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.
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