Generative AI in Financial Industry
summary
Overview of concrete LLM use cases in finance: building customer chatbots by prompt engineering or fine‑tuning on proprietary repos, auto‑generating localized content and icons with Llama 3 and Amazon Bedrock, converting videos into transcripts then chunking for targeted Q&A, and classification of document sensitivity before model ingestion. Calls out data drift with a COVID demand‑forecasting example, data‑quality and feature‑engineering checks, monitoring, sandboxing, and infrastructure cost tradeoffs between transparency and explainability.
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chapters (14)
- 0:00Introduction to speaker
- 0:47What is generative AI
- 4:01Deep learning in generative AI
- 5:45Major milestones in generative AI
- 6:52Common use cases in banking
- 7:37Content generation examples
- 8:18Video transcription with LLMs
- 9:22Language translation and localization
- 10:49Responsible AI considerations
- 13:40Ethics and model sensitivity
- 15:04Responsible AI principles
- 16:06Building AI awareness across teams
- 16:48Infrastructure and cost planning
- 17:31Closing and Q&A
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