  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.
