Job Summary
As a Financial Crime Analytics Lead, you will lead and manage multiple and competing stakeholder relationships, balance long and short-term project deliverables to ensure the successful delivery of project driven change activity in Financial Crime systems and processes.
Mandatory Skill-set
- 7+ years of relevant professional experience in financial crime, sanctions, fraud, legal, risk management or finance area;
- 3 - 5 years of relevant Financial Crime Analytics experience one or more of the following areas:
Anti Money Laundering, Financial Crime, Transaction Monitoring, Customer KYC/CDD, Customer screening, etc. - Advanced skills in development, validation and monitoring of AML analytics models, strategies, visualizations;
- Understanding of evolving methodologies/ tools/ technologies in the Financial Crime management space;
- Experience in building models using AI/ML methodologies;
- Modeling: Experience in one or more of analytical tools such as Qlik, R, Python, SQL, etc.
- Knowledge of data processes, ETL and tools/ vendor products such as Fenergo, NICE Actimize, SAS AML, Quantexa, Ripjar, etc.
Desired Skill-set
- PMP (Project Management Professional) certification.
Responsibilities
- Take the lead to ensure systems and processes relating to customer risk models, coverage assessments and reports are well documented and maintained;
- Comply to Organization Frameworks, policies and any regulatory requirements;
- Collaborate with various departments (compliance, risk, legal, etc.) to gather input on the design, methodology, and assumptions used in customer risk models;
- Take ownership of documenting and presenting technical results to non-technical business audiences, including the formation and delivery of effective recommendations and solutions;
- Monitor and assess regulatory changes and advise the business on how these impact current risk models and financial crime detection processes;
- Apply thought leadership to enhance data analytics, including use of artificial intelligence and machine learning;
- Drive innovation by researching and implementing emerging technologies, such as artificial intelligence (AI) and machine learning (ML), to improve the accuracy, efficiency, and predictive capabilities of financial crime detection models;
- Work with data science and engineering teams to design and optimize automated systems for monitoring customer behavior, identifying suspicious activities, and refining the overall risk assessment process;
- Evaluate the effectiveness of AI/ML models in detecting financial crime and continuously enhance model performance by iterating on algorithms, refining data inputs, and ensuring their alignment with the organization's objectives and regulatory requirements.
