Compliance & Risk

KYC & Identity Verification

Know Your Customer is not a one-time checkbox, it is a continuous obligation that spans onboarding, periodic review, and event-triggered reassessment. Customer due diligence (CDD) establishes the identity and purpose of the relationship; enhanced due diligence (EDD) applies when risk indicators, PEP status, high-risk jurisdiction, unusual transaction patterns, demand deeper scrutiny. Regulatory frameworks set the minimum bar: the Sixth Anti-Money Laundering Directive (6AMLD) tightened predicate offences and criminal liability across the EU, FATF Recommendations define global standards, the FCA's MLR 2017 obligations govern UK-regulated firms, and the Qatar Central Bank's AML/CFT framework imposes specific CDD requirements for firms operating in-country. Getting the programme right means knowing exactly which ruleset applies to each customer segment and geography.

Electronic KYC has transformed what was once a paper-heavy, branch-dependent process into a decisioning flow measurable in seconds. Document verification engines extract and validate data from passports, national ID cards, and driving licences; liveness detection confirms the document holder is present and alive, not a photograph or a deepfake. AI-assisted review layers on top to flag inconsistencies, mismatched fonts, cloned security features, metadata anomalies, that rule-based systems miss. The practical challenge for any payment firm is calibrating where automated approval stops and human review begins, because the cost of a false rejection sits in plain sight on the P&L, while the cost of a false pass is deferred and often much larger.

Onboarding a consumer and onboarding a corporate entity are fundamentally different exercises. For individuals, the process centres on identity document verification, sanctions and PEP screening, and source-of-funds assessment for higher-risk profiles. For businesses, the scope expands significantly: verifying the legal entity, understanding the ownership structure, and identifying ultimate beneficial owners (UBOs) down to the threshold defined by the relevant jurisdiction, typically 25% ownership or control. Complex structures involving holding companies, trusts, or nominees across multiple jurisdictions can require weeks of document collection and manual analysis. We help payment firms design KYC frameworks that are proportionate to their customer base and risk appetite, supported by technology integrations that lift straight-through processing rates without compromising regulatory integrity.

AML & Transaction Monitoring

Anti-money laundering obligations on payment firms have expanded steadily as regulators recognise that money moves through payment infrastructure before it reaches any bank account. Transaction monitoring is the operational core of any AML programme, and the difference between a programme that works and one that merely satisfies an auditor is substantial. Rules-based systems, threshold triggers, velocity checks, geographic filters, are fast to deploy and easy to explain to regulators, but they produce high false-positive volumes and are trivially circumvented by sophisticated actors who know the thresholds. Machine learning-based systems identify behavioural patterns and typology signatures across large transaction populations, catching what rules miss, but they require clean data, regular model validation, and staff who can interrogate and explain alerts. Most mature programmes run both in parallel.

The regulatory environment is layered and jurisdiction-specific. 6AMLD extends criminal liability to legal persons and broadens the list of predicate offences. FinCEN's Bank Secrecy Act obligations govern US-dollar correspondent flows. The Qatar Central Bank's AML/CFT Instructions set explicit transaction monitoring requirements for licensed payment institutions, and the Central Bank of the UAE has published detailed guidance on wire transfer obligations and suspicious transaction reporting. FATF's Recommendation 16, the travel rule, requires that originator and beneficiary information travels with wire transfers above threshold, and FATF's Virtual Assets guidance extends this to crypto transactions, a requirement now being operationalised by exchanges and custodians globally. Real-time payment rails introduce a specific tension: funds settle in seconds, leaving no practical window to hold and investigate a transaction before it completes. Effective AML on faster payments relies on pre-transaction scoring and post-transaction monitoring with rapid account action capability.

Suspicious Activity Reports and Suspicious Transaction Reports are the formal output of a functioning monitoring programme, but filing volume is a poor measure of programme quality. Regulators are increasingly focused on the quality of SAR narratives, the timeliness of filing, and whether the firm has a genuine typology library that reflects its actual business model and customer base. Correspondent banking de-risking, where banks exit relationships with payment firms rather than manage the risk, remains a live issue for firms that cannot demonstrate the rigour of their AML controls. We work with payment firms to design and stress-test transaction monitoring frameworks, build typology libraries tuned to specific business models, improve alert management workflows, and prepare the documentation that satisfies both internal audit and external regulatory examination.

Fraud Prevention & Detection

Operational fraud, account takeover, synthetic identity, social engineering, and first-party misuse, is distinct from AML in its mechanics, its commercial impact, and the controls required to address it. These fraud typologies are the dominant loss drivers across digital payment channels: account takeover through credential stuffing or SIM-swap attacks, synthetic identities constructed from real and fabricated data that pass standard KYC checks, authorised push payment fraud where victims are manipulated into initiating transactions, and first-party misuse where genuine customers make purchases and then dispute them. Each requires a different detection architecture and a different response playbook. Rule-based systems flag known patterns; machine learning models identify behavioural anomalies across device, session, and transaction signals that no static rule can capture. The most effective programmes layer both, with device intelligence and behavioural biometrics providing the earliest signal of account compromise, often before any transaction is attempted.