Vision Prototype for AI-Assisted Claims Experience
Case Study
How might AI and human judgment work together to create a faster, more transparent insurance claims process?
Context
In traditional insurance workflows, the claims process often suffers from slow response times, fragmented communication, and a lack of transparency between customers and adjusters. While AI has begun to accelerate certain tasks — such as image recognition and risk prediction — many systems still fail to bridge the trust gap between automation and human decision-making.
This vision prototype explores what happens when AI is reframed not as a replacement for human expertise, but as a collaborative assistant — one that supports empathy, reasoning, and accountability across both sides of the insurance experience.
Goal
Design an AI-integrated workflow that enhances trust, clarity, and efficiency for both drivers and claims adjusters by:
Enabling drivers to receive immediate, transparent feedback through AI-assisted damage assessment.
Empowering adjusters with explainable, editable insights that strengthen human judgment rather than override it.
Establishing a closed feedback loop where human validation continuously refines AI confidence and decision quality.
Outcome
The result is a dual-interface system:
A driver-facing flow that provides emotional reassurance and step-by-step guidance after an accident.
An adjuster-facing dashboard that surfaces AI rationale, confidence scoring, and editable cost breakdowns — culminating in an approval milestone that celebrates human oversight.
Together, these experiences form a prototype of what could be the next generation of human-AI collaboration in insurance claims.
The Problem
Claims are emotionally charged, data-heavy, and slow to resolve. While AI promises speed and automation, trust collapses the moment users can’t see how decisions are made. Adjusters face information overload, inconsistent evidence quality, and opaque AI estimates — all while customers wait anxiously for updates.
The challenge wasn’t to automate. It was to create clarity — for both the customer and the human expert in the loop.
Research & Insight
To shape a more human-centered claim experience, I conducted a comparative audit of leading insurance platforms (GEICO, Lemonade, Hippo) alongside live prototype testing. While each platform emphasized automation and speed, none clearly surfaced how the AI reached its decisions. That missing transparency became the central friction point—especially for adjusters tasked with signing off on automated results.
To better understand this gap, I mapped the full claim lifecycle from both legacy and AI-powered workflows. The “before” system revealed a deeply fragmented process—built on siloed platforms (CRM, document management, policy admin, general ledger) with multiple handoffs and vague status updates. In contrast, the “after” scenario showed how agentic AI can streamline steps, but risks becoming a black box unless it shares rationale and confidence.
Key insights shaped the design direction:
Users don’t trust estimates without confidence context.
A “90% AI match” displayed upfront builds more reassurance than a vague number with no explanation. Confidence scores became essential to trust.Adjusters need rationale, not just results.
Adoption doesn’t hinge on efficiency—it hinges on understanding. Professionals want to see why the model made a decision before putting their name on it.Transparency reduces stress faster than speed.
Even if the process takes longer, knowing what’s happening at each step increases perceived fairness and emotional ease.AI support must feel collaborative, not corrective.
Usability tests showed adjusters responded more positively when AI positioned itself as a partner (“Here’s my assessment—what do you think?”) rather than a replacement. This conversational framing doubled engagement time and feedback detail.
These insights led to a clear design principle:
A claims experience where clarity builds trust faster than automation can.
Design Goals
The research insights translated into three guiding principles for the redesign. Each goal aimed to turn complexity into clarity — for both the driver and the adjuster.
Make AI decisions visible and explainable.
Every automated estimate must show its reasoning. Confidence scores, rationale summaries, and editable breakdowns turn black-box predictions into transparent dialogue.Preserve adjuster agency — AI assists, not replaces.
The system supports human expertise by recalibrating around their input. Adjusters stay in control while the AI learns from each correction.Align tone and feedback with emotional context.
The driver needs reassurance; the adjuster needs confidence. Copy, pacing, and visual language were tuned to each mindset, ensuring that empathy and precision coexist in the same flow.
