Discovery
[Weeks 1 to 2]Onboarded with the insurance client and analyzed the reporting landscape along with the organization stucture of relevant user base.
Claims Reporting Ecosystem for Claims Group
Transforming Insurance Intelligence with the power of AI
Due to NDA, some content and specifics have been redacted.
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The goal
From fragmented dashboards to decision-ready intelligence
My client, a leading insurer, set out to revamp 100+ fragmented legacy dashboards and disconnected data sources that were slowing operations and decision-making.
Across 7 diverse business user groups, inconsistent KPIs and manual calculations created data latency and made it difficult to turn reporting into actionable insights. Meanwhile, short-term dashboard fixes had created a fragmented ecosystem that was difficult to scale.
My goal was to uncover the root causes behind this reporting chaos and create a scalable intelligence layer connecting validated data and meaningful KPIs to the needs of executives, managers, and analysts.
User need
Business users needed a reliable way to find and understand the right KPIs without manual calculations or digging through fragmented reports, so they could make timely, confident decisions.
Business need
The business needed to replace fragmented, short-term reporting solutions with a scalable and unified dashboard ecosystem that could support faster decision-making, operational efficiency, and future growth.
My role and ownership
From discovery to delivery: Co-leading research & prototype creation
Research
[Weeks 3 to 4]Co-led 30 (40-min) user interviews/observations, 7 focus groups (84 participants - 60-min) and understood the data. Ran 60-min card sorting activities for top 100 KPIs with all 3 user bases.
Brainstorming
[Week 5]Synthesized the findings and focused on points of friction and user goals. Created mental models. Registered user flows with buiness goals and user actions. Brainstormed with claims industry experts for AI based recommendations and overall reporting system feedback.
Design and testing
[Weeks 6 to 9]Designed lo-fi dashboard wireframes for quick testing with the users. Evaluated intial design screens with 30 users (30-min). Created an inclusive data visualization design system from scratch in Figma. Rendered hi-fi design screens. Prompt engineered executive summaries for crucial dashboards.
Client Hand-off
[Weeks 10 to 12]Presented research and design pieces to the buisness stakeholders. Handedover the technical specification and functional specification of the dashboards.
Research
I needed to understand how claims teams used reports today and what they actually needed from them.
User Interviews
The interviewes were all about gathering the pain points around report usage behavior.
The research plan evolved as I realized that users could describe the data they used, but not always the KPI they needed. I shifted the conversation toward their day-to-day business goals, decisions, and workarounds to uncover the underlying requirements.
Data Understanding
I anlayzed 10K+ data columns to understand the kind of information is being populated on the existing dashboards; how are the daat points related and calculated. More than 1K attributes were anlyzed and mapped to the user flows as per the personas.
Focus Groups
I conducted 7 focus group sessions across three rounds. It was about understanding KPIs used by each user group (executives, managers and analysts in each organizational group or insurance life-cycle); what they use today vs what KPIs they need and why. It helped in validating and prioritized the redesigned KPIs by criticality of buisness needs.
Key insights
The gap between user goals and existing KPIs was a major miss
Users knew what they needed to achieve, but couldn’t translate their goals into the right KPI
The problem wasn’t simply that the dashboards lacked the right metrics. Contextual inquiry revealed a deeper gap: business users understood their goals and decisions, but struggled to articulate the specific KPI needed to represent them. As a result, they often downloaded report data and performed their own calculations to arrive at the answer.
“I was not sure how to ask for this metric I calculate because it involves many permutations and combinations to say it is the right one.”
Senior Claims Executive, reviewing the claims executive dashboard
Users had to piece together fragmented data to create a number they could trust
The reporting problem extended beyond the dashboards themselves. Users were pulling information from three different sources, dealing with data that wasn’t always current, and ultimately using a physical calculator to validate the savings value they needed. This revealed a deeper trust and data-fragmentation problem: the system didn’t provide a single, reliable source of truth for critical business decisions.
“I always find the savings value on my calculator!”
Senior Claims Manager, reviewing the claims executive dashboard
The challenge
How might we unify goals, data, and KPIs so that claims users can make decisions without piecing information together?
Process
A unified design strategy across 3 different user groups
Sprint 1
The thinking
01
The Decision-First Mapping
I created a decision mapping as soon as I found out the roles and their hierarchies in the organization and their critical business goals. I added potential solution decisions to ensure we are setting the right expectations from the beginning.
02
Mental Models
I created archetypes to map each user group's mental model. This helped us get to the core of the problem, stand in the users' shoes, and understand what they said and why. Ultimately, this made it much easier to identify and jot down the key pain points and requirements.
Sprint 2
The building
03
Inclusive Intelligence
I created an accessible design system featuring a strategic color palette, high contrast, and prominent status indicators for risk. It was designed to guide users through the necessary information to achieve their goals. Accessibility was critical, as the existing system lacked readability, consistency, and sufficient contrast. As a result, colorblind users could easily distinguish status indicators and intuitively navigate data through simplified visualizations.
04
Strategic Interaction Design
- High-level KPI cards with month-over-month (MoM) variance: Architected a 'Primary Health' layer using standardized MoM variance indicators. This allows Executives to identify high-risk anomalies in under 5 seconds without manual data mining
- A dedicated natural-language box for automated insights Bridging the Insight Gap: Integrated an AI-driven summary layer that translates raw fraudulent claim rates into actionable monitoring recommendations, reducing the cognitive load of data interpretation by 30%
- Profitability Analysis Layer: Engineered a dual-axis visualization to track the relationship between Claims Paid and Premiums Earned. By calculating the Loss Ratio in real-time, I provided Executives with a direct indicator of portfolio health, moving the needle from 'data tracking' to 'underwriting strategy
- Clean, tiered data table with cross-filtering capabilities / Structured Drill-Downs: Developed an interactive hierarchy mapping 1,000+ attributes into a regional performance matrix. This provides the 'Analyst' tier with the granular root-cause data needed for settlement adjustments
The pivot
Pivoting to account for business context
The data roadmap was solving yesterday's problems
Midway through the engagement, I discovered that the Data Engineering team was building new Data Marts by lifting and shifting legacy attributes, while our research had uncovered new KPIs that users actually needed to make decisions. The existing roadmap could reproduce the old reporting system, but it couldn't support critical metrics like Savings Value and Claims Velocity.
I used the KPI Validation Sheet to map business criticality against technical effort, bringing Business and Technology leads together to identify the data gaps and prioritize the KPIs that mattered most.
The new direction
- Reconciled user needs with data constraints
- Used evidence to challenge the data roadmap
- Prioritized the data needed to move the needle
And the outcome...
- Connected business goals to the data required to measure them
- Created a trusted foundation for high-priority KPIs
- Used AI to surface what needs attention, not just more data
Impact
Less than 20 mins spent on average instead of 2 days in learning about the insights
- Decision Velocity: Reduced average "time-to-insight" by 25%, allowing executives to identify and address loss-ratio spikes in real-time rather than at the end of the fiscal month.
- Cognitive Load Reduction: Improved insight discovery by 35%, enabling users to identify insurance risks without manual data mining.
- Standardizing Quality: Established a new usability benchmark (3.7/5) for enterprise reporting, surpassing legacy satisfaction baselines.
- Operational Efficiency: Executed a consolidation strategy that decommissioned 33% of redundant legacy reports, reducing maintenance overhead.