Financial Market Surveillance

Jul 20, 2026 · 1 min read
projects

Jul 2026

  • Built an anomaly-detection model based on an unsupervised learning algorithm called Isolation Forest.
  • Detected anomalies in USD-INR exchange-rate data using rolling-volatility features.
  • Detected stress episodes matching real crisis periods (2008 GFC, 2013 taper tantrum, 2020 COVID), along with the recent 2026 rupee depreciation.
Machine Learning Quantitative Finance Anomaly Detection Time Series
Porush Yadav
Authors
Porush Yadav

Hi! I am Porush Yadav. I have graduated from IIT Kanpur with a BS-MS Dual Degree in Mathematics and Scientific Computing. I have Worked at Axis Bank on an image recognition system that leverages neural networks to automate document classification and enhance data extraction. Before that, I interned at Samsung R&D Institute India, eJET systems, and EZ Technologies, where I worked on machine learning applications in signal data compression, recommendation systems, classification systems, and image recognition.

I look forward to leveraging AI and ML expertise to develop impactful solutions that can solve real-life problems.