Projects / Contributions
Machine Learning Projects
Current Focus:
Developing an Anti-Money Laundering (AML) pipeline that detects suspicious transaction patterns using feature engineering, anomaly detection, and hybrid ML–LLM methods.
The system is built with MLflow and DVC and follows MLOps principles for continuous improvement and scalability.
Dynamic Pricing for ATM Transactions
Designed a system using customer segmentation (KMeans) and demand modeling (XGBoost) to optimize transaction fees, increasing revenue by 10–12% while maintaining transaction volume.
ATM Cash Demand Forecasting & Safety Stock Optimization
Developed a forecasting system using XGBoost and N-BEATS to predict daily cash demand for each ATM cassette ($1, $5, $20, $100).
The model generates accurate per-denomination forecasts and calculates safety stock levels to prevent depletion events, significantly reducing cash-out incidents and improving ATM uptime.
Defect Detection from ATM Telemetry Logs
Created a BERT-based anomaly detection model identifying software/hardware defects early. Integrated with LangGraph orchestration for LLM-based root-cause analysis, increasing detection from 30% to 80%.
AI / Agentic Projects
Speech Analytics for Compliance Monitoring in Call Center
Designed an AI-driven speech analytics system using an orchestrator–worker pattern built on LangGraph to analyze interactions between human agents and customers.
The agent evaluates compliance adherence, sentiment neutrality, and conversational quality, generating real-time recommendations for call improvement.
Integrated with MLflow for full observability and prompt tracking, enabling model auditability and continuous optimization.
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AI4Good: Detecting Anomalies in Energy Consumption using Machine Learning
Developed an ML-based system for a Latin American energy distribution company to detect abnormal consumption patterns and identify inefficiencies, potential technical issues, and non-technical losses. Used ETL, feature engineering, clustering, and unsupervised models (K-Means, Isolation Forest, LOF, One-Class SVM) to produce actionable insights. Delivered a scoring datamart, interactive dashboards, and a scalable analytics workflow.
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