Prathaban Mookiah

Background

About Prathaban Mookiah

Lead scientist within the Fraud and Security intelligence practice with a purview on all fraud and compliance solutions. I initiate machine learning focused projects and serve as a central point for new analytical development. Mentor junior data scientists and work closely with product management, developers, testers, delivery and sales teams to provide broad technical direction, guidance, and review of documentation. Part of many formal networks with key decision makers and serve as external spokesperson for the organization on analytic related matters pertaining to the products under my purview. Work with regulatory bodies within financial organizations for model governance and due diligence purposes. Manage and work with a data consortium consisting of TB+ of raw transactional data originating from various financial institutions. Some development highlights include ML based rule generation for fraud detection, adaptive alert scoring for AML alert prioritization, automated customer segmentation for compliance / CDD, self-tuning fraud models based on DL, real-time network generation for entity resolution and anomaly detection. Previously developed and led the development of several ML models for detecting fraud and anomalies in financial systems such as online banking, cards, and payments as well as customer retention. SAS fraud models are extremely high impact models in terms of the fraud loss exposure ($B+) and technical risks. Machine learning, Predictive analytics, Customer segmentation, Fraud modeling, Anomaly detection, Compliance and CDD, Data mining, Entity-resolution, Entity-networks, Real-time scoring, CI/CD, Microsoft Azure Python, SAS, GO, SQL, VIM, bash, Linux/Unix, z/OS, REST APIs,