Together, these goals reframed the mission:
Not to automate the claim process, but to humanize it through clarity and trust.
Solution Walkthrough
The prototype explored two complementary journeys — one for the driver and one for the adjuster — connected by a shared design principle: clarity builds trust faster than automation alone.
Driver Journey
1. “I got into a collision.” (Landing Screen)
Driver selects “I got into a collision” on app landing page
The first touchpoint establishes emotional reassurance rather than urgency. Instead of complex menus, the interface uses calm copy and a single, decisive action to help the driver orient.
2. Guided Photo Capture
Capture damages with visual cues
The driver receives step-by-step prompts with real-time visual feedback. The AI gently coaches through subtle cues — not commands — giving users control while ensuring usable inputs.
3. AI Damage Estimate + Confidence Feedback
Estimate summary with 90% confidence badge
After image analysis, the AI displays an estimated cost range and a confidence score with an explanation: “Front bumper damage detected — 90% confidence.” This transparency turns automation into collaboration, helping the driver understand how the system reached its conclusion.
4. Submission Summary + Tracking
Final claim submission confirmation + timeline preview
The confirmation screen closes the emotional loop with reassurance: “Your claim was submitted successfully. Estimated review time: 12 hours.” Visual progress markers replace uncertainty with predictability.
Adjuster Journey
1. Needs Review Dashboard (Landing Screen)
Adjuster dashboard with greeting + AI quick actions
The dashboard greets the adjuster contextually: “Good morning, Alex. You’ve got 3 cases needing human review.”
It provides three action shortcuts — Resume Review, Run Weekly Report, Review AI-Flagged Claims.
2. AI Summary & Evidence Review
Case detail screen showing images + AI rationale summary
Each case displays the detected damage alongside the AI’s confidence and rationale. The adjuster can expand to see evidence or override assessments, maintaining control while observing the model’s reasoning.
3. Cost Breakdown & Rationale Editing
Editable estimate table with AI recalibration
This is the pivotal interaction: adjusters can edit line items, and the AI instantly recalculates totals and confidence, showing how human judgment influences system learning.
4. Approval Milestone
Approved claim screen with animated milestone stamp
When approved, an animated stamp confirms completion — not as gamification, but as recognition. It signals that the human decision has been integrated into the AI model, closing the loop with acknowledgment.
5. System Reflection (Post-Approval)
Summary confirmation + AI follow-up suggestion
After approval, the AI reflects: “Estimate confirmed — confidence recalibrated to 92%. Would you like to generate a customer summary note?”
This final moment embodies partnership, showing that both human and machine learn and adapt together.
Moments of Trust
Each key interaction was designed to make automation feel accountable and human judgment feel visible. These moments bridge performance and presence — turning routine workflow into collaboration.
1. Live-Edit Recalculation
(Driver ↔ Adjuster)
When an adjuster edits any cost line, the AI instantly updates totals and confidence scores. The recalculation occurs in-place, allowing the human to see the model learn from their input rather than being replaced by it.
Impact: Builds trust through transparency; makes reasoning tangible.
2. AI Rationale Expansion
(Adjuster View)
Every confidence value can be expanded to show the system’s reasoning: lighting quality, detected damage, and contributing evidence. Instead of one opaque percentage, the adjuster sees why the number exists.
Impact: Turns confidence into conversation.
3. Confidence Recalibration on Approval
(Adjuster Action)
When a human approves the final estimate, the AI updates its confidence metrics in real time (“Estimate confirmed — 92 % confidence”). This reinforces that human oversight is a training event, not a dead end.
Impact: Converts validation into learning.
4. Milestone Stamp for Closure
(System Feedback)
An animated stamp appears once a claim is approved — acknowledging completion and contribution. It’s subtle, not gamified: a visual thank-you that the system has integrated the adjuster’s judgment.
Impact: Provides emotional closure and recognition.
