The losses already show up in the data. The model just hasn't looked.
Five ML models trained on 25 years of health plan operational data score denials, LPI, and FWA before they land, surfacing $40M in late-payment interest risk for a top-5 national health plan.
12 of Top 15
US health plans
3 of Top 5
Blue (BCBS) plans
100M
lives touched across the value chain
Avasant RadarView™
Leader in Healthcare Payer Business Process Transformation
Everest Group PEAK Matrix®
Leader in Healthcare Payer Intelligent Operations
NelsonHall NEAT
Leader in Healthcare Payer Agility & Innovation
Everest Group
Front-runner in the Generative AI Healthcare Payer Market
NelsonHall
Leader in Healthcare Payer Operational Transformation
ISG Provider Lens™
Leader in Payer Transformation on Healthcare Digital Services
Everest Group PEAK Matrix®
Leader in Healthcare Payer Business Process as a Service Solutions (BPaaS)
WHY THIS MATTERS
Bad health plan data carries a real cost
Data quality stopped being an IT problem when it started showing up in the claims P&L.
Administrative complexity
24 cents
Healthcare administrative costs consume roughly 24 cents of every premium dollar; data fragmentation is a primary driver.
JAMA Network Open, 2024
Denial rate
19%
ACA marketplace insurers on HealthCare.gov denied 19% of in-network claims in 2024, with administrative reasons accounting for 25% of denials.
KFF analysis of CMS Transparency in Coverage data, 2025
Uncaptured savings
$21B
A $21B savings opportunity remains through full automation of manual administrative transactions across claims, eligibility, and prior auth.
2025 CAQH Index, February 2026
PROVEN OUTCOMES
What 25 years of payer-trained models actually surface
Live outcomes from health plan data programs across multiple US health plans.
$25M
savings identified
Process mining surfaced $25M in savings opportunities for a top-5 national health plan, across claims, prior auth, and payment workflows.
$40M
in LPI losses prevented
After ML models surfaced the risk, AI propensity models flagged pended claims before regulatory deadlines across four states, converting identified risk into prevented loss.
100%
data lineage
Every data point is tagged with source, timestamp, and transformation at ingest, audit-ready from day one across all plan source systems.
$40M
LPI risk identified
Five ML models trained on 25 years of health plan operational data score denials, LPI, and FWA before they land, surfacing $40M in late-payment interest risk for a top-5 national plan.
Data intelligence solutions
Where health plan data lives and where the models run
Four layers from canonical data backbone to audit-ready lineage. AI models running across them. Built for health plan environments, not retrofitted.
Connects to what health plans already run
An end-to-end data backbone from ingestion to consumption, with a canonical schema that unifies claims, members, providers, and clinical data, and pre-built connectors for FACETS, QNXT, HRP, and HealthRules.
- Domain API contract mapping
- Captures claim, eligibility, auth updates
Score the loss before it lands
Pre-built AI models predict claims nearing payment deadlines, provider outliers, and denials likely to become appeals, flagging risks before losses land.
- Propensity scoring
- FWA propensity flagging
- Provider risk scoring
- Claim complexity routing
- Denial likelihood modeling
Signals most plans never see
Process mining turns operational event data into continuous diagnostics, showing where processes deviate, what the deviation costs, and the earliest point to intervene, across 15 to 20 critical health plan processes.
- 50+ behavioral patterns detected
- Intended versus executed comparison
- Digital process twin
Every decision explainable and traceable
Audit and lineage are built at ingest, not retrofitted. HIPAA-compliant across all delivery geographies, with every downstream decision traceable to input and model version.
- HITRUST, SOC 2 Type 2, ISO 27001, HIPAA, SSAE 18
- Audit trail at every data point
- Model-version traceability
WHO WE SERVE
Your plan type determines where the data gaps cost most
Denial patterns and data gaps sit in different places depending on your plan type and line of business.
National plans
Data gaps remain hidden across every line of business
We use process mining to uncover recurring denial and leakage patterns across Medicare, Medicaid, Commercial, and Exchange workflows, giving health plans end-to-end operational visibility.
Blues plans
Process data mined without complex migration
We apply pre-defined event log schemas across QNXT, Facets, and HealthRules to mine critical health plan processes without disrupting the underlying platform.
Self-funded
Every data error lands on the employer's book
We use behavioral pattern detection to identify recurring data deviations before they reach the payment cycle, helping self-funded employers avoid costly errors.
PROOF OF DELIVERY
Live data programs, each proving a different result
Pattern detection, process coverage, and operational tenure. Each from a different proof point.
50+
detected behaviors
Process mining detects more than 50 recurring behavioral patterns across plan workflows, with logic that flags repeat deviations automatically.
~20
processes mined
Pre-defined event-log schemas cover 15 to 20 critical health plan processes, from claims to prior auth to payment.
8
of the top 10 US health plans
Our domain expertise sits on 25 years of health plan data drawn from operations for 8 of the top 10 US health plans and more than 60 plans served.
INSIGHTS
Latest from the Firstsource team
Insights from the field, real operations, real outcomes, and perspectives from the people making it work in live operations.
Case Study
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Contact Us
Most health plan losses show up in the data first. Catch them there.
The models flag what manual review misses. The engagement starts with what they find in your book.
- 5 pre-built ML models on 25 years of health plan data.
- Audit-ready lineage and HIPAA compliance built at ingest.
- Priced on outcomes: LPI, denials, and FWA prevented.