Gen AI for Financial Risk Management for Enhanced Modeling

Explore GPT and BERT models—transformer architectures with unidirectional vs bidirectional designs, training objectives, pre-training and fine-tuning, and their use cases in text summarization, sentiment analysis, and question answering.

Discover public LLMs such as ChatGPT and Gemini, and learn prompt engineering to craft concise contextual prompts that extract optimal outputs from LLMs for financial modeling, visualization, and regulatory compliance.

Craft clear, specific prompts for ChatGPT to minimize token use, reduce iterations, and enable concise explanations of reinforcement learning, Q-learning, neural networks, and natural language processing.

Compare BERT and GPT-2 using transformers imports, define the tokenizers (wordpiece vs byte-pair encoding), and understand their encoder vs decoder architectures and tasks like classification and generation.

Discover how BERT variants, such as Finn BERT and Bloomberg Finn LM, power sentiment analysis, document classification, risk management, and market prediction in finance.

Master prompt engineering to optimize token usage in Gen AI for financial risk management, focusing on clarity, precision, and concise prompts for cost-efficient GPT-3 and GPT-4 responses.

Explore zero-shot learning, using semantic embeddings and attribute transfer to recognize unseen concepts, enabling transfer learning for image and text tasks in financial risk modeling.

Discover variational autoencoders and generative adversarial networks for creating synthetic data in financial risk management, with code notebooks to run and charts to explore, plus a concluding white paper.

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.

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.

Explore Hugging Face's open-source transformers and pre-trained models, and learn to fine-tune for finance tasks like credit risk prediction, sentiment analysis, and fraud detection, with practical deployment.

Explore notebooks on GAN and VAE for financial risk modeling, comparing real versus generated data, and analyzing latent spaces with mean and volatility and latent factors driving interest rates.

Explore agentic AI and autonomous agents that learn to reach goals through reinforcement learning, multi-agent systems, and optimization, financial decision making, using OpenAI Gym, TensorFlow, PyTorch, cvxpy, and Ray RLlib.

Covering GPT and BERT, lecture highlights prompt engineering for token optimization and Hugging Face as an open source resource for pretrained models in finance risk modeling with big data analytics.