TPA Adjuster Performance Transparency

Provides data-driven visibility into third-party administrator adjuster quality, speed, and consistency to support policy pricing and underwriting risk assessment.

The Problem

“TPA Adjuster Performance Transparency for Underwriting and Pricing”

Organizations face these key challenges:

1

Limited adjuster-level visibility across external TPAs

2

Performance data spread across claims systems, notes, and audit files

3

Manual audits cover too few claims to detect systemic issues

4

Inconsistent claims handling creates underwriting and reserving risk

Impact When Solved

Quantifies adjuster quality, speed, and consistency at individual and TPA levelsImproves underwriting risk assessment using operational claims handling signalsReduces dependence on manual audits and subjective performance reviewsEnables earlier detection of outlier adjusters, process drift, and claims leakage

The Shift

Before AI~85% Manual

Human Does

  • •Collect KPI reports, audit files, and claims summaries from TPAs
  • •Review sampled claim files and spreadsheets to assess adjuster performance
  • •Compare TPAs using periodic audits and claims manager feedback
  • •Escalate concerns about inconsistent handling or poor outcomes

Automation

    With AI~75% Automated

    Human Does

    • •Review adjuster and TPA risk signals and decide when intervention is needed
    • •Approve underwriting, pricing, or vendor management actions based on performance insights
    • •Investigate flagged outlier adjusters, disputed scores, or unusual claim patterns

    AI Handles

    • •Aggregate claims data, notes, correspondence, and audit results into adjuster-level performance views
    • •Score adjusters on quality, speed, consistency, and adherence to handling expectations
    • •Risk-adjust benchmarks across claim type, severity, jurisdiction, and litigation exposure
    • •Continuously monitor for deterioration, outliers, and early signs of leakage or escalation

    Operating Intelligence

    How it works

    AI watches every signal continuously.

    Humans investigate what it flags.

    False positives train the next watch cycle.

    Confidence90%
    ArchetypeMonitor & Flag
    Shape6-step linear
    Human gates1
    Autonomy
    67%AI controls 4 of 6 steps

    Who is in control at each step

    Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

    Loop shapelinear

    Step 1

    Observe

    Step 2

    Classify

    Step 3

    Route

    Step 4

    Exception Review

    Step 5

    Record

    Step 6

    Feedback

    AI lead

    Autonomous execution

    1AI
    2AI
    3AI
    5AI
    gate

    Human lead

    Approval, override, feedback

    4Human
    6↺ Loop
    AI-led step
    Human-controlled step
    Feedback loop
    TL;DR

    AI observes and classifies continuously. Humans only engage on flagged exceptions. Corrections sharpen future detection.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

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