Fraud Risk Monitoring, FS

About the Role:

business intelligence, and risk management, along with the ability to collaborate effectively with cross-functional teams.

Key Responsibilities:

• Design, build and operationalise transaction rules and risk-scoring frameworks for real- time and near-real-time fraud prevention.
• Develop data science and machine learning (ML) models in partnership with central DS team to generate customer, transaction, and paymode trust scores, contributing to enhanced risk assessment capabilities.
• Establish standards and procedures to enable data-driven solutions throughout the analytics lifecycle, from data collection to insights generation.
• Design, plan, and execute business intelligence (BI) projects to facilitate efficient data access, usage, and reporting of rule performance for internal and external stakeholders.
• Develop and deliver analytical tools to support the reporting of product and rule performance, leveraging data insights to drive informed decisions.
• Collaborate with business stakeholders to define success metrics and establish ongoing tracking mechanisms for monitoring risk rule performance across key deliverables.
• Partner with data teams to enhance the development of robust data pipelines to support the creation and deployment of risk management rules.
• Share responsibility for the data warehouse, including data modelling activities tailored to the needs of risk management functions.
• Prepare and present solution designs and strategic plans to senior stakeholders, effectively communicating complex analytical concepts and recommendations.

Role Requirements

• Minimum 3 years of experience in risk analytics, business intelligence, or related roles within the financial services industry.
• Proven track record of designing and implementing BI projects to support data-driven decision-making and reporting.
• Strong proficiency in data analysis tools and languages such as SQL, Python, R, or similar.
• Experience with data modelling, data warehousing, and data pipeline development.
• Familiarity with risk management principles and practices, including risk rule performance tracking and monitoring.
• Excellent communication and presentation skills, with the ability to convey complex technical concepts to non-technical stakeholders.
• Demonstrated ability to work effectively in cross-functional teams and collaborate with diverse stakeholders.
• Experience in developing and deploying data science and ML models is a plus.
• Working experience in tools such as Databricks, Microsoft Power BI is a plus.

Education

• Bachelor's degree in a quantitative field such as statistics, mathematics, economics, or computer science.
• Good to have a post-graduate degree in Computer Science