Decoding Market Chatter: Using Large Language Models to Extract Key Deal Insights from Trading Conversations (1)

In the intricate world of financial trading, where every second counts and accuracy is non-negotiable, the ability to swiftly and correctly extract information from trading conversations is vital. This aspect of trading is particularly crucial in risk management and compliance, sectors where the stakes are high and the margin for error is slim. Traditional methods, reliant on human expertise, grapple with the sheer volume and complexity of data, often leading to inefficiencies and potential risks. The advent of Large Language Models (LLMs) like GPT-4 heralds a new era in this landscape, offering a groundbreaking solution to these challenges.

The rapid extraction and accurate interpretation of deal-specific information from trading conversations is not just a matter of operational efficiency; it is a cornerstone of effective risk management and regulatory compliance. Inaccuracies or delays in information processing can lead to significant financial losses, compliance breaches, and reputational damage. As financial markets evolve, becoming increasingly fast-paced and regulated, the need for a more robust, automated solution becomes critical.

This blog post delves into the role of LLMs in revolutionizing the way deal information is extracted from trading conversations. We explore how these advanced AI models are adept at deciphering complex financial jargon, extracting key data points, and providing insights with unprecedented speed and accuracy. This technological leap not only streamlines the process but also significantly enhances risk management strategies and compliance protocols, ensuring that financial institutions stay ahead in a highly competitive and regulated market.

Part 1: Knowledge Distillation for Generating Synthetic Deal Information

Part 2: Utilizing Small Open-Source LLMs for Information Extraction

Part 3: Fine-Tuning Small LLMs for Enhanced Performance

Part 1: Knowledge Distillation for Generating Synthetic Deal Information

The ever-evolving landscape of financial markets is witnessing a revolutionary change with the integration of Large Language Models (LLMs). In an arena where real trading conversation data is scarce due to privacy and sensitivity, synthetic data generation through Knowledge Distillation emerges as a game-changer. This process, particularly using GPT-4, enables the creation of rich, diverse datasets crucial for training AI models in financial analysis.

Prepared Data Elements

Currencies and Currency Pairs

• Currencies: These are the individual currencies used in trading, like USD (United States Dollar), EUR (Euro), JPY (Japanese Yen), GBP (British Pound), etc.
• Currency Pairs: These are pairs of currencies that form the basis of forex trades. Examples include EURUSD (Euro/United States Dollar), GBPUSD (British Pound/United States Dollar), USDJPY (United States Dollar/Japanese Yen), etc. These pairs indicate how much of the second currency is needed to purchase one unit of the first currency.

Trade Types

• FX Spot: A foreign exchange spot transaction is a trade that involves the immediate exchange of one currency for another at the current market price.
• FX Swap: Involves exchanging a set amount of one currency for another and then reversing the trade at a later date.
• FX Vanilla Option: A financial instrument that gives the holder the right, but not the obligation, to buy or sell a currency at a predetermined price within a specified timeframe.
• Additional trade types could include more complex instruments like FX Barrier Options, Interest Rate Swaps (IRS), Equity Options, etc.

# Prepared data
currencies = ['USD', 'EUR', 'JPY', 'GBP']
currency_pairs = ['EURUSD', 'GBPUSD', 'USDJPY']
trade_types = ['FX Spot', 'FX Swap', 'FX Vanilla Option']

The Process of Creating Synthetic Data

The journey begins with the creation of JSON templates using GPT-4, each representing a unique type of financial trade. These templates outline the structure of potential conversations in various trading scenarios, such as FX Spot or FX Swaps.

trade_templates = {
    "FX Spot": {"currency_pair": "EURUSD", "rate": "1.1800", ...},
    "FX Swap": {"near_leg": {"currency_pair": "USDJPY", "rate": "110.00", ...}, ...},
    # ... other templates
}

Utilizing GPT-4’s sophisticated language understanding, we generate realistic conversations that populate these templates. This process involves crafting prompts that guide the AI to simulate actual trader dialogues.

import openai
def generate_synthetic_conversation(template):
    prompt = f"Create a detailed conversation based on this financial trade template: {template}"
    response = openai.Completion.create(prompt=prompt, model="gpt-4-1106-preview")
    return response.choices[0].text.strip()

fx_spot_conversation = generate_synthetic_conversation(trade_templates["FX Spot"])

Ensuring Realism and Diversity

To mimic the real-world variability of trading conversations, we introduce different styles, emotions, and tones. By varying these elements, the synthetic data reflects a broad spectrum of trading interactions, preparing AI models for the complexities of actual financial communication.

styles = ["formal", "casual"]
emotions = ["confident", "anxious"]
alice_style = random.choice(styles)
bob_emotion = random.choice(emotions)

prompt = f"Alice, who is {alice_style}, and Bob, who is {bob_emotion}, discuss an FX Swap trade..."

