and regulatory stress, (ii) semi-structured interviews with 30 regulatory and compliance officers to capture governance, policy alignment, and risk appetite, (iii) system logs and transaction datasets from partner banks (anonymized and de-identified) for quantitative analysis, and (iv) documentary evidence such as regulator guidelines and internal control manuals. Validity and reliability are established through pilot testing (n=20), Cronbach’s alpha assessment for multi-item scales (>0.7), and triangulation across survey, interview, and log data. Data analysis employs a combination of quantitative and qualitative techniques. Descriptive statistics summarize baseline characteristics and regulatory burden; multivariate regression analyzes determinants of detection accuracy and false-positive rates, controlling for bank size and IT maturity; time-series analysis investigates changes in investigation cycle times pre- and post-implementation; structural equation modeling tests the proposed relationships among technology adoption, process integration, data quality, and compliance outcomes. For qualitative data, thematic analysis identifies patterns in governance challenges and stakeholder perceptions, with coding grounded in the Technology-Organizational-Environmental framework and the RegTech maturity model. A conceptual model illustrating the interactions between distributed ledger components (tamper-evident ledgers, smart contracts), data provenance, identity and access management, and regulatory reporting processes is proposed and empirically tested. Expected findings indicate that the blockchain-enabled RegTech framework improves data integrity, provenance, and tamper-evidence, leading to higher detection accuracy by 12–18% and a reduction in false positives by 20–28% relative to conventional systems. The real-time auditable trail is anticipated to shorten investigation cycles by 25–40%, while smart-contract-driven governance automates compliance checks for customer due diligence, transaction monitoring rules, and suspicious activity reporting. The study is likely to reveal critical governance and interoperability bottlenecks, including privacy-preserving constraints, cross-border data sharing complexities, and the need for standardized APIs and regulator-facing dashboards. Theoretical contributions include empirical validation of the TOE framework’s applicability to RegTech in banking and integration of a RegTech-specific maturity model with blockchain affordances. Practical contributions encompass a scalable reference architecture, implementation guidelines, risk controls for smart contracts, and a regulatory reporting blueprint tailored to retail banking. The study advances knowledge by bridging blockchain technology, RegTech, and anti-fraud compliance in a real-world banking context, offering empirical evidence on performance gains, risk reductions, and governance prerequisites. It provides a blueprint for banks seeking to modernize compliance programs without compromising privacy or operational efficiency, and offers regulators a clearer view of how blockchain-enabled RegTech can support proportionate, auditable, and transparent supervisory processes. Based on the findings, recommendations include adopting privacy-preserving techniques such as zero-knowledge proofs, establishing interoperable data standards, implementing modular smart contracts with independent audit trails, and prioritizing cross-border data governance frameworks to facilitate international compliance. Further research is suggested to explore long-term resilience, interoperability with fintech ecosystems, and the environmental implications of large-scale blockchain deployments inRegTech contexts.