Accelerating health claims processing with AI-powered analytics
Extracts health claims data from the collected claims documents from the insurer’s claims management system using advanced OCR and human-in-loop.
Utilises industry-standard encodings (ATC, ICD, LOINC, SNOMED) for consistent data encoding.
ML algorithm categorizes encoded data into treatments, procedures and investigations.
ML-powered anomaly detection roots out non-included, anomalous and excessive charges
Outputs generated insights directly into the insurer system in multiple format options.
Claims decisions in as little as a few minutes, slashing processing times from hours to minutes.
Claims eligibility quantification without manual intervention.
Format-agnostic document digitization including CSV, PDF, text, or scanned images.
Airtight quality control on handwritten documents with human and OCR-assisted data extraction.
Predictive modelling for policy pricing, tariff trend tracking and doctor/provider level analytics.
Bill data codification using industry-standard medical classification systems like ICD, PCS, ATC and SNOMED.
95% accurate anomalous and excess charge detection.
Painless API-based integration with in-house systems, reducing setup time and costs.
15% of health claims are fraudulent, or inflated with unnecessary or excessive investigations, treatments, and procedures.
Manual claims data capture is not exhaustive and current technology like OCR is not 100% accurate.
Insurers face an increasing dearth of data regarding patterns in healthcare provider treatment and claims veracity.
Variations in claims terminologies across healthcare providers and geographies slow down claims processing.
Process efficiency improvement
Manual effort reduction
Claims payout reduction
Revitalise Your Insurance Decisioning with Precision and Speed
Revitalise Your Insurance Decisioning with Precision and Speed
Perfios Software is registered as an electronic system operator at the Ministry of Communication and Information of the Republic of Indonesia, Business Identification Number (NIB): 0207240112973