A Hybrid Machine Learning Model for Detecting and Preventing Corruption in Kenya’s Public Procurement Contracts

Corruption in public procurement undermines fiscal sustainability, distorts competition, and reduces service quality. Conventional anti-corruption controls-manual audits, rule-based checks, and ex-post reviews-struggle to flag sophisticated, evolving fraud patterns in real time. This study proposes and empirically evaluates a hybrid machine-learning (ML) framework that integrates interpretable supervised models (logistic regression) with high-accuracy ensemble methods (random forest) and unsupervised learning (k-means clustering and anomaly detection) to identify corruption-prone contracts within Kenya’s public procurement ecosystem. Using secondary procurement data-contract values, procurement methods, bidder histories, award timelines-and text-derived indicators from public audit narratives, we construct features representing red flags such as single-bid tenders, repeated awards, and significant deviations from estimated costs. Logistic regression provides transparent coefficient-level evidence, while random forest captures non-linear interactions; clustering approximates high-risk groupings where labels are incomplete. Results indicate that single-bid tenders, prior supplier all