◦ Developed an ML-based fraud detection system for government financial audit data using Isolation
Forest and statistical analysis.
◦ Engineered 40+ domain-specific features, including election proximity flags, GST/IT deduction flags,
rolling transaction windows, and other temporal, statistical, interaction, and cyclic features.
◦ Integrated Z-score logic, duplication checks, and rolling window mechanisms to improve fraud
detection accuracy.
◦ Visualized anomalous spending behavior using Seaborn and Matplotlib to support audit decisions.
◦ Optimized fraud detection by replacing manual full-dataset scanning with an ML model that targets
a 4–5% high-risk subset, boosting audit efficiency.
◦ Mentored by Shri Pravindra Yadav, Principal Accountant General (A&E), Jaipur