Developed with an investment of over USD 270 million, Behavox LLM 2.0 underpins our integrated ecosystem of products that offer users unparalleled visibility into their businesses. While earlier Behavox insights were derived using traditional machine learning methods, the introduction of LLMs has revolutionized the quality, cost-efficiency, and impact of these insights. Customers include J.P. Morgan, Brevan Howard, and SMBC.

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1. Strategic Leadership Contributions

Team Assembly & Vision

• Built a 40-person AI research team with:
  • 10 NLP researchers (specializing in financial corpus training)
  • 5 data engineers (handling proprietary datasets)
  • 3 compliance experts (ensuring regulatory alignment)
  • 2 AI ethicists (bias mitigation & data privacy)
  • 20 ML engineers (scaling training & deployment)

Key Decision: Partnered with top-tier banks & hedge funds to acquire annotated proprietary datasets (emails, trade logs, SEC filings).

Alignment with Business Goals

• Worked with C-suite to define 3 core LLM differentiators:
  1. Regulatory Precision (SEC, FINRA, MiFID II-aware)
  2. Financial Jargon Mastery (trading slang, compliance codes)
  3. Low Hallucination Rate (<2% vs. ~15% in GPT-4)

Key Innovations I Led:

A. Data Curation

• Training Corpus:
  • 10M+ financial documents (emails, Bloomberg chats, SEC filings)
  • Regulatory datasets from 50+ agencies (FINRA, FCA, etc.)
  • Synthetic data for edge cases (e.g., spoofing detection)

• Preprocessing:
  • De-identification pipeline (HIPAA/GDPR compliant)
  • Entity recognition for traders, funds, instruments

B. Model Development

• Base Model: Started with Llama 2-70B (commercially viable)
• Domain Adaptation:
  • Continued pretraining on financial texts (vocabulary expansion)
  • Added regulatory knowledge graphs via retrieval augmentation
• Fine-Tuning:
  • Supervised learning on 3 key tasks:
    1. Anomaly detection (market manipulation patterns)
    2. Regulatory citation (linking text to rules)
    3. Risk scoring (SAR/AML flags)
  • Used LoRA for efficient parameter updates

C. Reinforcement Learning (RLHF)

• Human Feedback:
  • 50+ compliance officers ranked outputs
  • Traders evaluated jargon accuracy
• AI Feedback:
  • Automated checks against known regulatory databases

D. Deployment

• On-Prem & Cloud: Optimized for NVIDIA HGX H100 clusters
• Latency: <500ms for real-time surveillance
• Security: FIPS 140-2 validated encryption