Healthcare insurers rely on payment integrity controls to prevent fraud and avoid unnecessary payments. However, these efforts tend to be highly inefficient – primarily focused on post-payment identification with systems segregated across multiple departments. These unintegrated systems prevent any holistic strategy to help Payers proactively detect fraud and enable higher levels of savings.
PayAssure uses advanced ML algorithms to help Payers verify the validity of claims, detect fraudulent ones, and flag errors, discrepancies, and duplication. This helps reduce manual processing errors and significantly improves Payer efficiency with timely audit of payment and advanced claims monitoring. Employing the industry’s most comprehensive set off analytics, tools and services, PayAssure enables Payers to move away from a “pay and chase” approach to a more proactive one focused on detecting and eliminating fraud and abuse at every stage of the process.
PayAssure addresses Payers’ fraud surveillance and detection demands with a holistic analytics-led strategy that limits false positives and prioritizes the highest risk claims. As a result, payers see significant ROI:
PayAssure leverage powerful AI/ML algorithms to improve fraud surveillance and detection, spot patterns and abnormalities, and enhance payment decisions. Using fraud propensity scoring techniques, PayAssure provides verification, enrollment, investigation, pre and post-pay analytics, auditing, and recovery, significantly improving savings and efficacy of the Payer claims process.
Key capabilities include:
ML-based anomaly detection enables faster exploration and decision-making on overpayments, fraud or manual errors.
PayAssure flags probable cases for immediate review and bypasses non-probable cases.
Proactively identify potential fraud before payment using emerging Fraud, Waste and Abuse (FWA) methodologies, fraud networks, and prioritized targets
Find fraud at different levels of care using procedural codes and flag duplicate and overutilization claims.
AI/ML-based insights help in proactive claim scoring and review processes that lower the risk of overpayment and minimize revenue loss due to fraud.
PayAssure’s Quantitative Scoring Claims algorithms assists Payers in identifying outliers linked to quantity overutilization and detects duplicate claims by providers.
PayAssure identifies outliers connected to frequent billing of unnecessary medical treatment, less likely procedure codes, and inappropriate coding methods. It also eliminates financial leakage for Payers at the physician level.
PayAssure’s Predictive Risk Scoring model allows Payers to audit only 10% of the most erroneous cases, saving time and effort over the manual approach while still collecting an equal number of errors.