Responsibilities:
Fraud Risk Management Analytics
Develop analytical models to identify potential card fraud and money mules activities, analyze patterns related to card misuse, chargebacks, identify networks of accounts involved in money mule activities using data linkage and clustering techniques, analyze unusual cashflow patterns and assets the risk of potential mule networks
Conduct data analytics to detect suspicious transaction patterns and fraudulent behaviour.
Perform post-transaction analysis to uncover trends and identify risk indicators
Collaborate with the fraud management team to support case analysis and reporting
Generate insights and reports on fraud trends, highlighting risk areas and mitigation effectiveness
Collection Analytics
Assess customer repayment behavior to refine collection strategies
Develop models to segment delinquent accounts based on risk levels
Provide insights on recovery rates and optimize prioritization for collection efforts
Provide business recommendations on collection effectiveness, portfolio segmentation, and customer repayment behavior
Develop collection productivity dashboard to support collection team, identify areas for productivity improvement
Support CBS related matters
Perform monthly CBS submission for cards and support CBS queries
Modelling, Analytics and Dashboarding for Lending Products
Build, maintain models and perform analytics to support lending products’ analytics use cases
Create and maintain dashboards for lending products
Automate reporting processes to improve efficiency and accuracy
Prepare management reports that highlight performance trends and business outcomes
Cross-Functional Collaboration
Partner with collection operation, credit risk, fraud management and product teams to ensure alignment of business strategies with risk appetite
Work with tech teams for any tech enhancement for collection and risk related data project
Support compliance and regulatory reporting as needed
Work closely with IT for any project related to CBS and Credit Risk related project from SG Consumer banking
Innovation and Best Practices
Stay updated on industry trends, best practices, and regulatory changes in the banking and wealth management sectors to identify opportunities for innovation and competitive advantage
Requirements:
Qualifications
Bachelor’s degree in Data Science, Data Analytics, Business Analytics, Statistics or related field; Master’s degree preferred
5+ years of experience in data analytics
- Proven expertise in analytics tools (e.g., SQL, Python, R) and visualization platforms (e.g., Tableau, Power BI)
Relevant Work Experience
- Experience in Fraud analytics, Collection analytics or CBS related data
- Familiarity with Consumer Banking products including Loan, Credit card, Mortgage
- Ability to work collaboratively in cross-functional teams and manage multiple priorities in a dynamic environment
- Knowledge of regulatory requirements and industry compliance standards in the banking sector
- Excellent communication and presentation skills to effectively convey complex data insights to non-technical stakeholders
Competencies/Skills
- Proficient in SAS, SQL, Python, R Programming and MS Excel
- Proficient in Business Intelligence tools (Tableau)
- Must be able to solve problem quickly and efficiently
- Strong analytical and communication skills