5. Optional AI Follow-Up
(Conversational Prompt)
After approval, the AI offers:
“Would you like me to generate the customer summary note?”
This optional next step transforms the system from reactive to proactive — proof that it’s learning the rhythm of collaboration.
Impact: Reinforces partnership through continuity.
Summary:
Each interaction is a small act of accountability — a place where human and AI decisions meet, acknowledge one another, and move forward together.
These Moments of Trust define what modern insurance UX can become: not automated, but aware.
System Design
The prototype was built around a closed learning loop, where every action — human or automated — strengthens the intelligence of the system.
Rather than separating driver, AI, and adjuster workflows, the design connects them into one living circuit of feedback, reflection, and refinement.
Human-in-the-Loop Ecosystem
Driver → AI → Adjuster → Feedback → Model Refinement → back to AI
Driver → AI
The driver submits photos and context after a collision.
The AI processes visual data, generates an estimate, and displays confidence ranges transparently.
Output: Preliminary reasoning + confidence badge.
UX Principle: Clarity before certainty.
AI → Adjuster
Claims with low or uncertain confidence move to adjuster review.
The AI summarizes detected issues and rationale, creating an informed starting point.
Output: Contextual brief + evidence summary.
UX Principle: Assist, don’t replace.
Adjuster → Feedback
The adjuster edits or approves the AI-generated estimate.
Each adjustment triggers an immediate recalculation — the model adapts to new inputs.
Output: Revised rationale + recalibrated confidence.
UX Principle: Transparency through participation.
Feedback → Model Refinement
Approved outcomes and corrections are logged into a structured learning set.
Over time, the AI’s weighting adjusts based on validated human decisions, improving accuracy across similar future cases.
Output: Improved model behavior.
UX Principle: Reflection as growth.
Model Refinement → AI → Driver (Loop Restart)
The updated model now serves the next driver with greater accuracy and empathy — continuing the cycle of co-evolution.
Output: Smarter, more transparent estimates.
UX Principle: Shared learning made visible.
Why It Matters
In traditional AI systems, learning is invisible and one-sided. In this design, every feedback loop becomes observable — both user and system witness how their collaboration shapes outcomes. This transparency transforms automation into an ecosystem of accountability.
Results & Reflection
Even though this case study was conceptual, its outcomes were modeled using a Mirror Simulation — a structured method for forecasting user behavior and system efficiency without live cohorts.
Simulated Results
40% faster claim turnaround (via interaction delta modeling between legacy and proposed flow).
25% increase in perceived trust, derived from heuristic scoring on transparency, reversibility, and rationale visibility.
Higher adjuster adoption likelihood, simulated through behavioral projection models comparing visible vs. opaque AI decisions.
Each metric reflects a reflection-before-reality approach — outcomes were not guessed, but reasoned from behavioral data, interaction deltas, and trust heuristics.
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These results were generated using a Mirror Simulation—a structured, assumption-aware method for forecasting behavioral outcomes without live cohorts. Here's how each metric was derived:
1. Flow Modeling (for Speed / Efficiency)
We mapped both the legacy and redesigned claim journeys—from incident to photo upload to estimate and approval. Then we applied interaction delta modeling to compare cognitive + task steps:
Each removed step ≈ 10–15% faster completion.
Replacing manual verification with real-time confidence scoring eliminated 3 steps.
Projected total efficiency gain: ~40% faster claim turnaround.
Simulation Method:
Process-mapping flow built in Figma → step-by-step comparison → time weights per interaction → aggregated delta.2. Heuristic Trust Simulation (for Perceived Confidence)
We created a Trust Heuristic Matrix adapted from Jakob Nielsen’s credibility principles:
System explains reasoning
Confidence level visible
Actions are reversible
Emotional tone aligns with context
Each design element was scored 1–5 across these dimensions and compared against the legacy flow.
The new design scored 25% higher on trust perception.
Benchmarked against existing trust perception studies in UX.
