Module 0: Quantum Computing and Algorithms Exercise 1: Quantum operations Module 1: Artificial Intelligence for Credit Scoring (No exercises) Module 2: AI in Credit Scoring (No exercises) Module 3: Machine Learning (No exercises) Exploratory Data Analysis & Feature Engineering Module 4: Exploratory Data Analysis (No exercises here; all exercises are in Module 5) Module 5: Feature Engineering Exercise 1: Exploratory Analysis in R Exercise 2: Detection and Treatment of Advanced Outliers Exercise 3: Stratified and Random Sampling in R Exercise 4: Multivariate imputation model Exercise 5: Univariate analysis in percentiles in R Exercise 6: Continuous variable optimal univariate analysis in Excel Exercise 7: Estimation of KS, Gini, and IV in Excel Exercise 8: Word Cloud analysis in R Machine Learning Module 6: Unsupervised Models Exercise 14: Core components in R and SAS Exercise 15: K-Means segmentation in R Module 7: Logistic Regression & LASSO Exercise 16: Logistic Regression in SAS & R Exercise 17: LASSO Logistic Regression in R Exercise 18: Model risk using confidence intervals Module 8: Trees, KNN & Naive Bayes Exercise 19: Decision Tree in SAS & R Exercise 20: KNN in R & SAS Exercise 21: Naive Bayes in R Module 9: Support Vector Machine (SVM) Exercise 22: SVM in R (data 1) Exercise 23: SVM in Python (data 2) Module 10: Ensemble Learning Exercise 24: Boosting in R Exercise 25: Bagging in R Exercise 26: Random Forest in R & Python Exercise 27: Gradient Boosting Trees Model Validation Module 11: Validation of ML & Traditional Models Exercise 28: Logistic Regression Goodness-of-Fit Test Exercise 29: Cross-validation in SAS Exercise 30: Gini, IV, Brier, Lift, CAP, ROC in SAS/Excel Exercise 31: Bootstrapping of SAS parameters Exercise 32: Jackknifing in SAS Exercise 33: ROC/Gini Bootstrapping in SAS Exercise 34: Kappa estimation Exercise 35: K-Fold Cross-Validation in R Exercise 36: Traffic-light validation (6-year horizon) Module 12: Stability Testing Exercise 37: Stability tests of models and factors Deep Learning Module 14: Introduction to Deep Learning (No exercises) Module 15: Deep Learning Feed Forward Neural Networks Exercise 38: Deep Learning Feed Forward Credit Scoring Module 16: Convolutional Neural Networks (CNN) Exercise 39: Credit Scoring using CNN Module 17: Recurrent Neural Networks (RNN / LSTM) Exercise 40: Credit Scoring using LSTM Module 18: Generative Adversarial Networks (GANs) Exercise 41: Credit Scoring using GANs Module 19: Calibration of ML & DL Models Exercise 42: Optimization — XGBoost, Random Forest & SVM Exercise 43: Optimized Deep Learning Credit Scoring Scorecard Construction Module 20: Traditional Scorecard Construction Exercise 44: Building Scorecard (Excel, R, Python) Exercise 45: Optimum cut-off point estimation Exercise 46: Confusion matrix: Type I & Type II error Quantum Machine Learning Module 21: Quantum Credit Scoring Exercise 47: Quantum K-means Exercise 48: Quantum SVM Exercise 49: Quantum Feed Forward NN Exercise 50: Quantum Convolutional NN Module 22: Tensor Networks for QML Exercise 51: Credit scoring with tensor networks Probabilistic Machine Learning Module 23: Probabilistic Machine Learning Exercise 52: Gaussian Process Regression Exercise 53: Bayesian Neural Networks Credit Scoring Model Risk Module 24: Model Risk in Credit Scoring Exercise 54: Model risk due to not recalibrating Credit Scoring Applications Module 25: Credit Scoring Models by Product Exercise 55: Behavior Score Logistic Regression (Python) Exercise 56: Behavior Score SVM (Python) Exercise 57: Behavior Score Random Forest (Python) Exercise 58: Behavior Score Gradient Boosting (Python) Exercise 59: Behavior Score LSTM (Python) Module 26: Typology of Scoring Models Exercise 60: Fraud Score with Neural Networks Exercise 61: Income Score Exercise 62: Collection Score Exercise 63: Recovery Score Exercise 64: Churn/Quit Score Calibration of PD Models Module 27: PD Calibration (IRB) Exercise 65: PD Calibration in ML Models Module 28: Machine Learning for Lifetime PD (IFRS 9) Exercise 66: PD Lifetime — Logistic Regression Exercise 67: PD Lifetime — Multinomial Regression Exercise 68: PD Lifetime — SVM Exercise 69: PD Lifetime — Deep Learning Exercise 70: PD Lifetime — LSTM Validation of PD Models Module 29: Validation of PD Models Exercise 71: Backtesting PD in Excel Exercise 72: Forecasting PD vs Real PD Automation of Credit Scoring & PD Module 30: Automation of Modeling with AI Exercise 73: Automated scoring — modeling, optimization & validation Exercise 74: Automation of PD modeling & validation

