Introduction

Risk management is a critical component of both the banking and insurance industries. In the face of economic volatility, regulatory changes, and emerging risks, financial institutions must develop robust risk analytics capabilities to safeguard their operations. Risk analytics enables organizations to assess, measure, and mitigate various types of risk, including credit, market, operational, and liquidity risk. This course will provide participants with an in-depth understanding of risk analytics techniques, the application of statistical models, and the use of data-driven decision-making tools to optimize risk management strategies in both banking and insurance sectors.

Through a combination of theory and practical exercises, participants will learn how to apply risk analytics tools to real-world scenarios, enabling them to make informed decisions that reduce risk exposure and enhance profitability.

Objectives

By the end of this course, participants will:

• Understand the fundamentals of risk management and its importance in banking and insurance.
• Learn how to assess and quantify various types of risk: credit, market, operational, and liquidity.
• Develop skills in using data analytics and statistical models to evaluate risk exposure.
• Understand regulatory requirements and industry standards for risk management, including Basel III, Solvency II, and IFRS 9.
• Master the use of key risk metrics: Value-at-Risk (VaR), stress testing, credit scoring, and scenario analysis.
• Gain hands-on experience with tools like Excel, R, Python, and SAS for risk modeling and data analysis.
• Learn how to build and validate predictive models for risk forecasting and decision support.

Who Should Attend?

This course is ideal for:

• Risk managers and professionals in banking and insurance sectors.
• Data analysts, financial analysts, and business intelligence professionals working in risk management.
• Executives and decision-makers who need to understand risk analytics for better strategic planning.
• Individuals looking to enhance their skills in risk quantification, modeling, and management.

Day 1: Introduction to Risk Management and Analytics

Morning Session: Introduction to Risk Management

• The role of risk management in banking and insurance.
• Types of risks in financial institutions: Credit, market, operational, and liquidity risks.
• Risk frameworks: Enterprise Risk Management (ERM), risk appetite, and risk tolerance.
• Regulatory overview: Basel III, Solvency II, and IFRS 9 guidelines.
• Overview of risk analytics tools and techniques.
• Hands-on: Analyze a case study of a risk management failure in banking or insurance.

Afternoon Session: Key Risk Metrics and Tools

• Overview of key risk metrics: Value-at-Risk (VaR), expected shortfall, and stress testing.
• Introduction to credit risk metrics: Credit scores, probability of default (PD), loss given default (LGD), and exposure at default (EAD).
• Liquidity risk and market risk: Duration, liquidity gaps, and market volatility.
• Hands-on: Calculate simple VaR for a portfolio using historical data in Excel.

Day 2: Credit Risk Analytics

Morning Session: Understanding Credit Risk

• What is credit risk and how does it impact financial institutions?
• Credit risk models: Credit scoring, credit ratings, and PD-LGD-EAD framework.
• How to evaluate creditworthiness using statistical and machine learning models.
• Credit risk portfolio management: Diversification, concentration risk, and risk-adjusted return.
• Hands-on: Build a credit scoring model using logistic regression in R.

Afternoon Session: Credit Risk Modelling Techniques

• Advanced techniques for credit risk modeling: Machine learning, decision trees, and neural networks.
• Credit risk mitigation strategies: Collateral, guarantees, and credit derivatives.
• Case studies: Assessing credit risk in corporate loans, mortgages, and credit cards.
• Hands-on: Use Python to implement a basic credit risk model using customer data.

Day 3: Market and Operational Risk Analytics

Morning Session: Market Risk Analytics

• Introduction to market risk: Factors influencing market risk (interest rates, foreign exchange, equity, and commodities).
• Quantifying market risk using VaR and stress testing.
• Value-at-Risk methodologies: Historical simulation, variance-covariance, and Monte Carlo simulation.
• Hands-on: Use Excel to compute VaR for a portfolio of financial assets.

Afternoon Session: Operational Risk Analytics

• Understanding operational risk: Fraud, system failure, compliance risk, and human error.
• Operational risk management frameworks: Basel II and III, Solvency II.
• How to measure operational risk: Scenario analysis, risk self-assessments, and loss data collection.
• Hands-on: Analyze operational risk using historical loss data in R or SAS.

Day 4: Advanced Risk Analytics Techniques

Morning Session: Predictive Modeling for Risk Forecasting

• Introduction to predictive analytics: Forecasting risk using statistical models.
• Techniques for building predictive models: Regression analysis, decision trees, and machine learning algorithms.
• Case studies: Using predictive models for credit scoring, market risk forecasting, and fraud detection.
• Hands-on: Build a predictive model for credit default prediction using R.

Afternoon Session: Stress Testing and Scenario Analysis

• Stress testing techniques: Designing scenarios to assess financial resilience under extreme conditions.
• Regulatory requirements for stress testing: Bank of England, Federal Reserve, and European Central Bank guidelines.
• Scenario analysis for both market and operational risks.
• Hands-on: Perform stress testing on a bank’s balance sheet using hypothetical adverse scenarios in Excel.

Day 5: Risk Management Implementation and Reporting

Morning Session: Implementing Risk Analytics in Banking and Insurance

• Integrating risk analytics into business decision-making: Credit underwriting, pricing, portfolio management, and investment strategies.
• Operationalizing risk models: Automation of risk analytics processes, real-time monitoring, and reporting.
• Developing a risk appetite framework for financial institutions.
• Hands-on: Design a risk management strategy using analytics for a hypothetical financial institution.

Afternoon Session: Reporting and Communicating Risk Findings

• Best practices for communicating risk analytics results to stakeholders.
• Reporting tools and dashboards: Building risk dashboards using Power BI or Tableau.
• Key considerations in risk reporting: Clarity, transparency, and alignment with business goals.
• Hands-on: Create a risk reporting dashboard using Power BI, including key metrics like VaR, stress test results, and credit risk indicators.

Materials and Tools:

• Required tools: Excel, R, Python, SAS, Power BI
• Sample datasets: Bank loan portfolios, market data, operational risk loss events, stress test scenarios.
• Recommended resources: Online guides for R, Python, SAS, and Power BI, along with regulatory reports (e.g., Basel III, Solvency II).