A unified machine learning framework for enterprise portfolio forecasting

This paper proposes and evaluates a unified machine-learning framework for enterprise portfolio management that integrates multi-horizon financial forecasting, unsupervised risk detection, and explainable reporting within a single pipeline. Using a synthetic but structurally realistic ERP-style dataset comprising 162,000 project–month records with 24 financial and operational features, the study adopts a quantitative design based on multi-source feature engineering, expanding-window temporal cross-validation, and benchmarking of five forecasting models (Linear Regression, Random Forest, XGBoost, LightGBM, CatBoost) across 1-, 3-, and 6-month horizons. Hyperparameters for the strongest models are tuned with Optuna, and three unsupervised detectors (Isolation Forest, COPOD, LODA) are applied to scaled numeric features, while SHAP is used to generate global and local explanations. Results show that gradient-boosted trees substantially outperform linear baselines, reducing MAE by roughly 25–40% and achieving R² ≈ 0.63 at 1 month, ≈ 0.57 at 3 months, and ≈ 0.43 at 6 months, with open commitments, backlog, change orders, and schedule slippage emerging as dominant drivers of future spend. The anomaly layer flags around 2% of records as high risk, capturing patterns such as vendor rate spikes, zero-commitment overspend, stalled backlog, and abrupt forecast collapses. Rather than introducing novel algorithms, this work contributes a unified, SHAP-enabled architecture that enhances auditability and governance by transforming model outputs into defensible financial narratives and providing a practical blueprint for future work to extend to real ERP data, streaming architectures, and human-in-the-loop risk governance.