Fraud and Credit Risk Business Analyst

Banking and domain

• AML/CFT: transaction monitoring, name screening, sanctions, customer risk rating and network analysis.

• Fraud: card, digital banking, application and scam fraud; mule account detection.

• Alert and case lifecycle: triage, investigation, STR filing and feedback to detection models.

• Credit risk data: PD, LGD, EAD, IFRS 9 ECL and early warning signals.

• Trade Surveillance Analytics, Data Quality & Controls, BCBS239 Data Governance

• Data from AML or fraud platforms such as NICE Actimize, SAS, Oracle FCCM, FICO Falcon or Feedzai.

Good-to-have skills

• MAS Notice 626 and model governance for detection models.

• Writing acceptance criteria for AI and generative AI outputs.

• Network and link analytics.

Certifications (preferred): CAMS; CFE; FRM; CBAP.

Requirements

Business and data analysis

• Attribute-level functional specifications and source-to-target mappings for bank data warehouses and marts.

• Defining risk KPIs and derived measures with grain, ownership and reconciliation rules.

• Designing customer, account, transaction and network-level risk views.

• SQL for data profiling and reconciliation.

Delivery and communication

• Running requirements workshops with Compliance, financial crime operations and risk teams.

• Leading SIT and UAT through to business sign-off.