Transforming Insurance Intelligence: Scaling 1,000+ Data Attributes into Executive Insights
I led the end-to-end redesign of a legacy insurance claims reporting ecosystem, consolidating 100+ fragmented reports into a unified, AI-enhanced Power BI system. Bridging the gap between SQL-level data architecture and C-suite strategy, I reduced time-to-insight by 25% and established a new corporate standard for accessible and data-driven design.
Role: Lead Product Designer | Timeline: 12 Months | Tools: Figma, Power BI, SQL, DAX, Copilot
Executive Summary: Transforming Claims Intelligence
A leading insurer faced stagnation in their operations. Claims processing was hindered by a legacy ecosystem of 100+ fragmented reports and disconnected data sources. 5 diverse business user groups lacking defined KPIs, resorted to manual calculations, leading to critical latency in executive decision-making.
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I led a cross-functional transformation to replace reporting chaos with a unified intelligence layer. By auditing data at the SQL/DAX level and conducting deep-dive KPI validation using in-dept user research, I bridged the gap between raw data marts and user goals. I architected a three-tiered Power BI ecosystem (Executive, Manager, Analyst) underpinned by a custom, inclusive design system.
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To solve executive "data noise," I integrated a Copilot-driven summary layer. I engineered custom prompts to bypass raw numbers, instead delivering high-level reasoning and actionable focus areas.
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AI-Generated Executive Insight Example: "Spike in pending claims due to regional licensing delays." (9 words - quick focus area!)
33%
Reduction in Report Redundancy
25%
Increase in Decision
Velocity
40%
Realignment from Data to Business Goals
I analyzed 1000+ attributes and my design strategy resulted in a 30% increase in claims processing visibility and eliminated manual KPI calculation for 50+ business users
The Context: From Chaos to Clarity
Problem Area
The insurance company was processing claims at a very low operational velocity. The reason was not that the teams were less productive but the reporting system in the entire claim lifecycle had latency issues, business users had no clear Key Performance Indicators (KPIs) defined even though they were best at their jobs. The CEO decided to engage with our team to transform the entire existing legacy reporting system.
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One particular business user mentioned that for instance a KPI around the total savings value was never a part of the dashboard due to complicated reporting systems and data. They literally used a calculator to get the value each time. At first it looked like the reports were at fault because they did not have correct KPIs. But further in-depth user research showed us that business users were not used to asking for the right KPIs or were unaware of how to translate their goals into KPIs with the report developers.
Apart from this the data sources were fragmented (and disconnected at peak hours) not just the reports. Our team created a unified data source or data marts to serve this purpose. On the top of that DAX queries were written to pull the right KPIs after validating those and interpreting and articulating those from the users.
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.
Role | Business Goal | Critical Decision Area | Solution Design |
|---|---|---|---|
Executive
| Root Cause Analysis | "Is our risk exposure within acceptable thresholds for Q4?" | AI-Powered Summary: A Copilot-driven narrative highlighting "Loss Ratio" spikes without requiring manual drill-downs. |
Claims Manager | Operational Velocity | "Where are the bottlenecks in the claim lifecycle causing latency?" | Exception-Based UI: High-contrast symbols flagging claims that have exceeded "Mean Time to Settlement" (MTTS) targets. |
Data Analyst | Portfolio Health | "What specific attributes (e.g., region, adjuster) are driving the spike in reserves?" | Interactive Drill-Downs: 1,000+ attribute accessibility via cross-filtering, powered by optimized DAX queries. |
System Evaluation Diagram
Before: No user goals considered in the reporting system
After: User aligned report design (role based hierarchical design)
Power BI's complex calculation were backed by user goals, their actions and business decisions
The Discovery: Validating the Truth
The Process
I conducted 7 focus group sessions in two rounds. The first round was all about gathering the pain points around report usage behavior and the second one was about understanding KPIs they use today vs what KPIs they need. It was not straightforward. I had to realign the plan to elicit the actual KPI needs by asking questions about their business goals day in and day out with respect to the reports and business values.
The KPIs were redesigned for the users and a third round to validate the KPIs and to order them as per priority or criticality of use took place.
