AI for Finance
Beyond Traditional Methods
Traditional machine learning often struggles with the chaotic nature of financial markets. Our quantum-inspired approach excels at identifying patterns in highly complex, volatile data, providing more accurate forecasts that adapt to changing market conditions.
- Identify market trends
- Adapt to changing conditions in real-time
- Process multimodal financial data efficiently
- Reduce computational resources needed for complex models
Research: QCML in Corporate Bond Markets
Our recent research with BlackRock shows our technology outperforms traditional methods for corporate bond analysis, especially in high-yield markets. We excel at finding patterns in sparse, complex financial data.
Read the research paper →Bond Similarity Learning
Our QCML technology creates distance metrics between securities, helping traders find suitable alternatives for illiquid bonds and identify securities with similar risk-return profiles.
Alternative Security Pricing
Price securities with limited market data by leveraging our quantum-inspired similarity measures to help identify comparable tradable securities with up-to-date pricing.
Risk Management
Create hedging strategies by identifying similar securities for more effective risk management.
Efficient Data Processing
Our models require fewer computing resources than traditional approaches while delivering better results. This makes our technology cost-effective for handling complex financial data.
We excel at finding signals in sparse data - precisely where traditional methods struggle the most. This is particularly valuable in applications where quality data is limited.