Healthcare Claims Classification
95% accuracy · 42,900+ claims
Production classification system (CatBoost, XGBoost, AutoGluon) at 95% accuracy across 42,900+ claims with daily feedback-loop retraining, paired with a denial-prediction and First-Time Pass Rate module that cut the denial rate from 3.2% to 2.4%.
Problem
Healthcare claims arrive in volume and denials are slow and costly. Claims had to be sorted into categories automatically, and denials predicted before resubmission.
Approach
- CatBoost, XGBoost and AutoGluon models for parent and category classification
- Daily feedback-loop retraining from staff corrections
- Rule-based First-Time Pass Rate module comparing rejected against corrected or paid claims by provider, payer, CPT and ICD code
- Validation agent that layers rule-based checks with generated root-cause analysis and corrective actions for each flagged case
Results
- 95% parent-model accuracy and 92%+ category-level accuracy across 42,900+ cases
- Claim denial rate cut from 3.2% to 2.4%
- Resubmission wait cut from 2 weeks to 3 days
Stack
- Python
- CatBoost
- XGBoost
- AutoGluon
- FastAPI
By Muzammil Nawaz Khan, AI Engineer.