A machine learning framework for predicting healthcare insurance claim denials

Abstract

Introduction The denial of healthcare insurance claims imposes a significant burden on providers, leading to decreased revenue and inefficient operations. This research presents a predictive model to assist providers in managing claim submissions. Methods The framework uses a rule-based claim scrubber that authenticates submissions before sending them, matching them against known denial patterns. It also employs a two-stage supervised pipeline. The study analyses 678,881 professional claim service lines submitted to a single U.S. payer between 2017 and 2024, of which 31.9% were denied. Stage 1 classifies a submitted line as accepted or denied using a Random Forest over 26 features Results On a temporally ordered, claim-grouped held-out partition of 139,615 lines that retains the natural denial prevalence, it attains a precisionrecall AUC of 0.777 (95% CI 0.770-0.784) against a no-skill baseline of 0.330, ROC-AUC 0.865, accuracy 0.794, balanced accuracy 0.784, recall 0.756 and Matthews correlation 0.552. Stage 2 assigns a reason code over a 67-class label space covering 98.8% of denials, reaching 0.269 Top-1 and 0.498 Top-3 accuracy; the same model reaches 0.918 Top-3 when candidates are restricted to the five most frequent codes, and we report both because reason-code accuracy depends heavily on the label space. Because Stage 2 scores only lines Stage 1 has flagged, end-to-end performance is the product of the two stages: the correct code appears among the first three candidates for 38.8% of all denied lines. Class imbalance was addressed with the Synthetic Minority Over-Sampling Technique applied within training folds only; an ablation shows no measurable benefit at this prevalence (difference in average precision −0.0002, 95% CI −0.0009 to +0.0006) and worse calibration, which we report rather than omit. Tuned XGBoost and LightGBM baselines on the identical partition are statistically indistinguishable from the Random Forest. Discussion The solution provides providers with a claim validation and denial-forecasting tool that reduces the denial rate and simplifies the revenue cycle.

The authors' abstract, as published at the source. Frontiers in Health Services, 2026 · DOI ↗