Developing an Integrated Framework for Intelligent Financial Risk Management Using Predictive Analytics, Machine Learning, and Real-Time Data
Keywords:
Financial intelligence, financial risk management, predictive analytics, machine learning, real-time data, algorithmic governance.Abstract
This study aimed to develop an integrated framework for intelligent financial risk management using predictive analytics, machine learning, and real-time data in financial and economic firms located in Tehran. This qualitative study was conducted using thematic analysis. The participants consisted of 22 financial managers, risk managers, data analysts, fintech specialists, internal auditors, and experts in intelligent financial systems in Tehran. They were selected through purposive and snowball sampling. Data were collected through semi-structured interviews and continued until theoretical saturation was achieved. The data were analyzed through open, axial, and selective coding using NVivo software. Trustworthiness was ensured through member checking, constant comparison, peer review, and maintaining an audit trail of the analytical process. Data analysis led to the extraction of five main categories: real-time data-driven risk infrastructure, predictive modeling and machine learning, algorithmic governance and model explainability, organizational integration and risk-oriented decision-making, and agile response to emerging risks. The findings indicated that intelligent financial risk management is not limited to the deployment of advanced algorithms. Rather, it requires the simultaneous integration of data quality, technological architecture, model interpretability, data-driven decision-making culture, and human oversight. The proposed framework suggests that intelligent financial risk management becomes effective when predictive analytics, machine learning, and real-time data are embedded within a governable, explainable, and organization-oriented system. Such a framework can facilitate early risk detection, reduce human error, improve response speed, and enhance the financial resilience of firms under volatile and uncertain conditions.
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