Moin Akhtar Chauhan

• As a part of Credit Risk Middle Office, led the process of NAV data collection and analysis for reporting to the Credit Officers for the Hedge Fund portfolio with over 2000 Hedge Funds, managed 2 analysts and their work, and delivered massive improvements in key metrics.
• Developed and implemented various innovative solutions to make the data collection process more efficient, this included building solutions in Python to automate data extraction and feed into the JPMC risk infrastructure. Extraction algorithms ranged from extracting data from unstructured PDF documents to excel spreadsheets and email body. Overall these innovative solutions resulted in over 300 hours on average time savings over a year, and helped my department to eliminate dependency on offshore consulting teams.
• Developed and implemented solutions to automate the generation of key reports presented to Credit Front Office, tools like Alteryx and Python were used to create and deploy such solutions. This helped team with time savings and reduced operational errors.
• Used JPMC's in house Risk Reporting Portal to create new reports, and migrate and modify legacy reports depending on the evolving business needs. This involved data exploration and SQL.
• Led the development of Unapproved Trading Tracker Dashboard. This involved using Python to scrape the data from a web portal, and applying data engineering algorithms to combine the unstructured data into an Excel Dashboard, which could be used to identifying and remediating the unapproved outstanding trades among Agent Lender and Principal Lender groups.
• Took lead and delivered on business ideas such as Data Source Dashboards, LATAM Hedge Funds Transition to North America reporting standards, remediating NAV ageing calculations, etc.
• Performed RED (Rapid Exposure Drill) process, which included collecting, analyzing, and reconciling overall Credit and Market Risk exposure posed by different financial products across different LOBs for an entity that is deemed risky in the near future by Risk Officers. Then assorting and reporting the data to Risk Officers.

• Project 1: Predictive modeling/ Data Pipeline/ Management Lifecycle:

Building an API framework for banking clients with the ability to predict the intraday fractional shares' trading volume for traded securities

• Project 2: Risk Management/ Credit Risk/ Model Migration:

Building a risk modeling API framework to help banking clients migrate from SAS to Python as their primary tool to develop financial risk models

• Project 3: NLP/ Payroll Documents Data Extraction:

Built an API framework and deployed a service to help the client extract desired information from unstructured and diverse Payroll documents

• Project 4: BNPL (Buy Now Pay Later) strategy-specific credit modeling:

Built BNPL credit-decisioning models and an API framework to help clients decide credit worthiness of a potential applicant to a BNPL loan

• Project 5: Differential Privacy:

Assessing the difference between the model performance built upon differentially private data vs non differentially private data, involving computing exposure in loan products (CCAR, CECL) using PySpark and Leap-Year for comparison purposes