Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
Summary: arXiv:2604.13462v1 Announce Type: cross
Abstract
Effective IT change management is crucial for businesses that rely on software and services, particularly in highly regulated sectors such as finance. In these environments, operational reliability, auditability, and explainability are essential. A significant portion of IT incidents can be traced back to changes made in the system. Therefore, identifying high-risk changes before they are deployed is vital for maintaining service integrity and compliance.
Introduction
This study presents a novel predictive incident risk scoring approach implemented at a large international bank. The methodology is designed to support engineers during the assessment and planning phases of change deployments by forecasting the potential for incidents to occur as a result of those changes. The model is constructed with regulatory constraints in mind, emphasizing auditability and explainability to ensure that decisions made are both traceable and transparent.
Methodology
To validate our approach, we utilized a one-year real-world dataset and conducted a comparative analysis between the existing rule-based process and three advanced machine learning models: HGBC (Histogram-based Gradient Boosting Classifier), LightGBM (Light Gradient Boosting Machine), and XGBoost (Extreme Gradient Boosting).
Key Findings
Our findings reveal that data-driven and interpretable models have the potential to outperform traditional rule-based methodologies while also adhering to compliance requirements. The study highlights the following points:
- The integration of aggregated team metrics enhances the model’s predictive power by capturing the organizational context.
- LightGBM demonstrated the best performance among the models tested, achieving superior accuracy in predicting incident risks.
- The application of SHAP (SHapley Additive exPlanations) values provided feature-level insights into the model’s predictions, allowing for a better understanding of the factors contributing to risk.
Conclusion
The implementation of predictive models in IT change management can significantly improve the reliability of IT operations in regulated environments. By leveraging machine learning techniques, organizations can proactively mitigate risks associated with changes, enhancing operational resilience while meeting stringent compliance requirements. The findings of this study advocate for a shift towards data-driven decision-making processes in IT change management, ensuring that businesses can navigate the complexities of regulatory landscapes effectively.
Implications for Future Research
Future research efforts should focus on expanding the dataset to include a broader range of incidents across different sectors. Additionally, exploring the integration of other machine learning techniques and their impact on incident prediction could yield further insights into optimizing IT change management practices.