A Hybrid Machine Learning Approach for Continuous Risk Management in Business Process Reengineering Projects

Abstract

This study proposes a hybrid machine learning approach for continuous risk management in Business Process Reengineering (BPR) projects. This approach combines supervised and unsupervised learning techniques, integrating feature selection and preprocessing through Principal Component Analysis (PCA), clustering with K-means, and visualization with t-SNE. The labeled data are then used as input for predictive modeling with XGBoost, optimized using Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Grid Search algorithms.PCA reduces data dimensionality, simplifying analysis and improving model performance. K-means and t-SNE are employed for data clustering and visualization, enabling the identification of risk segments and uncovering hidden patterns. XGBoost, a powerful boosting algorithm, is utilized for predictive modeling due to its efficiency, accuracy, and ability to handle missing values. Optimization techniques further enhance XGBoost's performance by fine-tuning its hyperparameters. The approach was applied to a risk database from the automotive sector, demonstrating its practical applicability. Results show that PSO achieves the lowest mean squared error (MSE) and root mean squared error (RMSE), followed by GWO and Grid Search. Mahalanobis distance yields more accurate clustering results compared to Euclidean, Manhattan, and Cosine distances. This hybrid machine learning approach significantly enhances risk detection, evaluation, and mitigation in BPR projects, offering a robust framework for proactive decision-making.