AgentisIQ  ·  Strategic Operations Architecture

Claims Decision Infrastructure

Porting the closed-loop architecture of experimentation platforms into P&C insurance claims operations — treating every strategic choice as a formal, trackable, learning-generating object.

▣ Decision-as-First-Class-Object  |  Closed Feedback Loop  |  Claims Operations
01
Decisions as Objects
Every claims decision is a formal, versioned, trackable entity — not a conversation
02
Hypothesis First
No decision is executed without a documented expected outcome and rationale
03
Expected vs. Actual
Every decision generates a measurement gap that must be reconciled and explained
04
Forced Closure
The loop cannot remain open — learnings feed back into the decision playbook
The Decision Loop Architecture — click each stage to expand
STAGE 01
Decision Registration
The decision is formally created as a first-class object with owner, scope, and type
→
STAGE 02
Hypothesis Definition
Expected outcome, timeframe, and success metrics are locked in before action
→
STAGE 03
Outcome Tracking
Actual results are measured against the prediction at defined checkpoints
→
STAGE 04
Loop Closure
Gap between expected and actual is analyzed and fed back into the decision playbook

Stage 01 · Decision Registration

Required Fields
  • Decision type (coverage / liability / settlement / litigation / vendor)
  • Claim ID and line of business
  • Decision owner and approving authority
  • Triggering condition or escalation path
  • Relevant reserve amount at time of decision
What This Replaces
  • Adjuster notes buried in the claim diary
  • Verbal approvals with no audit trail
  • Reserve changes with no documented rationale
  • Litigation referrals logged only in the law firm's system
  • Vendor assignments driven by habit, not data
System Analogy
  • Optimizely: creates a named, versioned experiment entity
  • Claims Infra: creates a named, versioned decision entity
  • Both: immutable record from the moment of creation
  • Both: cannot be altered without a logged amendment
  • Both: the starting point for all downstream measurement

Stage 02 · Hypothesis Definition

Claims Hypothesis Examples
  • "Accepting this BI demand at $45K will close the claim within 60 days and avoid $12K in legal fees"
  • "Assigning an SIU referral now will reduce indemnity by 30% on this file"
  • "Denying under Exclusion 7(b) carries <15% reversal probability on appeal"
  • "Subrogation demand to carrier X will recover ≥ $8,000 within 90 days"
Locked-In Metrics
  • Primary: indemnity paid vs. predicted
  • Secondary: cycle time, legal spend, reserve adequacy
  • Measurement date — when the outcome will be read
  • Confidence level (high / medium / speculative)
  • Comparable past decisions used to form the hypothesis
Why This Matters
  • Forces explicit reasoning before pressure of claim urgency
  • Creates an auditable basis for reserve adequacy reviews
  • Enables retrospective calibration of adjuster judgment
  • Surfaces systematic optimism or pessimism in team forecasts
  • Replaces "that's just how we handle these" with documented logic

Stage 03 · Outcome Tracking

Tracked Dimensions
  • Final indemnity vs. predicted indemnity ($ delta)
  • Actual cycle time vs. projected closure date
  • Litigation rate of decisions predicted to avoid suit
  • Reserve adequacy at 30 / 60 / 90-day development
  • Subrogation recovery realization rate
Automatic Triggers
  • Reserve change >25% flags decision for review
  • Claim exceeds predicted closure date by 30+ days
  • Litigation filed on a file predicted to settle
  • Coverage denial reversed on appeal
  • Subrogation demand rejected or expired
Reporting Surface
  • Decision accuracy score per adjuster and team
  • Hypothesis calibration curves (over/under-confident)
  • Decision type heatmap by outcome variance
  • Reserve development waterfalls by decision cohort
  • Legal spend attribution to specific decision choices

Stage 04 · Loop Closure

Closure Requirements
  • Written variance explanation when delta exceeds threshold
  • Attribution: market factor, adjuster judgment, new information?
  • Decision playbook update — or documented reason not to update
  • Peer review trigger for decisions with high negative variance
  • Supervisor sign-off on closure memo for authority-level decisions
Playbook Updates
  • Settlement authority bands revised based on outcome cohorts
  • Vendor scorecards refreshed from tracked performance
  • Coverage position templates updated from denial reversal data
  • SIU referral triggers recalibrated from fraud detection hits
  • Litigation hold criteria tightened or loosened from cost data
Organizational Output
  • Adjuster judgment calibration improves quarter-over-quarter
  • Reserve accuracy becomes a measurable, improvable KPI
  • Best decisions become institutional templates, not tribal knowledge
  • Leadership can trace loss ratio movement to specific decision patterns
  • Regulatory audits answered with decision-level evidence, not summaries
Decision Object Schema — five canonical claims decision types
Decision Type Hypothesis Template Primary Metric Measurement Window Loop Closure Trigger
Coverage Determination
Coverage		 "Coverage applies / does not apply under [policy section] because [stated basis]; estimated exposure if wrong: $[X]" Denial reversal rate on appeal Final resolution or 180 days Appeal filed, coverage reversed by court or DRO
Liability Assignment
Liability		 "Insured is [X%] liable based on [investigation findings]; predicted indemnity impact: $[Y]" Indemnity delta vs. predicted split Settlement or verdict Actual allocation deviates >20% from predicted
Settlement Authority
Settlement		 "Settling at $[X] will close within [N] days and avoid $[Y] in projected defense costs" Cost of claim vs. modeled alternative 60 / 90 days post-decision Claim litigates after predicted settlement, or settles above authority
Litigation Strategy
Litigation		 "Defending to verdict has [X%] win probability; expected all-in cost: $[Y] vs. demand of $[Z]" All-in litigation cost vs. settlement alternative Verdict or resolution Verdict or settlement exceeds predicted cost by >25%
Vendor / Expert Selection
Vendor		 "Using [vendor] for [service] will deliver [outcome] at $[cost] within [timeframe]" Vendor performance score vs. baseline File close or 90 days Vendor performance below tier threshold; cost overrun >15%
Infrastructure Contrast — traditional BI vs. decision infrastructure
✕   Traditional Claims BI (The Proxy Trap)
—Dashboards show what happened; they cannot show why decisions were made
—Loss ratios surface after the fact — decisions that caused them are untraceable
—Adjuster skill is evaluated on outcomes alone, not on the quality of reasoning
—Reserve changes are logged but the logic behind them lives in the adjuster's head
—Good decisions become tribal knowledge and leave when adjusters leave
—The same bad decision gets made repeatedly because there's no feedback loop
—Leadership manages by KPIs, not by the quality of the decisions producing them
✓   Claims Decision Infrastructure
→Every decision is a traceable object linked to its outcome — the "why" is always recoverable
→Loss ratio movement is attributable to specific decision patterns and cohorts
→Adjuster calibration — the gap between predicted and actual — becomes a coachable metric
→Every reserve change carries a locked-in hypothesis that must be resolved
→Best decisions are institutionalized as playbook templates that survive turnover
→The feedback loop forces the organization to learn from variance, not just observe it
→Leadership manages decision quality — the upstream cause of every KPI