ML Benchmarks & AWS Migration Boost FX Profitability, Scalability & Cut Costs

Industry: Finance, FX Trading

Client

Global Foreign Exchange Trading Provider

Goal

To enhance benchmark analytics for foreign exchange pairs and improve transaction cost analysis (TCA) across additional asset classes.

Challenges

  • Extending existing systems to include additional asset classes.
  • Incorporating machine learning models to predict the most profitable benchmarks for specific risk portfolios.
  • Migrating calculations to the cloud to improve scalability and efficiency.

Solution

Migrated calculations from physical servers to AWS, enabling more scalable and efficient operations.

Developed and implemented machine learning models to predict benchmark profitability for risk portfolios.

Provided high-level advisory on AI-driven trading strategies and risk management, leveraging data science tools to enhance decision-making.

Impact:

Extended benchmark analytics to multiple asset classes, improving the accuracy of profitability predictions for risk portfolios.

Migrated infrastructure to AWS, significantly enhancing scalability and reducing operational costs.

Delivered data-driven insights that optimized trading strategies and strengthened risk management practices.

*Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.