AI-Powered Detection of Fraud, Waste and Abuse

Combining leading claims digitization and categorization with precise triggers for inflated bills, minimising human involvement and errors in claims adjudication.

How Health Insurance Fraud Detection Works?

• Electronic claims documents across all formats or scanned handwritten documents from your claims management system feed into the Perfios Health Claims Analysis Solution.
• Claims documents are digitised using OCR and Human in Loop for handwritten documents.
• ML algorithm encodes the digitized data using standard medical classifications like ATC, LOINC, ICD and ICD - PCS.
• The encoded charges are classified and categorized into treatments, procedures and medications; the categories are compared against policy stipulations to isolate exclusions.
• ML-based fraud detection analyzes treatment data and flags anomalies like excessive, missed and unnecessary treatments.

Insurer Benefits from Health Insurance Fraud Detection

Payout Eligibility Evaluation

Determines the eligibility and accuracy of reimbursement without the need for manual intervention.

Seamless Data Extraction

Extracts and digitises data from any document regardless of format be it CSV, PDF, text or scanned images.

Predictive Analytics

Builds ML-based knowledge graphs using claims data to enable predictive modelling for insurance pricing, product development and provider management. 

Accurate Anomaly Detection

Automatically identifies anomalies and excess charges in your claims with over 95% accuracy. 

Seamless API Integration

Painless API-based integration with your claims management system, reduces setup time and costs.

Industry-Defining Challenges We Resolve

• 15% of filed health claims are fraudulent or inflated with unnecessary or excessive investigations, treatments and procedures.
• Superfluous treatments and inflated costs are impacting insurers’ ability to remain sustainable in the face of double digit inflation.
• Health insurance policies are becoming more complicated and more expensive for customers, impeding adoption and trust in the product.
• Insurers face an increasing dearth of data regarding patterns in healthcare provider treatment and claims veracity