Advancements in Data Mining Algorithms for Payment Integrity

This technical report discusses the evolution and advancements of data mining algorithms in the context of payment integrity within the healthcare sector. It outlines how these algorithms analyze extensive healthcare data to identify patterns and anomalies that may indicate fraud, waste, or abuse. The report details the shift from traditional rule-based systems to more sophisticated techniques, including prepay and post pay data mining strategies. It highlights five key areas of improvement in algorithms: pattern recognition, anomaly detection, unstructured data processing, predictive fraud scoring, and reinforcement learning. Additionally, the report anticipates future developments in data mining algorithms, emphasizing the role of AI and advanced technologies in enhancing efficiency and accuracy in detecting fraudulent claims. The integration of feedback loops between prepay and post pay systems is also discussed, indicating a trend towards more cohesive and effective payment integrity management.