Upon completion of this course, participants will be able to:

• Apply time-series models (e.g., ARIMA, GARCH) to forecast market volatility and asset returns.
• Utilize machine learning algorithms for enhanced credit default prediction and risk profiling.
• Develop advanced dashboards and visualizations tailored for executive financial reporting and regulatory submission.
• Implement network analysis and anomaly detection techniques to identify complex financial fraud patterns.
• Perform advanced portfolio optimization using quantitative analytical methods.
• Analyze and interpret customer transactional data for effective behavioral segmentation and product recommendation.
• Ensure analytical processes comply with financial regulations, including model risk governance.
• Apply scripting languages (R or Python) to automate complex financial data cleaning and analysis tasks.

• Financial Analysts and Quantitative Analysts
• Risk Management Professionals
• Business Intelligence (BI) Specialists in Finance
• Credit and Loan Officers
• Internal Auditors and Compliance Specialists in Banking
• Investment and Portfolio Managers
• Senior Treasury and Finance Staff

The training is delivered via a mix of theoretical concepts and intensive practical application. **Case studies** involve analyzing proprietary credit risk datasets to build predictive models. **Scenarios** include simulating a market volatility event and requiring participants to update risk dashboards in real-time. **Group activities** focus on designing an advanced fraud detection strategy for a mobile banking platform. **Individual exercises** require coding financial time-series analysis in a provided environment (R/Python). **Mini-case studies** address the ethical implications and governance of deploying black-box models in lending decisions, followed by **syndicate discussions**.

UNIT 1: Foundations of Financial Data Modeling

• Sourcing and Cleaning High-Frequency Financial Data (Tick Data, Market Snapshots)
• Key Financial Ratios and Metric Calculation (VaR, Sharpe Ratio, Beta)
• Advanced Feature Engineering from Transactional Data
• Understanding and Modeling Financial Time Series Data
• Introduction to Model Risk Management (MRM) and Governance

UNIT 2: Predictive Risk and Credit Analytics

• Building Classification Models for Credit Default Prediction (Logistic Regression, Trees)
• Segmenting Customers by Risk Profile and Behavioural Metrics
• Implementing Scorecard Models and Interpretation Techniques (SHAP, LIME)
• Analyzing Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD)
• Regulatory Requirements for Model Transparency (e.g., Explainable AI)

UNIT 3: Fraud Detection and Anomaly Analysis

• Techniques for Anomaly and Outlier Detection in Transaction Streams
• Using Unsupervised Learning (Clustering) for Fraud Pattern Identification
• Implementing Graph Databases and Network Analysis for Connected Fraud Rings
• Real-time Monitoring and Alerting Systems Implementation
• Case Studies: AML and KYC Data Analysis Techniques

UNIT 4: Portfolio Optimization and Market Forecasting

• Applying Modern Portfolio Theory (MPT) and Capital Asset Pricing Model (CAPM)
• Time-Series Forecasting Models: ARIMA, Prophet, and Advanced Deep Learning Approaches
• Backtesting and Stress Testing Analytical Models
• Visualizing Uncertainty and Volatility in Forecasts
• Using Monte Carlo Simulations for Risk Quantification

UNIT 5: Executive Visualization and Reporting

• Designing Executive Dashboards focused on Liquidity, Capital, and Risk Exposure
• Best Practices for Visualizing Regulatory Reports (BCBS 239, IFRS 9)
• Using advanced charting (Candlestick, Sankey) for market movement and flow analysis
• Storytelling with Data: Constructing Narratives for Board and Stakeholders
• Interactive Visualization Techniques for Drill-Down Analysis