- Model Development: Design and develop AI/ML models for transaction monitoring, focusing on anomaly detection and risk scoring.
- Data Analysis: Analyze large datasets to identify trends, patterns, and potential risks associated with financial transactions.
- System Optimization: Continuously improve the performance of monitoring systems by tuning models, refining algorithms, and integrating new data sources.
- Regulatory Compliance: Ensure that transaction monitoring processes meet all regulatory requirements and industry standards, including AML/KYC.
- Collaboration: Work closely with cross-functional teams, including compliance, IT, and risk management, to align monitoring strategies with business objectives.
- Reporting and Documentation: Prepare detailed documentation of model development processes, performance metrics, and compliance efforts.
- Training and Support: Provide training and support to team members on transaction monitoring tools and AI/ML methodologies.
- Research: Stay updated on industry trends, emerging technologies, and best practices in transaction monitoring and AI/ML.
- Experience: 3+ years of experience in transaction monitoring, fraud detection, or financial crime risk management, with a strong focus on AI/ML applications.
- Technical Skills: Proficiency in programming languages (e.g., Python, R), experience with machine learning frameworks (e.g., TensorFlow, Scikit-learn), and familiarity with data visualization tools.
- Certifications: Relevant certifications (e.g., CAMS, CFE) are a plus.
