About
Detail-oriented Data Analyst with 3+ years of experience in banking credit risk analytics, supporting credit card portfolio. Proficient in Excel, SQL, SAS, Python and Power BI for end-to-end data analysis, credit risk reporting, and portfolio performance monitoring. Experienced in analyzing delinquency trends, NPA movement, PD/LGD/EAD and risk indicators, with a strong focus on regulatory compliance, MIS management reporting.
Skills & Expertise (26)
Work Experience
Data Analyst (Banking Credit Risk Analytics)
Standard Bank
Jan 2023 - Present
Used Base SAS and PROC SQL to extract, transform, and validate large-volume credit risk portfolio data for risk monitoring and regulatory reporting. Performed daily validations in SAS between source systems and downstream risk marts to ensure data accuracy and completeness. Delivered end-to-end credit risk analytics for the credit card portfolio, covering origination quality, portfolio performance, delinquency trends and loss monitoring in alignment with IFRS 9 and Basel guidelines. Built and optimized SQL and SAS data pipelines to extract, cleanse, reconcile, and transform large-scale loan data, reducing reporting turnaround time by 30–40% and improving data reliability. Utilized SAS (Base SAS, PROC SQL) to prepare and validate PD, LGD, and EAD datasets for Expected Credit Loss (ECL) computation, ensuring audit-ready and governance-compliant outputs. Supported IFRS 9 staging (Stage 1, Stage 2, Stage 3) through rule-based logic, finance threshold validation, and exposure reconciliation across risk and systems. Performed portfolio performance analysis including DPD movement, roll-rate analysis, vintage and cohort tracking and NPL monitoring to identify emerging credit risks and portfolio deterioration. Developed interactive management dashboards using Power BI to track portfolio health, risk segmentation, approval rates, arrears trends and ECL movements saved 10+ hours per week in manual MIS preparation. Applied python for exploratory data analysis, statistical validation and segmentation analysis to identify default drivers and support risk insights. Automated recurring risk reports and control checks using SAS workflows and Advanced Excel, improving consistency, transparency and control frameworks. Conducted trend, variance and root-cause analysis on high-risk segments, contributing to a 5–7% improvement in early-stage delinquency containment. Collaborated with Risk, Finance, Model Risk, Collections and Compliance teams to deliver data-driven insights aligned with business strategy and regulatory expectations. Reduced data processing and reporting time by 30–40% through SQL and SAS automation. Improved early-stage delinquency monitoring, contributing to 5–7% better risk containment.
Education
B.Sc. in Computer Science - Jiwaji University
2020 - 2023 · Afghanistan
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