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Claim denials represent a major source of lost or delayed healthcare revenue. They are commonly driven by documentation gaps, coding inconsistencies, payer rule violations, and missing or invalid prior authorizations. Current denial prevention often depends on manual review and deterministic claim-scrubbing rules. These approaches may not capture complex payer-specific interactions among documentation quality, procedure codes, diagnosis codes, and authorization history. This article proposes an explainable gradient boosting model for estimating the probability that a health insurance claim could be denied before submission. The model is intended to identify the specific claim-level factors contributing to denial risk. The proposed framework uses a gradient-boosted tree ensemble trained conceptually on historical claims and remittance data. SHAP-based explanations would provide both global and local interpretability for denial-risk predictions. Conceptually, the model would return a denial risk score alongside an explanation of contributing factors such as incomplete documentation, diagnosis-code mismatch, expired authorization, or payer rule conflict. These outputs would support targeted pre-billing review rather than broad manual auditing. An explainable denial prediction model could help shift revenue cycle management from reactive appeals toward proactive prevention. Transparent reasoning would be essential for revenue cycle staff, clinical documentation teams, coders, and compliance stakeholders.

Prior authorization delays impede timely patient care and contribute to administrative pressure across clinical and revenue cycle workflows. These delays can affect scheduling, medication access, procedural planning, and patient confidence in the care process. Current authorization management tools are largely reactive and often focus on tracking request status after submission. They rarely predict which requests are likely to experience approval delays or explain the operational, clinical, or payer-specific reasons behind those delays. This article proposes an interpretable machine learning model for predicting the likelihood of prior authorization approval delays. The model is designed to provide transparent, request-level explanations that can guide pre-submission correction and authorization preparation. The proposed framework uses a gradient-boosted tree model trained conceptually on historical authorization requests. Inputs include payer-specific rules, clinical documentation features, procedure type, medical necessity indicators, and historical approval timelines, with SHAP used to attribute predicted delay risk to individual features. Conceptually, the model would output a delay probability and an explanation of the dominant drivers of that prediction. These drivers could include incomplete documentation, mismatch with payer medical necessity criteria, procedure categories associated with additional review, or payer-procedure combinations with historically slow turnaround. An interpretable prior authorization delay model could support earlier correction of incomplete requests, reduce administrative waste, and improve patient access. By aligning predictive analytics with transparent explanations, the framework could make authorization preparation more proactive and accountable.