Design and execute antifraud strategies across offline and digital lending channels to prevent fraud throughout the customer lifecycle.
Apply advanced analytics, machine learning, and statistical methods to uncover fraud patterns and improve prevention accuracy.
Designing features from behavioral, transactional, and device-level data. Utilizing supervised and unsupervised techniques (e.g., XGBoost, Random Forest) to detect anomalies and hidden fraud patterns.
Collaborating with data engineering to deploy models into production environments (batch and real-time), ensuring proper version control and governance.
Develop and maintain scalable antifraud systems, including rule engines, monitoring tools, and real-time data pipelines.
Contribute to innovation through research, experimentation, and proof-of-concept evaluation of new antifraud technologies and methodologies.
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