CyberFinance

Fraud & Financial Crime Technology

Assurance over the systems that catch fraud, laundering and payment abuse.

The situation

Fraud and AML systems are among the highest-consequence and least-audited technology in a financial institution: they are tuned by vendors, trusted implicitly, and rarely tested against the adversary they are meant to stop.

We assess the control environment end to end — detection logic and rule coverage, model governance for AI-driven scoring, alert quality and analyst workflow, data lineage, and the authentication controls that determine whether a fraudulent instruction ever reaches the system at all.

What the client receives

Schedule of deliverables

01Fraud control framework assessment across payment channels
02Transaction monitoring and AML system control review
03Detection rule coverage and tuning analysis against current fraud typologies
04Model governance and explainability review for AI-based scoring
05Strong customer authentication (PSD2 / PSD3) and 3DS control assessment
06Social engineering and authorised push payment fraud resilience review
07Fraud data quality and lineage assurance

Frameworks and standards

Assessment is carried out against named references, so a conclusion can be traced back to the requirement it was measured against.

PSD2 / SCA RTS
EBA guidelines on fraud reporting
AMLD / AMLR package
FATF recommendations
ISO/IEC 42001 for AI-driven scoring

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Discuss this engagement

Tell us about the channels affected and the detection stack behind them and we will set out the scope, the method and the reporting format.