Risk Identification and Service Upgrade in Logistics Claims

Abstract

With the rapid expansion of the logistics industry, the contradiction between improving customer claim experience and controlling enterprise costs has become increasingly prominent. Traditional manual-dominated claim risk identification is inefficient and relies on experience, making it difficult to meet the refined management needs of large-scale waybills. To address this issue, this paper constructs a data-driven standardized modeling system covering three core tasks: risk labeling, compensation amount prediction, and dual-path risk labeling. For risk labeling, a seven-step process is adopted: basic indicator construction, deep feature engineering, compensation grade division, threshold optimization, dynamic adaptation, labeling fine-tuning, and verification. Gaussian Mixture Model (GMM) is used to cluster two-dimensional data of "actual compensation amount - claim difference", and constrained nonlinear programming is applied to optimize thresholds, ensuring reasonable claims account for 84.99% and severe excess claims for 2.97%, which meets business constraints. For compensation amount prediction, a six-layer architecture is built, including feature enhancement, multi-model integration, control engineering enhancement, and business constraints. Exclusive features such as compensation time-series correlation are added, and a weighted voting integrated model with Elastic Net, Random Forest, and Gradient Boosting Tree is constructed. Adaptive PID and multi-state Kalman Filter are introduced to improve stability, with the model achieving RMSE of 112.3 and R² of 0.841 on the verification set, and prediction fluctuation reduced by over 40%. For dual-path risk labeling, two schemes are designed. Path 1 reuses and adapts the risk labeling rules, while Path 2 builds an end-to-end classification model. A triple strategy (SMOTE-ENN hybrid sampling, class weight compensation, stratified cross-validation) is used to solve the extreme class imbalance of severe excess samples. Both paths meet business constraints, with a prediction consistency of 81.02%, suitable for different scenarios. This paper innovatively integrates machine learning and control engineering, designs a dual-path scheme and a triple strategy for class balance, providing a standardized reference for logistics claim risk management.