Sayuj Sreenivasan

I developed an AI-based financial anomaly detection system using Isolation Forest and rule-based validation to identify irregularities and potential risks in company financial data. It achieved ~85–90% anomaly detection accuracy on datasets containing 1,000+ financial records.

During the AICTE Virtual Internship with AICTE, Shell India Markets Pvt. Ltd., and Edunet Foundation, I analyzed datasets and applied AI/ML concepts to data-driven problem-solving. I’ve worked with Python and project-based learning, and I’m studying Computer Science & Engineering at MANGALORE INSTITUTE OF TECHNOLOGY AND ENGINEERING (MITE).