Predictive Data Analytics for Early Detection of Hidden Business Risks Using Machine Learning

Abstract

This research paper presents a machine learning-based framework for the early detection of hidden business risks using operational and financial data. Businesses generate large volumes of data related to sales, inventory, customer activity, payments, expenses, and operational systems. Analyzing these data can help identify unusual patterns and potential risks before they become serious problems. The paper proposes a data-driven approach that combines data preparation, feature engineering, machine learning-based risk classification, anomaly detection, and model explainability. It discusses methods such as Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Isolation Forest for identifying potential business risks. The proposed framework aims to support early warning systems, risk prioritization, and interpretable decision-making. It also outlines an evaluation methodology using metrics such as precision, recall, F1-score, PR-AUC, false alerts, and lead time. This paper describes a proposed framework and evaluation plan. Actual performance results should be reported only after implementation and testing on a suitable dataset.