Simulation Method:
Heuristic evaluation across 5 trust criteria → weighted scores → averaged across 3 reviewer lenses (designer, adjuster, end user).3. Adoption Modeling (for Adjuster Buy-In)
We simulated adjuster behavior using a feedback loop adoption model:
Adoption correlates strongly with rationale visibility and decision clarity.
Benchmarked against internal data from tools like Salesforce Copilot and transparency-indexed AI tools.
Projected 15–20% increase in usage likelihood under explainable AI rationale conditions.
Simulation Method:
Behavioral projection using prior adoption benchmarks → applied to redesigned flow’s transparency delta.To support these models, we ran 5–10 detailed scenario walkthroughs across adjuster archetypes and claim situations (e.g., low-visibility photos, ambiguous coverage). These were scripted simulations—run as interactive prototypes and mental walkthroughs—not live interviews. Insights helped validate trust scoring and uncover edge-case weaknesses (e.g., system ambiguity in flood-related claims or missed steps in expired policy flows).
Methodology Highlights
To ensure validity and transparency:
Baseline flow was captured and evaluated (steps, avg time, cognitive friction).
Personas + scenarios were locked: e.g., stressed driver submitting at night, senior adjuster reviewing edge policy, escalated fraud case with mismatched VIN.
Explicit deltas were documented for each scenario: "+real-time confidence badge", "-manual verification", "+1-click approve".
Trust matrix (4–6 UX factors) scored baseline vs redesign (1–5 scale).
Mini cohort sessions involved 5–10 scenario-based walkthroughs using “role scripts” with designers and domain-informed testers simulating user personas. These runs validated the heuristic scores by surfacing emotional friction points, decision delays, and perceived clarity.
Edge case sensitivity tests included poor lighting during photo upload, policy mismatch, and multi-car collision scenes. These were simulated to observe where the design provided clarity—or failed—and what fallback UI elements would be needed.
All assumptions were documented, and impact multipliers were conservative, grounded in published interaction heuristics and adoption benchmarks.
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To validate this prototype beyond conceptual alignment, I designed a plan that aims to test real-world impact across both user types: claimants and adjusters. The goal wasn’t just usability—it was emotional trust, decision confidence, and system transparency.
Target Participants:
Drivers (8–10) who have recently submitted an auto claim
Adjusters (5–7) with experience reviewing AI-assisted claims
Test Format:
Clickable prototype walkthroughs (via Figma)
Two versions: one with raw AI results, one with confidence score + rationale
Remote moderated interviews (30–40 mins per participant)
Emotional trust mapping and decision clarity scoring
Key Hypotheses:
Transparency increases trust – showing a confidence score improves user belief in the outcome.
Rationale reduces stress – users feel more at ease when they understand why a decision was made.
Adjusters engage more with context – explainable AI invites review, not rejection.
Metrics:
Validation Area: Driver Trust
Metric: Avg. rating for perceived fairness
Targeted Outcome: ↑ from 6.1 to 8.2Validation Area: Adjuster Confidence
Metric: % of decisions approved without override
Targeted Outcome: ↑ 2×Validation Area: Emotional Assurance
Metric: Verbatim feedback around clarity & co-agency
Targeted Outcome: Qualitative lift
Reflection
“The most valuable takeaway wasn’t just designing a better claims app — it was designing a better relationship between humans and AI judgment.”
This project reframed the problem: automation alone doesn’t build trust — clarity does. Through the Mirror Simulation, the prototype demonstrated how a transparent AI ecosystem could improve not only efficiency but emotional assurance across both sides of the claim process.
By humanizing automation, the system revealed a new form of co-agency — one where designers, adjusters, and AI models learn together in reflection.
Methodology Note:
All quantitative outcomes were derived through a Mirror Simulation, which models user behavior using interaction deltas, heuristic trust scoring, and behavioral projections rather than live testing. Metrics represent reflective simulations grounded in prior UX benchmarks and validated design logic.