Analyst: FRM & Data Analytics

Description:

Job purpose

• The AML and Fraud Risk Data Analyst plays a critical role in the detection, prevention, and mitigation of AML and fraudulent activities within an organization. This role involves leveraging data analytics, statistical methods, and specialized software to analyse and interpret data, identify patterns, and provide insights that help prevent and combat fraud.

Key responsibilities

Data Analysis

• Data Collection: Gather and aggregate data from various sources, including transaction records, customer databases, and external data feeds.
• Data Cleaning: Ensure data accuracy by cleaning and preprocessing data, removing duplicates, and correcting errors.

Fraud Detection

• Pattern Recognition: Identify and analyse patterns, trends, and anomalies in data that may indicate fraudulent activity.
• Algorithm Development: Develop and implement algorithms and rules-based models to detect potential fraud, using techniques such as AI, Machine Learning, statistical analysis, and predictive modelling.

Investigation and Reporting

• Case Investigation: Assist in investigating suspected fraud cases by providing data-driven insights and supporting evidence.
• Reporting: Prepare detailed reports and visualizations to communicate findings to stakeholders, including senior management, compliance officers, and risk officers.

Monitoring and Surveillance

• Real-Time Monitoring: Implement and maintain real-time monitoring systems to detect and respond to suspicious activities promptly.
• Alert Management: Manage alerts generated by fraud detection systems, prioritize them based on risk, and initiate appropriate responses.

Risk Assessment

• Risk Scoring: Develop and apply risk scoring models to assess the likelihood and impact of potential fraud.
• Risk Mitigation: Recommend, document, and implement measures to mitigate identified risks, including process, control, and system changes, within the Fraud Risk Register.

Dashboard Building and Maintenance

• Create Flows: Develop SharePoint lists for task management and establish automated flows to integrate data into Power BI.
• Build BI Dashboard: Design and implement Power BI dashboards to provide actionable insights on tasks.
• Maintenance: Ensure ongoing maintenance of dashboards to uphold data accuracy, reliability, and functionality.

Continuous Improvement

• Trend Analysis: Analyse emerging trends in fraud tactics and adapt detection and prevention strategies accordingly.
• Process Improvement: Continuously evaluate and improve fraud detection processes, tools, and technologies to enhance effectiveness.

Internal and external relationships

• Inter-departmental Collaboration: Work closely with other departments, such as compliance, IT, and operations, to ensure a coordinated approach to fraud prevention.
• External Liaison: Collaborate with external entities, such as law enforcement agencies and other financial institutions, during fraud investigations and information sharing.

Competencies and Behaviours required

Qualifications and Experience

Qualifications in (any combination of 2 or more):

Required:

• Information Technology, Data Analysis, or Artificial

Intelligence and Machine Learning.

Preferred:

• Criminology
• Science, Technology, Engineering or Mathematics
• Statistics
• Economics
• Business Analytics

Experience Required:

• Experience in Data Analysis (12-18 Months)

Knowledge, Skills & Abilities Required

Data Analysis Tools

• Proficiency in tools such as SQL, Excel, R, and SAS for data extraction, manipulation, and analysis.
• Experience with data visualization tools like Tableau, Power BI, or QlikView, etc.

Behaviours required

Strong analytical and investigative skills, meticulous attention to detail, effective communication, and the ability to adapt and learn continuously, all while maintaining integrity and ethical judgment.

Requirements:

• The AML and Fraud Risk Data Analyst plays a critical role in the detection, prevention, and mitigation of AML and fraudulent activities within an organization. This role involves leveraging data analytics, statistical methods, and specialized software to analyse and interpret data, identify patterns, and provide insights that help prevent and combat fraud.

• Data Collection: Gather and aggregate data from various sources, including transaction records, customer databases, and external data feeds.
• Data Cleaning: Ensure data accuracy by cleaning and preprocessing data, removing duplicates, and correcting errors.

• Pattern Recognition: Identify and analyse patterns, trends, and anomalies in data that may indicate fraudulent activity.
• Algorithm Development: Develop and implement algorithms and rules-based models to detect potential fraud, using techniques such as AI, Machine Learning, statistical analysis, and predictive modelling.

• Case Investigation: Assist in investigating suspected fraud cases by providing data-driven insights and supporting evidence.
• Reporting: Prepare detailed reports and visualizations to communicate findings to stakeholders, including senior management, compliance officers, and risk officers.

• Real-Time Monitoring: Implement and maintain real-time monitoring systems to detect and respond to suspicious activities promptly.
• Alert Management: Manage alerts generated by fraud detection systems, prioritize them based on risk, and initiate appropriate responses.

• Risk Scoring: Develop and apply risk scoring models to assess the likelihood and impact of potential fraud.
• Risk Mitigation: Recommend, document, and implement measures to mitigate identified risks, including process, control, and system changes, within the Fraud Risk Register.

• Create Flows: Develop SharePoint lists for task management and establish automated flows to integrate data into Power BI.
• Build BI Dashboard: Design and implement Power BI dashboards to provide actionable insights on tasks.
• Maintenance: Ensure ongoing maintenance of dashboards to uphold data accuracy, reliability, and functionality.

• Trend Analysis: Analyse emerging trends in fraud tactics and adapt detection and prevention strategies accordingly.
• Process Improvement: Continuously evaluate and improve fraud detection processes, tools, and technologies to enhance effectiveness.

• Inter-departmental Collaboration: Work closely with other departments, such as compliance, IT, and operations, to ensure a coordinated approach to fraud prevention.
• External Liaison: Collaborate with external entities, such as law enforcement agencies and other financial institutions, during fraud investigations and information sharing.

• Information Technology, Data Analysis, or Artificial

• Criminology
• Science, Technology, Engineering or Mathematics
• Statistics
• Economics
• Business Analytics

• Experience in Data Analysis (12-18 Months)

• Proficiency in tools such as SQL, Excel, R, and SAS for data extraction, manipulation, and analysis.
• Experience with data visualization tools like Tableau, Power BI, or QlikView, etc.