Enterprise Risk Intelligence: Machine Learning Models for Predicting Compliance, Fraud, and Operational Failures

Abstract

Companies are growing more vulnerable to interdependent and intricate risks of regulatory non-compliance, financial fraud, and operation breakdowns. Traditional rule-based monitoring and periodic audits struggle to cope with the scale, velocity, and heterogeneity of modern enterprise data, leading to delayed detection and residual risk. This paper suggests a unified Enterprise Risk Intelligence framework which uses machine learning (ML) models to forecast compliance violations, fraud cases, and business outages with one data and feature engineering pipeline. Heterogeneous data sources such as compliance logs, financial transactions and events of the operational system are merged in an enterprise records layer and converted to risk indicators of event frequency, severity, and cross-domain dependencies. Above this base, ensemble models like Gradient Boosting and Random Forests, and then augmented with time-series architectures, like BiLSTM-FCN, provide high predictive accuracy on both types of risk, and their performance is assessed by AUC, precision, recall, F1 score and AUPRC. The transparency of a specific model and the need to extract rules and liabilities makes the implementation of a focused model interpretability layer through SHAP, LIME, and rule extraction an essential requirement of extremely regulated settings. The framework is deployed in Enterprise Risk Management (ERM) systems in a scalable, secure manner through APIs, alerting systems, and dashboards, taking into consideration the data governance, privacy, and regulatory limitations. The data quality, imbalance, and concept drift identified in error analysis and segment-level diagnostics are the main challenges that encourage the continuation of future research in continual learning, graph-based risk modeling, and fairness-aware risk analytics.