Paper: arXiv 2508.02283

Authors: Francis Boabang, Samuel Asante Gyamerah

Abstract

In insurance fraud prediction, handling class imbalance remains a critical challenge. This paper presents a novel multistage focal loss function designed to enhance the performance of machine learning models in such imbalanced settings by helping to escape local minima and converge to a good solution. Building upon the foundation of the standard focal loss, our proposed approach introduces a dynamic, multi-stage convex and nonconvex mechanism that progressively adjusts the focus on hard-to-classify samples across training epochs. This strategic refinement facilitates more stable learning and improved discrimination between fraudulent and legitimate cases. Through extensive experimentation on a real-world insurance dataset, our method achieved better performance than the traditional focal loss, as measured by accuracy, precision, F1-score, recall and Area Under the Curve (AUC) metrics on the auto insurance dataset. These results demonstrate the efficacy of the multistage focal loss in boosting model robustness and predictive accuracy in highly skewed classification tasks, offering significant implications for fraud detection systems in the insurance industry. An explainable model is included to interpret the results.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper focuses on a modified loss function (multistage focal loss) which involves moderate mathematical formulation but primarily adapts existing concepts, while the empirical section details extensive experimentation on a real-world insurance dataset with standard ML metrics (accuracy, F1, AUC) and mentions preprocessing like SMOTE/ADASYN.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Improve Fraud Detection<br>in Class Imbalanced Data"] --> B{"Data Input"}
  B --> C["Real-World Auto Insurance Dataset<br>Skewed: Legitimate vs Fraud"]
  C --> D["Key Methodology:<br>Multistage Focal Loss Function"]
  D --> E["Computational Process:<br>Training ML Models with<br>Dynamic Stage Adjustments"]
  E --> F["Performance Metrics:<br>F1, Precision, Recall, AUC"]
  F --> G["Key Outcome:<br>Outperforms Standard Focal Loss<br>Explainable AI Included"]

Cited References (38)

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and 30 more
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  2. Instance S H A P : an instance-based estimation approach for S hapley values (2024)
  3. FW-Shapley: Real-Time Estimation of Weighted Shapley Values (2024)
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0 of 38 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.