Module 1: Foundations of Generative AI – Prompts and Embeddings in Credit Risk Exercise 1: Prompt a Credit Risk Memo with OpenAI or a local model Exercise 2: Generate and visualize text embeddings (PCA or t-SNE on borrower narratives) Module 2: Foundations of Generative AI and AI Agents in Finance Exercise 3: Generate synthetic borrower names and profiles using LLMs Exercise 4: Simulate a simple agent making approve/reject decisions based on rules Exercise 5: Create a credit applicant description using a pre-trained language model Module 3: Generative Adversarial Networks (GANs) for Credit Data Synthesis Exercise: Build a basic GAN in PyTorch or TensorFlow for credit data Exercise: Use t-SNE/PCA to visualize real vs. synthetic borrowers Exercise: Train a logistic regression on GAN-augmented data and evaluate AUC Module 4: Variational Autoencoders (VAEs) and Latent Credit Risk Modeling Exercise 6: Implement a VAE to reduce credit scoring data from 20 to 5 dimensions Exercise 7: Cluster latent borrower profiles to segment high-risk borrowers Exercise 8: Generate 100 new synthetic applicants using decoder manipulation Module 5: LLMs for Generating Credit Narratives and Reports Exercise 9: Convert credit scores, income, and history into a narrative with GPT-3 or DeepSeek Exercise 10: Score generated memos using sentiment and readability metrics Exercise 11: Create a prompt toolkit for different reporting formats (memo, summary, compliance) Module 6: Reinforcement Learning for Credit Policy Optimization Exercise 12: Implement a Q-learning agent for loan approvals (accept/reject) Exercise 14: Introduce penalty for wrong approvals and reward for correct rejections Exercise 15: Compare RL agent vs. logistic model on profit-maximizing loan strategy Module 7: AI Agents for Credit Workflow and Decision Automation Exercise 16: Create an AI underwriting assistant using LangChain Exercise 17: Give the agent access to a scoring model and a document retrieval tool Exercise 18: Simulate 5 credit applications and record the agent’s reasoning paths Module 8: Explainability and Fairness in Generative AI Models Exercise 19: Generate SHAP values for both real and synthetic data models Exercise 20: Compare fairness metrics before and after synthetic augmentation Exercise 21: Generate an XAI report on credit scoring using LLM-generated explanations Module 9: Stress Testing Credit Models with Synthetic Scenarios Exercise 22: Use an LLM to generate 3 plausible adverse macroeconomic scenarios Exercise 23: Simulate portfolio default rates under synthetic shocks Exercise 24: Create a stress dashboard using Plotly/Dash Module 10: Multi-Agent Simulations of Credit Portfolios Exercise 25: Build an agent-based model (ABM) for 1,000 agents with different risk propensities Exercise 26: Simulate policy interventions (payment holidays, moratoria) Exercise 27: Plot heatmaps of default clusters under different scenarios Module 11: Deploying Generative AI and Agents in Credit Risk Exercise 27: Deploy a credit scoring model as a REST API Exercise 28: Set up logging of LLM decisions and audit trails Exercise 29: Create a compliance checklist for model risk governance Module 12: Credit Rating using Structured and Unstructured Data with Generative AI Exercise 30: Credit Rating – Financial + News Fusion Model (structured financials + text embeddings + classification; compare AUC with and without text)