Shubhi Garg

I’m a Credit Risk Analyst with 3+ years’ experience across global banks, specializing in IFRS9/Basel-aligned risk analytics, model validation, and automation. At Wells Fargo, I led end-to-end credit risk and offer friction analysis with SAS, cut review cycle time by ~2–3 weeks, and built an automated anomaly detection framework for UDAAP and portfolio risk signals—reducing manual effort from ~15 days to under 4 hours (~90%). I also developed a Python-based NLP pipeline for customer complaints, reducing manual review workload by ~80%, and automated monthly risk insights to improve turnaround time by over 85%.

Previously at Standard Chartered, I developed and validated Probability of Default (PD) models for a mortgage portfolio aligned with PRA and EBA IRB frameworks, and improved reliability and stakeholder confidence by ~30% through performance analysis using GINI metrics. I automated 10+ SAS workflows and supported model governance by assessing redevelopment triggers and refresh requirements. I’m motivated by building production-grade, controls-ready solutions—whether that’s SAS automation, NLP-driven risk insights, or a production-ready credit risk prediction system deployed via FastAPI/Streamlit and powered by Scikit-Learn and XGBoost.