Explainable AI in Request-for-Quote

TL;DR

This paper applies explainable AI models to predict RFQ fulfillment in financial markets, enhancing prediction accuracy and providing transparent decision tools for market participants.

Contribution

It introduces the use of multiple advanced XAI algorithms for RFQ prediction, improving accuracy and transparency in financial market decision-making.

Findings

Improved RFQ fill rate prediction accuracy

Enhanced transparency with XAI models

More efficient quote price generation

Abstract

In the contemporary financial landscape, accurately predicting the probability of filling a Request-For-Quote (RFQ) is crucial for improving market efficiency for less liquid asset classes. This paper explores the application of explainable AI (XAI) models to forecast the likelihood of RFQ fulfillment. By leveraging advanced algorithms including Logistic Regression, Random Forest, XGBoost and Bayesian Neural Tree, we are able to improve the accuracy of RFQ fill rate predictions and generate the most efficient quote price for market makers. XAI serves as a robust and transparent tool for market participants to navigate the complexities of RFQs with greater precision.