Interpretable Machine Learning Models for Financial Risk Assessment and Forecasting

Antonella Ezequiel

Interpretable Machine Learning Models for Financial Risk Assessment and Forecasting

Authors

Antonella Ezequiel

Financial Data Scientist, Argentina.

Keywords:

Financial Risk Assessment, Forecasting, Explainable AI, Model Transparency, Financial Technology

Synopsis

The increasing complexity of financial markets necessitates predictive systems that are not only accurate but also interpretable. This paper explores interpretable machine learning (IML) models tailored for financial risk assessment and forecasting, emphasizing the importance of transparency in high-stakes domains like finance. We investigate the trade-offs between accuracy and interpretability and assess how emerging IML techniques address regulatory and operational demands. The paper also benchmarks a set of models on real-world financial data, demonstrating that interpretability need not come at the cost of performance. We propose an integrated framework for deploying interpretable models in financial institutions to enhance decision-making and compliance.

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