VForce · gold.fwa_scorecard · tenant: bop
Findings live on the VForce platform. Explore the design on vforce360.ai, or ask them in plain language with Askura.
Edgent analyzes historical claims to surface duplicates, upcoding, over-use, and above-benchmark charges, with every finding traced to a rule and to the source line. Explainable first, machine learning where it earns its keep, and a human reviewer on every conclusion.
*Internal validation against a synthetic 82,000-line claims dataset with planted fraud and known ground truth. Results on client data depend on data quality and the agreed methodology.
Findings live on the VForce platform. Explore the design on vforce360.ai, or ask them in plain language with Askura.
One governed pass over the claims file produces claim-level and provider-level findings, ranked and traceable, ready for review and recovery.
Exact resubmissions and the same clinical event billed across different claim IDs.
High-level codes inconsistent with the diagnosis, and procedure-to-diagnosis mismatches.
Panel components billed separately alongside the panel that already includes them.
Statistically anomalous utilization of specific procedures against provider peers.
Billed amounts compared to locality-adjusted Medicare rates, reported as a percent of Medicare.
Implausible same-day volumes of high-value procedures by a single provider.
Service after discharge, negative spans, and overlapping episodes of care.
Unsupervised models surface suspicious patterns the rules were never told to look for.
Rules carry the high-confidence findings, machine learning finds the unknowns, and the reviewer makes the system smarter with every decision.
Known fraud patterns detected by transparent, reproducible logic. Every flag names the claim, the rule, and the dollars, so it holds up to scrutiny.
Anomaly detection over provider and procedure profiles ranks the borderline cases and surfaces novel schemes, with no labeled data required.
Every confirm or reject becomes a label that calibrates thresholds now and trains supervised models over time, cutting false positives with use.
The people behind this solution have built integrity systems where the stakes and the volumes are real.
Our engineers built trust, safety, and integrity systems for a global online marketplace and its health offering, processing high-volume event streams to detect abuse in a closed-loop, event-driven architecture.
Provider master data management and claims-data modernization for a major commercial health payer in a HIPAA-regulated environment.
Senior engineering on claims-organization technology for a national insurance carrier, including claims data structures and leakage signals at high volume.
The analysis runs in an isolated environment with no external connectivity. Your data is never sent to a third party or used to train anyone's model.
Flow for ingestion, a governed lakehouse for the medallion data model and Medicare benchmarking, Askura for conversational access to the findings, and the machine-learning and feedback layers, all on the VForce platform.