Fictional training simulation. D2V Mock is an invented training organization. All people, data, incidents, and documents are fictional. Any resemblance to a real entity is coincidental.
MISSION 06 · RESPONSIBLE CLAIMS TRIAGE
COMPLETE GUIDED LAB
D2V Mock
Insurance
Design an AI-assisted claims-triage workflow that organizes evidence and urgency while preserving adjuster authority, auditability, and bias controls.
Guided lab available: review the public mission specification here, then enter the paid workspace for staged evidence, decisions, artifacts, progress tracking, and portfolio export.
01 / CORE QUESTION
Which claims need attention first—and what may the system decide?
The simulated property insurer receives a surge of property claims after regional weather events. Intake teams manually sort incomplete submissions, identify urgent living-condition or safety needs, and route cases to adjusters. Leadership wants AI-assisted triage, but claim complexity, policy coverage, fraud signals, claimant communication, and regulatory expectations make automated disposition inappropriate.
The mission is not solved by producing a fluent recommendation. The learner must define the decision boundary, reconstruct the evidence, build an inspectable workflow, and establish what would justify deployment.
DECISION TO IMPROVEWhich claims require immediate human attention, what evidence supports that urgency, and where must the system be prohibited from recommending coverage or denial outcomes?
CORE METHOD → MISSION
One operating decision. Multiple deployment boundaries.
The Core Labs isolate individual failure modes. This mission forces several of them to coexist under one customer timeline, one evidence room, and one final deployment recommendation.
$79STANDARD INDIVIDUAL PRICE · REGULATED MISSION · LAUNCH ACCESS IS CURRENTLY FREE
Can claims-triage assistance be released without turning a useful prediction into an opaque or biased claims decision?
Narrative text, policy facts, severity indicators, prior handling, model features, and human notes differ in authority and can create proxy effects across customer groups.
Choose shadow, recommendation-only, limited pilot, or stop — with fairness slices, evidence lineage, human review independence, and critical-error containment.
CORE METHOD MAP
Review all 19 Core Labs →7 Core Labs are exercised here
02 / STAKEHOLDERS
Success has competing definitions.
The design must represent authority, incentives, operational constraints, and unacceptable outcomes rather than collapsing stakeholder needs into a generic requirements list.
Claims operations director
Needs faster intake, consistent routing, and measurable service improvement during volume spikes.
Senior adjuster
Requires complete source context and rejects black-box recommendations that blur triage with adjudication.
Compliance and legal lead
Owns auditability, consumer communication, retention, adverse-impact review, and prohibited uses.
Special investigations lead
Wants suspicious patterns surfaced without turning weak proxies into accusations or delayed assistance.
03 / EVIDENCE PLAN
The operating truth must be reconstructed.
The guided evidence room combines structured data, policies, interviews, and operational records. Every material conclusion must remain traceable to source evidence and freshness.
Claimant statements, loss date, location, occupancy, damage type, contact, and urgent-needs flags.
Coverage terms, deductibles, limits, exclusions, effective dates, and policy versions.
Photos, videos, estimates, receipts, police or fire reports, and document metadata.
Event footprints, wind, hail, flood, wildfire, and confidence by time and location.
Triage route, adjuster actions, cycle time, requests, outcomes, and reopenings.
Third-party scores, duplicate attributes, network links, and known limitations.
Calls, messages, accessibility needs, temporary housing requests, and missed contacts.
Permitted use, review authority, notice language, retention, monitoring, and appeal pathways.
EXPECTED DATA DEFECTSMissing documentsAmbiguous loss datesDuplicate claimsVendor score opacityAddress geocoding errorOutcome label bias
04 / REQUIRED BUILD
A thin slice that can change a real decision.
The build must connect evidence, logic, human authority, failure handling, and measurement. A model or dashboard alone is not a complete intervention.
Claim evidence model
Organize intake, policy, submitted evidence, event context, communications, and provenance without making legal conclusions.
Deterministic eligibility gates
Use explicit rules for missing information, duplicate checks, jurisdiction, and mandatory human review.
Urgency assistance
Surface safety, habitability, displacement, accessibility, and time-sensitive indicators with evidence citations.
Triage explanation interface
Show what changed priority, what is uncertain, which sources conflict, and what the adjuster must verify.
Bias and proxy review
Test features, errors, queue position, and service outcomes across relevant groups and geographies.
