Predictive Analytics in Financial Risk Management: A Data-Driven Approach

Abstract

The financial industry has long relied on statistical models for risk assessment, but predictive analytics and intelligent data systems have significantly enhanced the accuracy and efficiency of these processes. This paper explores how predictive modeling, machine learning, and real-time analytics are applied to credit scoring, fraud detection, investment forecasting, and operational risk management. It discusses the importance of integrating structured financial data with unstructured data sources such as social media sentiment and news feeds to create more comprehensive risk profiles. The paper highlights both the opportunities and the limitations of predictive analytics in financial decision-making. Regulatory compliance, data governance, and model transparency are emphasized as essential factors for sustainable adoption. The study also provides insight into how predictive analytics can support financial resilience during economic uncertainty.

KEYWORDS: Financial Analytics, Predictive Modeling, Risk Management, Intelligent Data Systems, Fraud Detection