Interpretable Predictive Analytics Approach for Robust Financial Risk Assessment and Forecasting
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 humanâAI 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
References
Most read articles by the same author(s)
- Dr. Priya Sharma, A Deep Learning-Based Personalized Recommendation Architecture for E-Commerce Using CNN-Driven Sequential Representation Learning and Temporal User Behavior Optimization , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 05 (2026): Volume 03 Issue 05
- Dr. James William Carter, Dr. Emily Rose Thompson, A Hybrid QuantumâClassical Deep Learning Approach for Image Recognition: Performance Analysis of Quanvolution-Based Convolutional Models , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 06 (2026): Volume 03 Issue 06
- Bima Satria Nugraha, Professor Anindya larasati, Dr. Huỳnh Chà DƩng, Assessing The Interoperability And Semantic Readiness Of BIM And IFC Data For AI Integration In The Architecture, Engineering, And Construction Industry: A Systematic Review , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 11 (2025): Volume 02 Issue 11
- Dr. Larian D. Venorth, Prof. Maevis K. Durand, The Transformative Trajectory Of Large Language Models: Societal Impact, Predictive Limitations, And The Unforeseen Geohazard Nexus , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Mr. Ram Pratap Singh, An Intelligent Machine Learning Framework for Customer Churn Prediction in CRM Systems , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 07 (2026): Volume 03 Issue 07
- Ananya Patel (Ph.D. Candidate), ADVANCING FINANCIAL PREDICTION THROUGH QUANTUM MACHINE LEARNING , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 02 (2025): Volume 02 Issue 02
- Dr. Sara Mohammadi, A Scalable Python-Based Architecture for Causal Structure Learning in Non-Gaussian Linear Systems Using the PyCD-LiNGAM Framework , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 06 (2026): Volume 03 Issue 06
- Mr. Madhav Sharma, Prediction of Heart Disease Using Ensemble Machine Learning Techniques , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Chinedu Emmanuel Okafor, Intelligent Healthcare Systems Powered by Large Language Models: Applications, Limitations, and Emerging Research Perspectives , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Eko Purnomo, Rendra Alfiansyah, A Dynamic Nexus: Integrating Big Data Analytics and Distributed Computing for Real-Time Risk Management of Derivatives Portfolios , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 10 (2025): Volume 02 Issue 10
Similar Articles
- Dr. Emil Novak, Deep Learning For EâCommerce Recommendations: Capturing Long- And Short-Term User Preferences With Cnn-Based Representation Learning , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 12 (2025): Volume 02 Issue 12
- Dr. Lucas Vermeulen, Sophie De Smet, Dr. Thomas Dubois, Integrated Temporal Analytics and AI-Based Approaches for Predicting Culinary Ingredient Consumption Patterns: Evidence from Thai Markets , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Javier M. Ortega, Dr. Lucia FernĂĄndez-RĂos, Predictive Modeling of Online Retail Revenue Using Data Exploration and Intelligent Algorithms , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Minh Quang Tran, Dr. Lan Anh Nguyen, An Advanced Analytical Architecture for Leveraging Big Data in Artificial Intelligence Systems: Techniques, Optimization Strategies, and Case-Based Evaluation , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Ananya Patel (Ph.D. Candidate), ADVANCING FINANCIAL PREDICTION THROUGH QUANTUM MACHINE LEARNING , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 02 (2025): Volume 02 Issue 02
- Igor Litovsky, A Systematic Review of Machine Learning Approaches For AI-Driven Fraud Detection in Loyalty Programs , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Chinedu Emmanuel Okafor, Intelligent Healthcare Systems Powered by Large Language Models: Applications, Limitations, and Emerging Research Perspectives , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Dr Adrian Morrow, Dynamic AI Based Credit Scoring and Alternative Data Driven Risk Governance in Digital Lending Platforms , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 11 (2025): Volume 02 Issue 11
- Dr. Hana Bekele Tadesse, Intelligent Sequential Analytics Framework for Enhancing Monetary Transfer Scheduling in Logistics-Based Financial Systems , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 02 (2026): Volume 03 Issue 02
- Eko Purnomo, Rendra Alfiansyah, A Dynamic Nexus: Integrating Big Data Analytics and Distributed Computing for Real-Time Risk Management of Derivatives Portfolios , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 10 (2025): Volume 02 Issue 10
You may also start an advanced similarity search for this article.