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Aml Model Validation Jobs in Georgia (NOW HIRING)

... indicators, and validate AI-generated outputs with sound human judgment * Monitor daily ... Complete compliance reviews for new and existing clients to ensure adherence to PCI, AML, and other ...

Aml Model Validation information

What are the key skills and qualifications needed to thrive as an AML Model Validation Analyst, and why are they important?

To excel in AML Model Validation, you typically need a strong background in quantitative analysis, statistics, and experience with anti-money laundering regulations, often supported by a degree in finance, mathematics, or a related field. Familiarity with statistical software (such as SAS, R, or Python), model validation frameworks, and knowledge of regulatory guidelines like those from the OCC or FFIEC are important. Strong analytical thinking, attention to detail, and clear communication skills set outstanding professionals apart in this role. These competencies are crucial for ensuring AML models are accurate, compliant, and effective in detecting suspicious financial activities.

What is AML model validation?

AML model validation is the process of evaluating and testing anti-money laundering (AML) models to ensure they are accurate, effective, and compliant with regulatory standards. This involves examining the model’s design, data inputs, performance metrics, and overall effectiveness in detecting suspicious activities. Regular validation helps to identify weaknesses, reduce false positives or negatives, and ensure that the model adapts to evolving risk scenarios. Financial institutions are required by regulators to validate their AML models regularly to mitigate risks and maintain robust compliance programs.

What are some common challenges faced by professionals in AML Model Validation roles, and how can they be addressed?

Professionals in AML Model Validation often encounter challenges such as ensuring models remain effective against evolving financial crime techniques and managing the complexity of regulatory expectations. They must regularly update and back-test models to address changes in transaction patterns and compliance requirements, which can be resource-intensive. Collaboration with data scientists, risk management teams, and compliance officers is crucial for interpreting results and implementing improvements. Staying current with regulatory guidance and industry best practices helps address these challenges and supports career advancement in this dynamic field.

What is the difference between Aml Model Validation vs Aml Analyst?

AspectAml Model ValidationAml Analyst
CertificationsAML certifications, model validation trainingAML certifications, compliance training
Work EnvironmentModel validation teams, risk management departmentsCompliance departments, financial institutions
Primary FocusValidating AML models, ensuring accuracy and effectivenessMonitoring transactions, investigating suspicious activities
Industry UsageFinancial institutions, banks, fintechsFinancial institutions, banks, regulatory agencies

While both roles operate within AML frameworks, Aml Model Validation focuses on testing and validating AML models to ensure they work effectively, whereas Aml Analysts handle daily transaction monitoring and investigations. The validation role emphasizes model accuracy and compliance, while analysts focus on detecting and reporting suspicious activities.

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Vice President; Quantitative Finance Analyst

Vice President; Quantitative Finance Analyst

Bank of America

Atlanta, GA • On-site

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.


Job description

Job Description:

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.
Being a Great Place to Work and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates' physical, emotional, and financial wellness through affordable, competitive and flexible benefits.
We value the unique perspectives individuals bring from all backgrounds and career paths - whether shaped by military service, community college education, or a wide range of work and life experiences. These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impact the communities we serve.
Bank of America is committed to an in-office culture that supports collaboration, engagement, and career development. Our approach includes clear in-office expectations, while providing an appropriate level of flexibility based on role-specific responsibilities and business needs.
At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!

Job Description:
This job is responsible for conducting quantitative analytics and modeling projects for specific business units or risk types. Key responsibilities include developing new models, analytic processes, or systems approaches, creating technical documentation for related activities, and working with Technology staff in the design of systems to run models developed. Job expectations include having a broad knowledge of financial markets and products.

Responsibilities:

  • Design and develop traditional and advanced statistical and machine learning (Neural Network, XGBoost, Random Forest, Support Vector Machine etc.) models to detect financial crimes and money laundering activities in bank products and channels.
  • Perform large scale data mining and pattern recognition on complex, high-dimensional datasets using SQL, SAS and Python to identify suspicious behaviors and transaction anomalies.
  • Perform ongoing monitoring and model maintenance to ensure model performance, data integrity and co-efficient stability to maintain model compliance.
  • Conduct internet and external research to understand evolving scenarios in global financial crimes and leverage those findings in feature engineering i.e., to identify variables and its transformations to be used in new model development (E.g., Transaction velocity, relative transaction volume etc.).
  • Work (support and lead) with cross-functional team of data scientists, compliance experts and technologists to build cutting-edge analytical solutions that strengthen the bank's financial crime detection framework.
  • Understand and execute activities that form the end-to-end model development and use life cycle.
  • Ensure model governance and regulatory compliance by preparing thorough documentation, support model validation, remediate validation findings and facilitate audits aligned with FinCen, OCC and FRB guidelines.
  • Drive innovation in AML model development by evaluating emerging AI/ML techniques, exploring external data sources, and contributing to strategic roadmap planning for money laundering detection capabilities.

Required Skills & Experience:

  • Master's degree or equivalent in Analytics, Mathematics, Statistics, Finance, or related: and 3 years of experience in the job offered or a related Quantitative occupation.
  • Must include 3 years of experience in each of the following: Developing predictive risk models for fraud and AML detection leveraging machine learning techniques such as deep learning using Python libraries (scikit-learn, XGBoost, LightGBM, TensorFlow/Pytorch, featuretools, NetworkX etc), SAS, and SQL based feature extraction;
  • Designing, training, and validating supervised and unsupervised statistical and machine learning models specifically for financial crime detection;
  • Extracting, transforming, and analyzing large-scale, complex datasets with a focus on identifying transaction anomalies and suspicious behavioral patterns using Pandas, NumPy, Spark/PySpark;
  • Analyzing multi-dimensional datasets to extract actionable insights for AML monitoring, sanctions screening, and fraud prevention using Python, R, SQL, Spark, Hadoop, and develop dashboards in data visualization tools like Tableau/Power BI; and,
  • Utilizing expertise in global money laundering trends, including structuring, rapid fund movement, and check sequencing, and recognizing transactional patterns and behavioral indicators associated with illicit financial activity.
  • Employer will accept pre or post Masters degree experience.
  • 10% domestic travel, as necessary.

If interested apply online at www.bankofamerica.com/careers or email your resume to bofajobs@bofa.com and reference the job title of the role and requisition number.

EMPLOYER: Bank of America N.A.

Shift:

1st shift (United States of America)

Hours Per Week: 

40