From Doubt to Data Integrity: Redefining the Securitization
Experience
Capital Markets Engine
As the sole UX Designer for the Capital Markets team, I redesigned the Capital Markets Engine (CME)—an enterprise tool used to manage loan pipelines, create deals, and facilitate audits in the securitization process. My role involved leading user research, defining and refining requirements alongside the product manager and solution architect, and delivering high-fidelity, scalable designs that restored data trust and streamlined workflows across the Capital Markets organization.
Brief
The Capital Markets Engine replaces the legacy system, Sparta, which the team used to manage loan scenarios and deals. Over time, users lost trust in Sparta’s data accuracy, leading them to work in Excel—creating inefficiencies, duplications, and lack of transparency.
The redesign aimed to bring these offline workflows back into a unified platform, giving users the flexibility of Excel, the consistency of enterprise tools, and the transparency required for an auditable securitization process.
Result
Through user interviews, iterative design, and close collaboration with the product and engineering teams, I delivered a reimagined CME experience that:
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Gave analysts control over their data through editable tables and lock/unlock functionality.
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Reduced reliance on Excel, aligning users and developers around a single, trustworthy system.
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Introduced scalable patterns using AG Grid and design system tokens, accelerating design and development.
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Clarified requirements and MVP scope, transforming ambiguity into a feasible, user-centered solution roadmap.
My Role
Sr UX Designer
UX Researcher
Skills
Wireframing + Prototyping
User Interviews
Tools
Figma + FigJam
Jira
Team
Product Manager
Solution Architect
Engineers
Problem
Brief
The securitization process depends on accurate loan data—but users didn’t trust the information coming from upstream systems. In Sparta, all loans remained in open flow until locked into a deal, which meant the data could change at any time. Since upstream data from underwriting was often outdated or incomplete, analysts exported everything into Excel, made manual edits, and emailed spreadsheets during audits. This created hidden work, data discrepancies, and zero transparency in the system.
Current State
Sparta’s outdated interface and rigid functionality couldn’t keep up with the complexity of the Capital Markets workflow.
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Outdated UI: Sparse layout, no visual hierarchy, and limited filtering or sorting made it hard to navigate.
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Low adoption of existing features: Users ignored components that didn’t support their day-to-day processes, helping us deprioritize them for the MVP.
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Offline audit processes: Auditors reviewed Excel exports, sent back changes, and analysts re-entered updates manually, creating error risk.
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Broken data sync: Once loans were exported, they lost connection to upstream updates. By the time audits finished, data in Sparta was out of date.
These discoveries helped us prioritize what truly mattered: bringing Excel’s flexibility and transparency into CME, while enabling better control of data integrity.
Solution Overview
Smarter Editing Experience
Through user research, I identified that editing was essential to the workflow—not just for six fields, as originally scoped, but for over 100 potential attributes per loan. I designed scalable single-loan and bulk-edit patterns that gave users flexibility while keeping system performance and data accuracy intact.
Excel-Like Functionality
I introduced AG Grid to replicate the familiar filtering, sorting, and bulk editing that users relied on in Excel. Because AG Grid was already used in other enterprise tools, developers were familiar with it, reducing build time and costs while keeping UI patterns consistent across platforms.
Data Control
To address trust issues, I worked with solution architects to design a lock/unlock flow that let users decide when to stop syncing from upstream data. Once analysts verified or edited values, they could lock them—preserving their version while still maintaining upstream visibility.
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Understanding the Problem
Capital Markets analysts and managers handle multiple deals simultaneously and rely heavily on precision and timing. The legacy tool slowed them down and left them uncertain about their data.
CME needed to not only streamline their workflows but also rebuild trust—ensuring users felt confident that what they saw in the system reflected the most accurate information available.
What users struggled with
Misaligned data context: Upstream values from underwriting often required adjustment to fit securitization workflows.
Restricted editing: Analysts lacked the flexibility to update multiple attributes as loan details evolved through deal creation.
Offline audits: Manual Excel workflows reduced visibility and caused delays.
Business needs and goals
Transition from legacy to enterprise platform: Integrate the securitization team into the new enterprise ecosystem, aligning with other departments already using the improved platform.
Define MVP and clarify scope: Refine ambiguous requirements and prioritize features for the first release of the Capital Markets Engine.
Improve data transparency and workflow efficiency: Replace fragmented offline processes with a single, auditable tool.
