Prerequisites

• Proficiency in data analysis tools (e.g., Python, R).
• Solid understanding of statistical methods.
• Basic knowledge of risk management principles.

Learning Outcomes

By the end of this course, participants will:
1. Apply advanced analytics to identify and assess financial risks.
2. Develop predictive models for credit, market, and operational risks.
3. Implement machine learning techniques for fraud detection.
4. Integrate regulatory requirements into risk management practices.
5. Utilize data visualization tools to communicate risk insights effectively.

Course Outline

• Fundamentals of Risk Management in Kenya
• Data Analytics for Credit Risk Assessment
• Market and Operational Risk Analytics
• Fraud Detection Using Machine Learning
• Communicating Risk Insights

Duration: 5 days

Target Audience
Risk managers, financial analysts, data scientists, and professionals involved in risk assessment and management
within financial institutions