Deal Generated

{'model': 'gpt-4-1106-preview',
  'trade_type': 'FX Spot',
  'currency': 'EURRUB',
  'trader1': {'name': 'Alice',
   'style': 'formal',
   'emotion': 'optimistic',
   'tone': 'optimistic',
   'attitude': 'contrary',
   'perspective': 'synthetic'},
  'trader2': {'name': 'Bob',
   'style': 'conversational',
   'emotion': 'hopeful',
   'tone': 'serious',
   'attitude': 'realistic',
   'perspective': 'subjective'},
  'deal': {'trade_type': 'FX Spot',
   'currency_pair': 'EURRUB',
   'rate': '90.5000',
   'amount': '1000000',
   'trade_date': '2023-03-05',
   'settlement_date': '2023-03-07',
   'buyer': 'Alice',
   'seller': 'Bob',
   'conversation': [{'name': 'Alice',
     'message': 'Good day, Bob! I trust you are well. In the realm of possibility, I am interested in securing a position in EURRUB, hoping to acquire at a rate of 90.5000. Could we entertain such an optimistic transaction?'},
    {'name': 'Bob',
     'message': "Hey Alice, hope you're good too. The EURRUB spot rate you're asking for is quite optimistic given current markets. However, understanding the volatility, I'm ready to sell at that rate for the sake of moving forward with this deal."},
    {'name': 'Alice',
     'message': "Marvelous, Bob! You've always been a beacon of practicality. In the spirit of progress and cooperation, let’s formalize this transaction. A million euros exchanging hands at 90.5000 RUB per euro then?"},
    {'name': 'Bob',
     'message': "That's right Alice. Let's wrap this up. I confirm selling you EURRUB at 90.5000 for a total of 1,000,000 euros. We will have the settlement done by the 7th of March. Agreed?"},
    {'name': 'Alice',
     'message': 'Agreed, dear Bob. It is always a pleasure to conclude agreements with such clarity and precision. Until our next venture into the markets!'},
    {'name': 'Bob',
     'message': "Likewise, Alice. Take care and let's speak soon about future opportunities."}]}}

Accuracy and Realism in Synthetic Data Generation

A crucial aspect to consider when utilizing synthetic data, particularly in the sensitive realm of financial trading, is the accuracy and realism of the generated conversations. While LLMs like GPT-4 are incredibly advanced and capable of producing highly realistic dialogue, it’s important to recognize that the conversations they generate are approximations and may not always perfectly mirror real-world scenarios.

Approximate Nature of Synthetic Conversations

• Simulated Realism: The conversations generated by GPT-4 are based on patterns learned from vast amounts of text data. While they can closely mimic the structure and content of real trading conversations, they are, in essence, simulations.
• Contextual Limitations: The LLM may not fully capture the intricate nuances of specific market conditions or the subtleties of trader relationships and individual personalities that can be crucial in real-world trading environments.

Benefits Despite Imperfections

• Training and Testing: Despite not being exact replicas, these synthetic conversations are invaluable for training and testing AI models. They provide a diverse array of dialogues that AI can learn from, which is especially beneficial where actual trading data is scarce or sensitive.
• Risk Management and Compliance: In applications like risk management and compliance monitoring, having a wide range of scenarios, even if not perfectly accurate, enhances the AI model’s ability to identify potential risks or compliance issues in various contexts.

Ethical and Practical Considerations

• Transparency: When using synthetic data, it’s crucial to maintain transparency about its nature and limitations.
• Continuous Improvement: Ongoing refinement of the generation process and templates, informed by real-world data and feedback, can gradually increase the accuracy and applicability of the synthetic data.

While synthetic trading conversations generated by LLMs may not be exact replicas of real-world interactions, they offer a close approximation that is immensely useful for training AI models. The key is to understand and acknowledge their approximate nature while leveraging their strengths in diverse and comprehensive data-driven applications in finance.

Conclusion

The use of Knowledge Distillation and GPT-4 in generating synthetic financial conversations marks a significant stride in financial technology. As we continue to advance, these innovations open new avenues for AI applications in finance, reshaping how we interact with and understand the world of trading.