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International Journal of Intelligent Data and Machine Learning

Open Access Peer Review International
Open Access

Interpretable Predictive Analytics Approach for Robust Financial Risk Assessment and Forecasting

DOI https://doi.org/10.55640/ijidml-v03i09-02
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Muhammad Rizky Pratama 1 , Siti Aulia Rahma 1 ,
4 Department of Computer Science, Nusantara Institute of Technology, Jakarta, Indonesia
4 Department of Data Science, Indonesian School of Computing, Bandung, Indonesia

Abstract

Financial risk forecasting increasingly relies on machine learning and deep learning models capable of processing high-dimensional, nonlinear, and temporally dependent financial information. However, predictive accuracy alone is insufficient for financial decision-making because risk analysts, managers, regulators, and other stakeholders require understandable evidence explaining why a model produces a particular risk estimate. This paper develops an interpretable predictive analytics approach for robust financial risk assessment and forecasting, emphasizing the integration of predictive performance, explainability, temporal modeling, and decision-oriented interpretation. The study synthesizes the provided literature on explainable artificial intelligence (XAI), financial time-series forecasting, automated machine learning, and deep-learning interpretability. A conceptual framework is proposed in which financial data preprocessing, risk-feature construction, predictive modeling, explanation generation, robustness assessment, and decision interpretation operate as interconnected stages. The analysis indicates that explainability should not be treated as an independent visualization layer but as an integral component of the predictive pipeline. Global explanations can reveal systematic risk drivers, while local explanations can clarify individual forecasts and anomalous predictions. The proposed approach further emphasizes stability of explanations, temporal consistency, model transparency, and human–AI collaboration. The resulting framework provides a research-oriented foundation for developing financial forecasting systems that balance predictive capability with interpretability, accountability, and practical usability.

Keywords

Explainable Artificial Intelligence, Financial Risk Forecasting, Predictive Analytics

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