User Interviews
I conducted four interviews with managers and analysts to understand their roles, daily tasks, and frustrations. Working with a UX researcher, we took detailed notes, asked probing questions, and synthesized findings through card sorting. These insights revealed the full complexity of the securitization process—from upstream data handoffs to downstream audits—and guided our MVP priorities.
KEY INSIGHTS
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Editing is essential at scale.
Analysts needed to edit any attribute within the loan record, not just the few initially scoped. This insight completely reshaped requirements and highlighted how integral editing was to their process.
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Data integrity drives trust.
Upstream data from underwriting was unreliable, forcing analysts to rebuild it manually. By introducing editable fields and the lock/unlock system, we gave users control over accuracy while maintaining data integrity within defined boundaries.
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Excel familiarity increases adoption.
Analysts viewed Excel as both a safety net and a power tool. Integrating AG Grid brought familiar features—filtering, sorting, and bulk editing—into CME, making it a natural transition for users and a cost-effective choice for developers.
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Some workflows remain offline.
Third-party audits still happen in Excel, but we designed for seamless re-import of audited data, ensuring CME stayed the single source of truth after external reviews.
Design Strategy
Brief
To focus our scope and solve the most critical workflow gaps, I prioritized three key pain points surfaced during research and collaboration with the product manager and solution architect. Each informed a major design decision that improved trust, accuracy, and efficiency in the Capital Markets Engine.
PAIN POINT
Analysts relied on Excel to correct inaccuracies from upstream data before sending it to auditors. These offline edits introduced errors and limited visibility across the team.
HOW MIGHT WE
How might we enable analysts to make accurate, auditable edits directly within the tool—reducing reliance on Excel and minimizing errors passed to auditors?
FEATURE
I designed editable data tables that allow analysts to update fields in real time within CME. This ensured that changes were immediately visible, traceable, and part of the same workflow, reducing redundant data entry and improving data accuracy before audits.
PAIN POINT
Users wanted a way to manage only the data relevant to the securitization process, not everything imported from underwriting. They also needed stability while waiting for third-party auditor updates.
HOW MIGHT WE
How might we give analysts control over which data remains dynamic and which should remain stable during the audit and securitization process?
FEATURE
I designed a lock/unlock mechanism that lets analysts determine when to stop receiving automatic upstream updates. Once locked, only their verified edits persist—allowing them to focus on the most relevant and finalized data for securitization while minimizing disruptions from ongoing changes upstream.
Mapping the Securitization Journey
To visualize the securitization process uncovered during discovery, I created a detailed user flow outlining each step in the Capital Markets workflow—from managing pipelines to finalizing deals.
Since the requirements were still ambiguous, I annotated the flow with questions and assumptions at each stage to clarify gaps, align with cross-functional teams, and help estimate scope and effort.
Design Challenges and Solutions
I partnered with multiple product teams to prioritize new features, refine existing screens, and build a shared component library that aligned with low-code constraints—ensuring consistent, scalable delivery across the platform.
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Uncovering the Need for Flexibility
The original requirements assumed users only needed to edit seven key attributes per loan. However, user testing revealed that any of the 100+ attributes could need to be updated during the audit process—far beyond the initial scope.
I considered the user needs as well as technical constraints when designing a solution. To bridge the gap, I explored multiple solutions.
Reimagining the Edit Experience
Direct Field Editing
Direct field editing within the AG Grid table mimicked Excel’s flexibility, but this proved technically infeasible given the volume of data and lack of field-level locking controls.
Bulk Editing
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AFTER
Scalable Bulk Editing
The final solution introduced a bulk edit feature that streamlined how users updated data across multiple loans. Instead of scrolling through dozens of columns, analysts could search for specific attributes, input changes directly, and apply them to one or many selected loans—whether locked or unlocked. This approach reduced cognitive load, improved efficiency, and maintained data integrity by automatically locking edited loans to prevent overwrites from upstream updates.
Key Takeaways
What began as a simple redesign task quickly evolved into a complex, multi-phase initiative. Through user interviews and workflow analysis, I uncovered critical dependencies and data challenges that hadn’t been reflected in the original requirements.
From ambiguity to clarity
Discovering that analysts needed to edit nearly all loan attributes—and that the lock/unlock functionality added significant technical complexity—expanded the project’s scope. Collaborating closely with the product manager and solution architect, I helped refine ambiguous requirements and break the epic into smaller, manageable features to prevent bottlenecks.
This clarity aligned the team around user priorities and ensured each release addressed real workflow needs, setting a scalable foundation for data accuracy and trust in the Capital Markets Engine.