Threat Intelligence: AML Strategic Analytic Innovation

The role:

The Threat Intelligence: AML strategic analytic innovation role will be responsible for optimizing the AML program through cutting-edge analytic tools and techniques. In this position, you will support the AML department by conducting in-depth data analysis, pulling key insights, and leveraging advanced technologies such as large language models (LLMs) or robotic process automation (RPA) to drive efficiency, improve threat detection capabilities, and streamline investigative workflows. Additionally, you will be responsible for developing and integrating LLM-driven solutions into business processes, enhancing automation, and improving AML monitoring effectiveness.

What you’ll do:

• Build experimental models utilizing machine learning and statistical modeling methods for supervised and unsupervised learning.

• Use advanced data mining techniques to identify patterns, anomalies, and potential red flags across large datasets.

• Contribute to the continuous optimization of the AML program by identifying opportunities for improvement in processes, tools, and workflows.

• Design, develop, and implement automated solutions to enhance AML investigative workflows, reducing manual effort and improving detection efficiency.

• Leverage large language models (LLMs) to optimize typology identification, suspicious activity reporting, and risk assessments, ensuring greater consistency and accuracy in AML processes.

• Integrate LLM-based tools into existing business systems to automate report generation, transaction monitoring, and case review processes.

• Collaborate with external technology service providers (TSPs) and industry groups to stay updated on the latest system/process innovations. 

• Support integration of new TSPs into existing monitoring and surveillance tools.

• Work with technical teams to integrate new analytical techniques into existing systems, improving efficiency and predictive capabilities.

• Develop prompt engineering strategies to fine-tune LLM outputs for AML-specific use cases, ensuring optimal performance and reducing false positives.

• Provide regular updates on threat intelligence findings, ensuring all stakeholders are aware of the evolving risks and necessary responses.

• Research ongoing Machine Learning and Artificial Intelligence trends and products.

• Document and refine integration methodologies to support scalability, reproducibility, and compliance with model risk management requirements.

• Develop reports and presentations on threat intelligence findings, ensuring that they are clear, actionable, and tailored to different audiences, including senior leadership and regulatory bodies.

What you’ll need:

• Bachelor’s Degree or Master’s Degree in Statistics, Computer Science, Mathematics, Finance, Engineering, or other relevant fields.

• 7+ years of experience in the finance industry focusing on BSA/AML, OFAC, or fraud modeling/analytics, with a demonstrated ability to build and deploy AI/LLM-driven solutions in a business environment.

• Hands-on experience implementing LLMs in AML or financial crime detection, with a strong understanding of their capabilities and limitations.

• Proficiency in designing, fine-tuning, and integrating LLMs into transaction monitoring, adverse media screening, or suspicious activity reporting workflows.

• Experience working with AI/ML models in cloud environments and integrating them into enterprise systems.

• Statistical/data analytical skills, including data quality validation, and predictive modeling experience in SQL, R and/or Python. 

• Knowledge of and ability to leverage traditional databases, cloud-based computing, and distributed computing. 

• Proficiency in data analysis and investigative tools, with experience pulling, analyzing, and visualizing large datasets from various sources.

• Strong problem-solving skills, with the ability to distill complex data into clear, actionable intelligence.

• Excellent written and verbal communication skills, with the ability to present findings and insights to both technical and non-technical stakeholders.

• A proactive approach to learning and staying up-to-date on industry trends, emerging threats, and innovative technologies.

• Knowledge of AML regulations and the USA PATRIOT Act.

• Familiarity with regulatory guidance on Model Risk Management (Federal Reserve SR Letter 11-7, OCC Bulletin 2011-12, FDIC FIL 22-2017, DFS504)

• Experience with data visualization (e.g., Tableau)

• Experience with cloud data infrastructure  (e.g., Snowflake)

• Experience with automated transaction monitoring (e.g., Verafin)

• Experience with customer/transaction screening (e.g., LexisNexis)

• CAMS certification preferred