Beyond productivity: Using generative AI to grow in financial services (FSI202)

Using Generative AI to Grow in Financial Services

Overview

  1. Bridgewater Associates' AI Journey

    • Bridgewater's mission is to build and maintain the best understanding of markets and economies in the world.
    • Bridgewater started an AI and ML initiative called "AI Labs" (A Labs) about 2 years ago to rethink how they understand markets and economies using AI and ML.
    • Their approach is founded on three elements: 1) everything grounded in causal relationships, 2) diagnosable at every turn, and 3) self-improving.
    • They have built a multi-agent architecture to leverage large language models (LLMs) and provide more structured and useful responses to investment-related questions.
    • The goal is to enable the AI system to go through the entire research circle (perceive, hypothesize, investigate, synthesize) on its own, with human oversight and feedback.
  2. MUFG's Transformation of Corporate Sales Activities

    • MUFG is the largest financial group in Japan, with a global presence and over 100 years of history.
    • MUFG faced challenges in their corporate sales activities, which were time-consuming, skill-dependent, and not scalable using traditional AI.
    • They leveraged generative AI to:
      • Efficiently extract relevant data from large documents
      • Scalably generate sales proposals and presentations
      • Proactively adapt the AI system based on sales feedback and performance
    • MUFG was able to increase their sales generation by 10x while maintaining a 30% conversion rate.
  3. Crypto.com's Real-Time Market Sentiment Analysis

    • Crypto.com is a licensed cryptocurrency trading platform with over 100 million users globally.
    • They faced challenges in consolidating and analyzing news and sentiment data from various sources to provide real-time market insights.
    • Crypto.com leveraged a multi-agent consensus-seeking approach, utilizing different LLMs and fine-tuning techniques, to improve sentiment analysis accuracy and responsiveness.
    • They also explored integrating market data and sentiment analysis to generate more comprehensive market insights and forecasts.
    • The AI-powered solution helped Crypto.com improve their productivity and time-to-market for new features by 10x or more.

Key Takeaways:

  • Successful adoption of generative AI in financial services requires a tight coupling between industry experts and technology experts.
  • Leveraging the strengths of LLMs while addressing their weaknesses through structured workflows and human feedback is crucial.
  • Generative AI can transform various financial services use cases, from investment research to corporate sales to real-time market analysis.
  • Continuous iteration, adaptation, and monitoring of the AI systems are essential to ensure their effectiveness and relevance.

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