An Explainable Artificial Intelligence Framework for Early Warning of Systemic Financial Risk

Abstract

The accumulation of macro-financial imbalances leads to systemic financial crises which create major risks for economic stability. Theoretical foundations of traditional early-warning systems fail to predict the complex relationships and sudden changes which occur in contemporary financial systems. Machine learning developments enable better prediction results, but their users face comprehension difficulties which prevent them from being used in regulatory settings and building public confidence. The research develops an early-warning system for financial system risk assessment through the combination of ensemble machine learning methods with XAI. The system uses macroeconomic data together with financial market information and banking sector performance to create future risk predictions and its design includes methods which allow users to understand results throughout the entire modeling process. The research uses explainability tools which include feature-attribution methods and model-agnostic interpretation techniques to showcase both important risk factors and specific times when risk levels increased. The proposed system aims to achieve both predictive accuracy and transparent performance. The system includes all necessary processes for data creation and model development together with explainability assessment methods which enable organizations to monitor financial system stability. The research creates a system for predicting risks which allows organizations to track systemic financial dangers.