Leveraging data analytics to enhance Bank Secrecy Act (BSA) compliance and Anti-Money Laundering (AML) efforts at Green Dot.
Requirements
- Expertise in Python, R, SQL, and machine learning frameworks.
- Experience with big data technologies such as Hadoop, Spark, or cloud-based analytics platforms.
Responsibilities
- Develop and implement machine learning models to identify potential money laundering and fraud risks.
- Analyze large datasets to detect patterns, anomalies, and trends related to financial crime.
- Enhance transaction monitoring systems using predictive analytics and AI-driven solutions.
- Collaborate with compliance teams to refine BSA/AML risk assessment methodologies.
- Conduct statistical analysis to improve the effectiveness of suspicious activity reporting (SAR).
- Work with engineering teams to integrate data science solutions into compliance platforms.
- Provide insights and recommendations to senior leadership based on data-driven findings.
Other
- All employees in this role are required to complete and maintain current training in Bank Secrecy Act (BSA), Anti-Money Laundering (AML), and Office of Foreign Assets Control (OFAC) regulations.
- 5 years of experience in data science, analytics, or financial crime prevention.
- Strong knowledge of BSA, AML, KYC, and financial regulations.
- Excellent problem-solving, analytical, and communication skills.
- Certification in Certified Anti-Money Laundering Specialist (CAMS) or Certified Fraud Examiner (CFE).
- Experience working with regulatory agencies or financial institutions.
- All applicants must be legally authorized to work in the United States at the time of application and throughout the duration of employment, without the need for current or future sponsorship.
