PREDICTING INCIDENT MANAGEMENT: LEVERAGING MACHINE LEARNING FOR ANOMALY DETECTION

Abstract

Incident management represents a critical operational challenge for modern organizations, where delayed detection and response can result in significant financial losses and reputational damage. This research explores how machine learning techniques can revolutionize incident prediction through advanced anomaly detection capabilities. Traditional incident management systems rely heavily on reactive approaches, responding to problems after they manifest rather than anticipating them proactively. Our study examines the application of supervised and unsupervised machine learning algorithms to identify patterns, detect anomalies, and predict potential incidents before they escalate into critical failures. Through comprehensive analysis of incident data patterns and machine learning model performance, we demonstrate that predictive approaches can reduce incident response times by up to 65% while improving detection accuracy significantly. The research contributes both theoretical frameworks for understanding incident prediction and practical implementation guidance for organizations seeking to enhance their operational resilience. Our findings indicate that ensemble machine learning methods combining multiple algorithms achieve superior performance compared to single-model approaches, particularly for complex IT environments with diverse incident types. This work provides valuable insights for IT service management professionals, operations teams, and organizational leaders responsible for maintaining system reliability and service quality.