Gen AI for Financial Risk Management for Enhanced Modeling

Name: Gen AI for Financial Risk Management for Enhanced Modeling
Rating: 3.1 (16 reviews)

Promp Engineering, LLMs, Syntheic Data and Essential Python

Last updated 1/2025

English

What you'll learn

9 sections • 37 lectures • 1h 4m total length

Intro to the Course1:17
Introduction to the Gen AI0:25
Revised Content1:32
Into Part 21:38
Explore how generative AI powers financial risk modeling by examining LMS basics, prompt engineering, zero-shot prompts, synthetic data generation, and open-source tools like Huggingface, with case studies and white papers.

Intro to VAE-GANs0:20
Intro to VAEs0:40
Intro to GANs1:03
Intro to VAE GANs0:48
Intro to VAEs2:05
Explore variational autoencoders, a generative model that learns latent representations and generates samples. Understand encoders and decoders, the latent space, reparameterization, and the KL divergence in training.
Intro to GANs2:13
Discover generative adversarial networks, with generator and discriminator trained adversarially to create realistic data, including conditional, cycle, and Wasserstein gans for image and text generation, data augmentation, and anomaly detection.

Gen AI for Financial Risk Management for Enhanced Modeling

Course Structure:

Introduction

Lecture: Intro to the Course – An overview of the course structure, goals, and objectives in leveraging Gen AI for financial risk management.
Lecture: Introduction to Gen AI – A deep dive into how advanced artificial intelligence is transforming financial risk assessment and decision-making processes.

Large Language Models (LLMs)

Lecture: Introduction to LLMs – A comprehensive introduction to LLMs, discussing their capabilities in handling complex financial data and natural language processing tasks.
Lecture: Public LLMs Overview – A detailed examination of popular public LLMs such as BERT, GPT, and RoBERTa, and their real-world applications in finance.
Lecture: Using GPT and BERT in Finance – Practical use cases for sentiment analysis, financial forecasting, and risk evaluation using state-of-the-art language models.
Lecture: ChatGPT and Gemini in Finance – Exploring the real-time capabilities of these models to generate insights and support decision-making in financial markets.
Lecture: Model Differences – A comparative analysis of various LLMs focusing on performance, accuracy, and adaptability to financial use cases.
Lecture: BERT’s Role in Finance – In-depth exploration of BERT’s applications in document classification, regulatory compliance, and financial reporting.

Synthetic Data Techniques

Lecture: Generative Models – Understanding how VAEs and GANs create realistic synthetic financial data for model training.
Lecture: VAEs in Financial Applications – Utilizing VAEs for anomaly detection and predictive analytics in financial institutions.
Lecture: GANs for Fraud Detection – Harnessing GANs to generate synthetic data that enhances fraud detection and risk management capabilities.

Tools and Libraries for AI Development

Lecture: Hugging Face Overview – A detailed guide on how Hugging Face’s ecosystem supports financial risk management through model deployment and fine-tuning.
Lecture: Advanced Hugging Face Applications – Exploring real-time deployment of Hugging Face models to enable continuous monitoring and evaluation of financial risks.

Final Insights

Lecture: Final Words – Summarizing the key concepts, best practices, and future trends in Gen AI for financial risk management.