Module 1: Foundations of Generative AI – Prompts and Embeddings in Credit Risk

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Objective:

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Introduce the core building blocks of Generative AI — specifically prompt engineering and embeddings — and how these elements are transforming credit risk management. Participants will learn how LLMs interpret input, generate financial narratives, and represent meaning from unstructured data. This foundation is essential for all subsequent modules.

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       Topics Covered:

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      What is Generative AI?

• Generative vs. Discriminative models

• Applications in text, tabular data, and decision support

• Key model types: GPT, BERT, Diffusion Models, VAEs

      What is a Prompt?

• Anatomy of a prompt: instructions, examples, constraints

• Prompt engineering strategies (zero-shot, few-shot)

• Prompt tuning vs. fine-tuning

      What is an Embedding?

• Semantic representation of text

• Vector space interpretation (similarity, clustering, sentiment)

• Sentence vs. document embeddings

       Financial Applications

• Use of prompts for memo generation, loan decisions, and justification

• Use of embeddings to convert qualitative sources (news, reports) into numerical inputs for models

• Integrating embeddings with structured features in credit scoring

      Architecture Overview

• Transformer encoder/decoder overview (attention, tokenization)

• How LLMs learn relationships in financial texts

• Embedding APIs (OpenAI, HuggingFace, Cohere)

 

       Exercises: 

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• Exercise 1: Prompt a Credit Risk Memo with OpenAI or Local Model

• Use a financial data dictionary (e.g., income, DSCR, credit score)

• Build a prompt that instructs the model to generate a professional credit assessment memo

• Exercise 2: Generate and Visualize Text Embeddings

• Load 5 example borrower narratives or press releases

• Use sentence-transformers or text-embedding-ada-002 to generate embeddings

• Apply PCA or t-SNE to visualize borrower similarity clusters

 

Module 2: Foundations of Generative AI and

AI Agents in Finance

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       Concepts:

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• What is Generative AI? Comparison with Discriminative models

• Introduction to AI agents, autonomy, reasoning, memory

• Historical challenges in credit risk and where AI helps

 

       You Will Learn To:

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• Identify use cases of GenAI in risk scoring, reporting, simulation

• Understand the architecture of generative models (GANs, VAEs, LLMs)

• Understand the role of agents in credit decision automation

 

      Exercises:

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• 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

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      Concepts:

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• GAN architecture: generator, discriminator, loss function

• Credit dataset imbalance and class augmentation

• Best practices for GANs in tabular financial data

 

      You Will Learn To:

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• Train a GAN to generate realistic loan application data

• Evaluate synthetic data quality using visualization and statistical metrics

• Use synthetic data to balance minority/default classes

 

      Exercises:

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• Build a basic GAN in PyTorch or TensorFlow for credit data

• Use T-SNE/PCA to visualize real vs. synthetic borrowers

• Train a logistic regression on GAN-augmented data and evaluate AUC

 

Module 4: Variational Autoencoders (VAEs) and

Latent Credit Risk Modeling

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      Concepts:

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• Latent space encoding for borrower profiles

• Reconstruction loss and KL divergence

• Use of VAEs for dimensionality reduction and anomaly detection

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      You Will Learn To:

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• Compress borrower time series using a VAE

• Detect unusual borrowers in latent space

• Generate borrower profiles with latent features controlling risk level

 

      Exercises:

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• 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

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      Concepts:

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• Prompt engineering for financial applications

• Structured-to-unstructured generation: tabular to text

• Risks of hallucinations and mitigation strategies

 

       You Will Learn To:

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• Build prompts to generate credit memos from numerical input

• Fine-tune an LLM on internal credit narratives (optional dataset)

• Evaluate generated texts for readability, tone, and risk insights

 

       Exercises:

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• Exercise 9: Convert credit scores + income + history into a narrative with GPT-3 or Deep Seek

• Exercise 10: Score generated memos using sentiment and readability metrics

• Exercise 11: Create a prompt toolkit for various reporting formats (memo, summary, compliance)

 

Module 6: Reinforcement Learning for Credit Policy Optimization

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      Concepts:

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• Markov Decision Processes (MDP) for credit agents

• Q-learning, Deep Q Networks (DQN)

• Simulated credit environments and reward shaping

 

       You Will Learn To:

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• Create a credit simulation environment (OpenAI Gym style)

• Train an agent to learn approval strategies that balance risk and return

• Evaluate policy performance under cost constraints (defaults, missed profits)

 

       Exercises:

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• Exercise 12: Implement 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

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Module 7: AI Agents for Credit Workflow and Decision Automation

 

      Concepts:

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• LangChain and memory-enabled agents

• Tool usage: calculators, retrievers, risk engines

• Multi-step reasoning and dynamic task execution

 

