Objective
The U.S. Justice Department estimates that 3% of healthcare claims in the United States, worth almost a hundred billion dollars, are fraudulent. Investigating claims is time consuming and expensive with payers pursuing fraudulent cases for months or years after payments have been made. Fraudulent claims contribute to the increased cost of care, slow down valid claims, and lead to higher healthcare premiums for patients
Outcome
Machine learning models can be used to automate claims assessment and routing based on existing fraud patterns. This process flags potentially fraudulent claims for further review, but also has the added benefit of automatically identifying good transactions and streamlining their approval and payment. More advanced anomaly detection systems can be deployed to find new patterns and to flag those for review, which leads to prompt investigation of new fraud types. AI systems can also provide clear reason codes for investigators, so they can quickly see the key factors that led the AI to indicate fraud which streamlines their investigation. With AI based fraud detection, fraudulent claims can be evaluated and flagged before they are paid, which reduces costs for payers, helps keep costs lower for patients and helps catch fraudsters in the act.
Business Value
Provide clear reason codes for investigators, so they can quickly see the key factors that led the AI to indicate fraud which streamlines their investigation. With AI based fraud detection, fraudulent claims can be evaluated and flagged before they are paid, which reduces costs for payers, helps keep costs lower for patients and helps catch fraudsters in the act.
H2O's AI and Data Approaches
This solution is powered by the H2O AI Cloud Driverless AI AutoML, H2O-3, and H2O.ai Wave. The data science approaches include genetic algorithm, advanced feature engineering, NLP, GLM, GBM, XGBoost, ensemble stacking, classification algorithms, and various machine learning interpretability algorithms