Total 1 financial planner job vacancies in ITE-CE (College East)

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.