Enhanced Due Diligence in Prepaid and Cross-Border Payment Platforms: A Risk-Typology Framework for Cardholder Account Monitoring

Abstract

This review examines how enhanced due diligence can be strengthened in prepaid and cross-border payment environments through a risk-typology approach to cardholder account monitoring. It adopts a structured narrative review method, synthesising interdisciplinary evidence from anti-money-laundering research, payment systems, financial regulation, transaction analytics, risk management, digital finance, and monitoring technologies. The analysis evaluates the expansion of prepaid ecosystems, cross-border exposure, financial-crime typologies, cardholder profiling, alert logic, investigative frameworks, regulatory governance, and technological innovation.

The findings show that effective monitoring depends on integrating customer identity, product characteristics, funding patterns, transaction velocity, geographic exposure, counterparties, devices, and historical behaviour within a dynamic risk architecture. Static onboarding classifications and isolated transaction thresholds are insufficient for detecting complex patterns such as rapid pass-through activity, mule-account behaviour, unusual cross-border corridors, account takeover, and linked transactional networks. The review further demonstrates that enhanced due diligence is most effective when embedded within a continuous investigative cycle that connects alert triage, contextual verification, source-of-funds assessment, relationship analysis, documented judgement, escalation, and feedback into future risk calibration. Cross-border compliance also requires consistent governance, secure information exchange, proportionate reporting, and coordination across jurisdictions.

The review concludes that resilient monitoring systems should combine behavioural baselines, typology-driven indicators, explainable analytics, and human oversight. It recommends continuous validation of alert scenarios, periodic recalibration of risk scores, stronger data governance, investment in investigator expertise, and careful integration of machine learning. Future research should test these frameworks on realistic multi-jurisdictional datasets while preserving privacy, proportionality, transparency, and financial inclusion. Particular attention should be given to model drift, emerging typologies, interoperability constraints, and jurisdiction-specific behavioural variation.