Machine Learning for Early Detection of Operational Risks in Complex Organizations

Abstract

Operational incidents in complex organisations, such as process failures, service outages and control breakdowns, are rarely instantaneous; they are usually preceded by a build-up of abnormal readings across several monitored metrics. This paper evaluates machine learning for detecting that build-up early enough to intervene. A simulation harness generates daily readings of five process metrics for 60 operational processes over 200 days, with incidents preceded by a gradual drift over three to nine days. Three detection approaches are compared on 20 held-out processes: unsupervised anomaly scoring with an isolation forest, and supervised logistic regression and gradient boosting trained to predict an incident within the next five days from current readings, their day-to-day changes and five-day rolling means. Supervised models detect 82% of incidents within ten days of onset with mean lead times of 4.3 and 5.4 days, respectively, at a false-alert rate of about 8.5% of process-days; the isolation forest detects 62% with a longer mean lead but near-random discrimination at the window level. The paper distinguishes detection rate, lead time and false-alert burden as the three quantities that jointly determine operational value, shows that they trade off differently across methods, and proposes an alerting architecture that combines unsupervised screening with supervised confirmation. Guidance is offered on labelling incidents, choosing the prediction window and governing alert fatigue.