AI For Banks

Overview

1 day practical course on the latest cutting-edge artificial intelligence (AI) applications by banks in their lending and trading book decision-making and risk management.

Learning Objectives

How AI is being used by banks to make retail loans and to risk manage these – credit decisioning, probability of default (PD) modelling and loan pricing
How AI is being used by banks in automation of corporate financial statement analysis, including templating of financials, extraction of key credit and ESG metrics and generation of credit risk memos
How AI is being used by banks in liquidity stress-testing

Who the course is for

Risk management
Quants
Traders

Day One Morning

AI – Credit Risk – Retail

Comparison – traditional vs AI credit scoring of, and credit decisioning with respect to, retail customers
Alternative data sources for retail customers
AI probability of default (PD) modelling for retail loans
AI for retail loan pricing and expected credit loss (ECL) provisioning
AI generation of early warning signals for borrower stress to amend uncommitted credit facilities
Use of LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) to explain AI-generated outputs (xAI). Regulatory expectations around explainability
Case Study 1 – Gradient Boosting Machines vs logistic regression for retail customer PD modelling, risk-based loan pricing, ECL provisioning and credit decisioning. Results of the two methods compared
Case Study 2 – Random Forests for retail customer credit scoring, fraud detection and early warning signals
Case Study 3 – Use of LIME to explain why a retail loan application was declined
Case Study 4 – Neural Networks for retail customer PD modelling

AI – Credit Risk – Corporate

Challenges in applying AI for corporate loan credit risk modelling
AI in financial statement analysis – extraction of key credit (and ESG) metrics and generation of credit risk memos
Case Study 5 – Using OCR (Optical Character Recognition) and computer vision to compile templated financial statements and extract key credit and ESG metrics
Case Study 6 – Using NLP (Natural Language Processing) to generate credit risk memos

Day One Afternoon

AI – Market Risk

Case Study 7 – LSTM (Long Short-Term Memory) and GARCH combined with machine learning (ML) to forecast volatility
Case Study 8 – Deep learning model to forecast asset prices
Case Study 9 – Using AI to estimated Stressed Expected Shortfall
Case Study 10 – Market risk stress-testing using: (a) Generative Adversarial Networks (GANs); (b) Variational Autoencoders (VAEs); (c) Reinforcement Learning

AI – Liquidity Risk

Case Study 11 – Using GANs, VAEs and Reinforcement Learning to generate liquidity stress scenarios
Case Study 12 – Using LSTM to project stressed cash inflows and outflows and measure liquidity buffer survival horizon