Explainable AI Model for Recognizing Financial Crisis Roots Based on Pigeon Optimization and Gradient Boosting Model

Abstract Utilizing Artificial Intelligence (AI) techniques to forecast, recognize, and classify financial crisis roots are important research challenges that have attracted the interest of researchers. Moreover, Explainable (XAI) concept enables AI interpret results processing testing complex data patterns so humans can find efficient ways infer logic behind classifying patterns. This paper proposes a novel XAI model automatically recognize interprets features selection operation. Using benchmark dataset, proposed utilized pigeon optimizer optimize feature operation, then Gradient Boosting classifier is based on obtained reduct most features. The practical showed short-term rates by which be detected. classification built-in in Pigeon Inspired Optimizer (PIO) algorithm achieved training accuracy 99% 96.7%, respectively, recognizing roots, an better performance compared random forest classifier.