P Abhinash

About P Abhinash

I work at the intersection of data analysis and AI model development, the part of the…

Experience

1. Senior Credit Risk & Data Analyst

   JPMorganChase

   Aug 2024 — Present · US

   With millions of daily financial transactions flowing through JPMorgan\'s credit portfolios requiring continuous risk monitoring, the credit risk and transaction data was analyzed using Python and SQL to identify exposure concentrations, early warning signals, and portfolio-level risk trends, enabling senior risk officers to make faster, data-backed credit limit and portfolio management decisions across multiple lending products.• As manual credit scoring processes were unable to keep pace with the volume and complexity of JPMorgan\'s loan portfolio, PySpark-based machine learning modules were developed on AWS-hosted Hadoop infrastructure to automate credit risk scoring, default probability forecasting, and risk-adjusted return analysis, reducing credit assessment cycle time by approximately 40% and improving model-driven decisioning coverage across the portfolio.• With credit risk and regulatory reporting teams requiring clean, audit-ready financial data delivered daily within strict SLA windows, automated data pipelines were built using Spark, Scala, and Apache Airflow to ingest, transform, and route 50M+ daily financial records from HDFS and AWS S3 to downstream risk and compliance systems, achieving 99%+ SLA adherence and eliminating manual data preparation that previously consumed 3+ analyst hours daily.• Given the need for consistent, repeatable computation of portfolio-level credit risk KPIs, regulatory capital metrics, and risk-weighted asset calculations across multiple business lines, HiveQL and Spark SQL workflows were designed on partitioned financial datasets to generate standardized risk segmentation and exposure summary reports, reducing manual report generation effort by 40% and ensuring metric consistency across six downstream risk and finance teams.