The Logic Bridge
This was how the KPIs actually transitioned from user goals, acknowledging the manual process and getting rendered in the reporting system. I used SQL to audit the data transformation layer, ensuring our design was grounded in technical reality, not just user assumptions.
Automated the 'Calculator Gap' by translating manual user workarounds into scalable DAX logic
Risk Mitigation
To mitigate the risk of rejection during the transition from legacy spreadsheets, I conducted iterative usability testing with 15 power users. This led to a 40% reduction in 'time-to-insight' and ensured the KPI logic matched the mental models of the actuarial team.
The System: Inclusive Intelligence
The Inclusive Design system
Business was not hesitant about having an inclusive system, they were not aware of the power of an accessible design system. The report developers were unaware of how to design a dashboard mapped to user goals. A part of it goes without saying that inclusive design is the need of the hour. Probably the developers might be afraid people will not appreciate simplified colors and had a mindset of more colors means good dashboard.
Due to non disclosure agreement, the colors use din my designs are not all inclusive. Also, the color palate cannot be presented here.
Before: Rainbow Dashboard with high cognitive load, inaccessible
Designed to show all data
After: The Accessible System with Strategic use of color, high contrast, and status for risk
Designed to tell a story to the relevant user with required information to achieve their goals
Along with calculating measures and calculated columns, DAX was used to create symbols and render card designs that stand out
The Strategy: The Hardest Pivot
Reconciling Data Reality with User Needs
Midway through the engagement, we hit a critical misalignment. While my research and focus groups had identified high-priority KPIs essential for executive decision-making, the Data Engineering team was building Data Marts based on a "lift and shift" of legacy attributes.
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The technical roadmap was perfectly optimized to replicate a broken system, but it lacked the new data attributes required to fuel the "Savings Value" and "Claims Velocity" KPIs the business actually needed (I mentioned in the case study in the problem area about how calculators were used to calculate some KPIs)
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The Trade-off: Scope vs. Impact
The project was at a standstill. I had two choices:
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Design the dashboards using only the "easy" existing data (resulting in low-value tools).
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Advocate for a structural change in the Data Mart roadmap (risking the timeline).
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The Action: Data-Driven Advocacy
I leveraged the KPI Validation Sheet I had developed. I filtered the metrics by "Business Criticality" and "Technical Effort." I then convened a session with the Business and Technology leads to present a "Truth Map."
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Evidence-Based Negotiation: I demonstrated that without specific new attributes, the Executive AI Summary (Copilot) would have no "intelligence" to pull from.
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Strategic Prioritization: I proposed a two-phased roadmap. Phase 1 focused on the Top 5 Priority KPIs that moved the needle for the CEO, even if it meant delaying secondary features to update the Data Mart schema.
The Result: A Unified Roadmap
By stepping out of the "Design" silo and into "Product Strategy," I successfully influenced both the Data and Business teams. My points were validated: the Data Marts were adjusted to follow User Goals, and the top-priority KPIs were built into the first release. This shift ensured the final product wasn't just a "new look" for old data, but a functional tool for business growth.
The "AI" Philosophy
Users, especially executives complained about cluttered and data heavy visuals. They had business decisions to make which would get delayed when just because they could not find the right data point for their argument. Copilot was a feature that came with Power BI and was well practiced by me and my team. It looked like executives would follow this scenario in real life: They had goals, they would make some decisions looking at the dashboards and then take an action to impact the business in some way. To make it simple and for them to look at what data needs attention in the given report context, using executive AI summary and quick question answer chat felt like a helping hand to help them look at the obvious and also focus on problem areas.
​The Showcase: The Tiered Ecosystem
Executive Summary Dashboard
This dashboard was created by targeting an executive user who need s quick summary of what is happening in the claims world and if there are any standout insights that need attention.
Following are some important design decisions made during the process.
​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%
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
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
A side-panel conversational AI /Conversational Intelligence
Engineered a Copilot interface to handle complex ad-hoc querying. I designed the conversation beyond 'what is' and added depth to the conversation to show 'why and what can be'