用可解释的机器学习预测金融系统变更风险，提前防患于未然。
Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
- 基于LightGBM构建可审计的风险评分模型，融合团队协作数据提升预测力。
- 在真实一年数据上，模型准确率优于传统规则流程，关键特征可解释。
- 适合需要合规与透明性的金融、医疗等强监管行业的IT运维团队使用。
在金融等强监管领域，IT变更管理对运营可靠性至关重要。研究提出一种预测性事件风险评分方法，用于大型国际银行的变更部署前评估。为满足合规要求，模型设计注重可审计性与可解释性，采用SHAP值提供特征级洞察。基于一年真实数据，对比了规则流程与三种机器学习模型（HGBC、LightGBM、XGBoost）的表现。结果表明，引入团队聚合指标后，LightGBM表现最优，能有效识别高风险变更。数据驱动且可解释的模型不仅超越传统规则方法，还支持主动风险管控，提升IT系统可靠性。
原文摘要 · Abstract (English)
Effective IT change management is important for businesses that depend on software and services, particularly in highly regulated sectors such as finance, where operational reliability, auditability, and explainability are essential. A significant portion of IT incidents are caused by changes, making it important to identify high-risk changes before deployment. This study presents a predictive incident risk scoring approach at a large international bank. The approach supports engineers during the assessment and planning phases of change deployments by predicting the potential of inducing incidents. To satisfy regulatory constraints, we built the model with auditability and explainability in mind, applying SHAP values to provide feature-level insights and ensure decisions are traceable and transparent. Using a one-year real-world dataset, we compare the existing rule-based process with three machine learning models: HGBC, LightGBM, and XGBoost. LightGBM achieved the best performance, particularly when enriched with aggregated team metrics that capture organisational context. Our results show that data-driven, interpretable models can outperform rule-based approaches while meeting compliance needs, enabling proactive risk mitigation and more reliable IT operations.
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