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.

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.