       You Will Learn To:

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• Build an agent to handle a loan request from input to recommendation

• Use tools like PDF readers, calculators, and search for credit info retrieval

• Implement memory in agents to recall similar past decisions

 

       Exercises:

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• Exercises 16: Create an AI underwriting assistant using LangChain

• Exercises 17: Give the agent access to a scoring model and a document retrieval tool

• Exercises 18: Simulate 5 credit applications and record agent reasoning paths

 

Module 8: Explainability and Fairness in

Generative  AI Models

 

       Concepts:

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• Explainability for GANs, VAEs, and LLMs

• SHAP, LIME, fairness and bias audits

• Regulatory requirements for synthetic data usage

 

       You Will Learn To:

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• Audit a GAN-based credit score for biased feature generation

• Apply SHAP to interpret VAE-generated borrower data

• Test for fairness: disparate impact and equal opportunity

 

       Exercises:

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• Exercises 19: Generate SHAP values for both real and synthetic data models

• Exercises 20: Compare fairness metrics before and after synthetic augmentation

• Exercises 21: Generate an XAI report on credit scoring using LLM-generated explanations

 

Module 9: Stress Testing Credit Models

with Synthetic Scenarios

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      Concepts:

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• Macroeconomic scenario generation with GenAI

• Tail risk simulation and credit migration

• Reverse stress testing using counterfactuals

 

       You Will Learn To:

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• Generate economic downturn scenarios using VAE or GPT

• Feed scenarios into credit models and analyze PD impact

• Visualize stress impacts across segments and portfolios

 

       Exercises:

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• Exercises 22: Use LLM to generate 3 plausible adverse macroeconomic scenarios

• Exercises 23: Simulate portfolio default rates under synthetic shocks

• Exercises 24: Create a stress dashboard using Plotly/Dash

 

Module 10: Multi-Agent Simulations

of Credit Portfolios

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      Concepts:

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• Agent-based modeling (ABM) for systemic risk

• Modeling borrower-agent, lender-agent dynamics

• Contagion, feedback loops, and policy simulation

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       You Will Learn To:

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• Design borrower and lender agents with simple behavioral rules

• Simulate the effect of interest rate hikes or unemployment shocks

• Visualize emergent behaviors and defaults over time

 

       Exercises:

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• Exercises 25: Build an ABM for 1000 agents with different risk propensities

• Exercises 26: Simulate policy interventions: payment holidays, moratoria

• Exercises 27: Plot heatmaps of default clusters under different scenarios

 

Module 11: Deploying Generative AI and

Agents in Credit Risk

 

      Concepts:

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• API integration, model serving (FastAPI, Docker)

• Monitoring agent decisions for drift and bias

• Compliance with SR 11-7, ECB TRIM, and IFRS 9 expectations

 

       You Will Learn To:

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• Package models and agents into deployable services

• Implement logging and monitoring of AI agent outputs

• Build governance documentation and compliance artifacts

 

       Exercises:

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• Exercises 27: Deploy a credit scoring model as a REST API

• Exercises 28: Set up logging of LLM decisions and audit trails

• Exercises 29: Create a compliance checklist for model risk governance

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Module 12: Credit Rating using Structured and Unstructured Data with Generative AI

Goal: Equip participants with the skills to design AI-driven credit rating models that integrate both structured data (e.g., financial ratios, credit scores) and unstructured data (e.g., news, company reports, call transcripts). Generative AI, particularly LLMs, embeddings, and multi-modal learning, will be applied to process and interpret textual information to enrich traditional scoring systems.

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       Concepts:

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• Fundamentals of credit rating methodologies (internal and external ratings)

• Structured data: quantitative features (financials, ratios, credit history)

• Unstructured data: sentiment, tone, disclosure in news/reports

• Generative AI for unstructured data: transformers, embeddings, summarization

• Multi-modal fusion: combining structured and unstructured insights

• Model validation and regulatory concerns with textual features

 

      You Will Learn To:

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• Parse and embed company press releases and news articles using LLMs

• Extract credit-relevant signals (e.g., sentiment, risk language) from text

• Combine numerical and textual vectors for credit rating prediction

• Automate credit rating reports with structured + LLM-generated summaries

• Evaluate how adding textual signals improves rating model AUC or F1

 

      Exercises:​

• Exercise 30: Credit Rating Financial + News: Fusion Model

• Load a sample dataset of SME balance sheet features (structured)

• Scrape or load text data (e.g., company news, annual report excerpts)

• Use transformers (e.g., BERT or RoBERTa) to embed textual news data

• Concatenate text embeddings with financial data and fit a classification model

• Evaluate AUC and confusion matrix with and without text data

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