Navigating Financial Instability: An Interpretable Paradigm for Multi-Label Crisis Prediction

We present a rigorous methodology for applying Elastic Weight Consolidation (EWC) to the continual prediction of banking, systemic, currency and inflation crises. Traditional models, trained on earlier regimes, are prone to catastrophic forgetting when updated on new regimes for financial crisis prediction that is a sequential, non-stationary learning problem with data arriving across time and heterogeneous jurisdictions. EWC mitigates this by constraining important parameters, quantified via a diagonal-Fisher-information approximation, to remain near previously learnt optima while permitting flexibility in less critical directions. We formalise the Bayesian derivation underpinning EWC, present the practical diagonalFisher estimator, and integrate the method into a reproducible training pipeline for multi-label crisis prediction. Our protocol uses representative task partitions, rigorous Fisher estimation, lambda scheduling, and a suite of evaluation metrics to compare performance with baseline traditional models. We provide ablation studies for Fisher estimation, penalty aggregation, and sequential walk-forward evaluation, and discuss limitations and interpretability for policy use. Our experimental model is tested for 2 years prior financial crisis prediction on Harvard Business School's global crisis database for 42 years and 70 countries, and it demonstrates ROC-AUC of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 3. 1 2 \%}$</tex> in inflation crises, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 3. 5 3 \%}$</tex> in systemic crises, 77.63% in currency crises and 67.51% in banking crises predictions, outperforming the traditional models' results. We also assess the sensitivity of EWC to its core hyperparameters for real-world application. By integrating SHAP, this work aims to bridge probabilistic continual-learning theory and operational early-warning systems, making models both interpretable and robust in dynamic financial environments.

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