Governed feedback and monitoring
Separate corrections from labels, review drift, log overrides, and prohibit silent expansion into adjudication.
05 / STAGED COMPLICATION
The model learned documentation style instead of urgency.
The complication is released only after the learner commits the first problem frame, architecture, and evaluation plan.
REVEAL CASE COMPLICATION+
Shadow evaluation shows that short claim descriptions are materially more likely to miss urgent review. The pattern is concentrated in call-center, multilingual, low-bandwidth, and represented intake. Historical routing labels reflect documentation practices, staffing, backlog, and prior escalation behavior rather than a neutral measure of claim need.
Required response: Contain the pilot, identify affected recommendations, inspect features and labels, add direct emergency-need collection, revise fairness and service evaluation, and decide whether predictive routing should continue or narrow to evidence extraction and deterministic urgent-review prompts.
Remove brevity features
Necessary, but correlated proxies and invalid historical labels may preserve the same failure.
Improve direct intake
Collect operational needs explicitly instead of inferring vulnerability from writing style.
Narrow the use case
Retain evidence extraction, duplicate review, and deterministic urgent prompts without learned severity scoring.
Revise and retest
Requires adjudicated cases, slice thresholds, remediation, and independent release review.
06 / EVALUATION
Value and harm must be measured together.
The final deployment decision must use predeclared technical, operational, adoption, financial, and risk measures. The strongest metric cannot erase a critical failure.
| Measure | What it tests | Target behavior |
|---|---|---|
| Urgent-case recall | Share of genuinely time-sensitive claims receiving timely human attention. | High recall with severity-weighted review. |
| Triage precision | Whether expedited routing corresponds to documented urgency. | Avoid overwhelming specialist queues. |
| Evidence attribution | Whether each surfaced fact is supported by the displayed source. | Unsupported or misattributed claims fail. |
| Service-time improvement | Time to first meaningful contact and correct adjuster assignment. | Compare with current process and event volume. |
| Adverse-impact review | Differences in errors, delay, queue position, and requests for documentation. | Investigate mechanism, not one aggregate ratio. |
| Boundary compliance | Whether the system remains within triage and avoids coverage or denial decisions. | Any boundary violation is critical. |
The workflow distinguishes intake completeness, urgency, routing, investigation, and adjudication.
Every priority factor is traceable to evidence and governed policy.
Human adjusters retain authority over coverage, liability, payment, and denial.
Proxy and historical-label risks are examined rather than hidden behind performance metrics.
Claimants have a clear path for correction, accessibility support, and human contact.
The launch plan includes monitoring, incident review, rollback, and prohibited-use enforcement.
HOW TO WORK
Bring your own agent. Keep the evidence chain visible.
The lab permits agent-assisted investigation and implementation. Strong work records material agent recommendations, checks them against authorized evidence, and documents what the learner accepted, changed, rejected, or left unresolved.
Give bounded context.
Provide the mission objective, approved evidence, constraints, and required output rather than asking for a generic solution.
Ask for alternatives.
Require multiple hypotheses, failure modes, and disconfirming evidence before choosing an intervention.
Trace every material claim.
Check source IDs, calculations, code behavior, policy constraints, and unsupported causal language.
Own the recommendation.
Record why the final decision follows from the evidence and what would cause it to change.
07 / REQUIRED DELIVERABLES
The complete record of the deployment decision.
The reviewed lab will score evidence traceability, technical judgment, implementation quality, risk handling, operating readiness, and the consistency of the final recommendation.
Use and boundary memorandum
Permitted triage purpose, prohibited decisions, decision owners, and risk classification.
Evidence and policy model
Claim, policy, document, event, communication, provenance, and rule lineage.
Triage workflow design
Rules, model assistance, explanations, queues, human authority, and correction path.
Evaluation harness
Urgency cases, incomplete evidence, duplicate claims, proxy tests, and boundary violations.
Governance and monitoring plan
Approvals, logs, notices, access, drift, adverse-impact review, and rollback.
Executive deployment recommendation
Business value, unresolved evidence, conditions, scope, and launch decision.
GUIDED LAB
Enter the D2V Mock Insurance guided lab.
The complete guided workspace includes a synthetic claims evidence pack, governance decisions, staged evaluation cases, notebook, artifact templates, and portfolio export.