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Case Study 1

Reimagining Financial Reconciliation

Automating credit card transaction matching for legal practice management via Plaid integration

Role Lead Product Designer
Team PM, 5 Engineers, Accounting SMEs
Platform PracticePanther - Enterprise SaaS
Impact 40% Increase in User Confidence
Plaid Integration Reconciliation Dashboard
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01

The Landscape

PracticePanther & Trust Accounting

PracticePanther is a legal practice management platform serving 50,000+ legal professionals across billing, payments, and trust accounting. I led the design of the financial reconciliation system, partnering with 1 PM and 5 engineers to bring clarity to complex accounting workflows.

This project accelerated adoption of our accounting module by giving firms the confidence to trust our automation and led to a 40% increase in user confidence.

Team

Lead Product Designer 1
Product Manager 1
Engineers 5
Accounting SMEs 2

My Responsibilities

User Research Interaction Design Prototyping Usability Testing System Design
02

The Challenge: Manual Reconciliation in a Compliance-Heavy Domain

Manual reconciliation is more than just a slow process - it's a major risk for law firms. Without automation, accountants spend hours every week fixing messy data entry errors and mismatched records from CSV imports. One small human oversight can lead to a serious audit flag.

The Typical Manual Journey

🏦
Login & Export

Log in to banks and download CSVs

🔍
Manual Cross-Referencing

Compare bank statements with internal records for matches

📝
Categorization & Data Entry

Enter expenses and payments into the system manually

The Problem

Error-Prone Workflows at Scale

Law firms were reconciling bank statements via CSV uploads and Excel - a process that was slow, error-prone, and risked compliance audits.

Manual CSV imports create data entry errors and duplicate entries
Weekly reconciliation batches take hours of accountant time
Bar Association requirements mandate audit trails - errors risk compliance flags
No automated matching - every transaction verified manually
Strategic Business Context

Unlocking Growth with Panther Accounting Plus

Premium Tier Expansion - Core feature for Enterprise-tier subscriptions
Market Alignment - Closing the feature gap with industry leaders to prevent churn
Secure Accuracy - Replace manual error-prone workflows with real-time data

Competitors already offered this:

Clio MyCase

"How might we design a seamless matching experience for credit card transactions that empowers billing teams to maintain effortless trust accounting compliance?"

- The core design question
03

Discovery & Strategic Insights

I kicked off discovery by partnering with my PM to conduct 12 user interviews and shadowing billing teams during live reconciliation sessions. This allowed me to map real-world flows and understand where the friction points were. I paired these insights with a competitive audit to identify where current market solutions were failing.

0 Law firm interviews across varying firm sizes
0 Research methods: interviews, workflow shadowing, competitive audit

Key Findings

Observation

Users reconcile weekly or monthly in batches

Design bulk workflows, not one-at-a-time processing

Observation

Fear of errors outweighs need for speed

UI must act as a safety net - every number accurate to the cent

Observation

CSV uploads confuse mental models

Design a guided matching UI that also serves as a CSV fallback

Observation

Firms manage multiple credit card accounts

Provide a clear way to distinguish between different cards so users don't get confused

Product Goals

Reduce Manual Bookkeeping Time

Automate transaction imports and matching to save accountants hours of work each week

Ensure Accounting Accuracy

Strict compliance with legal trust-accounting rules - zero tolerance for errors

Increase Balance Trust

Users must trust that displayed balances reflect reality through verification states

Compete with Market Leaders

Position PracticePanther to rival Clio and MyCase in trust accounting

04

Solution Architecture: 3-Phase Strategy

Instead of designing a simple "Import Transactions" feature, I reframed the experience into three logical phases to reduce cognitive load and build user trust progressively.

Phase 1

Connect

Link firm credit card accounts via Plaid. Import transactions with a secure OAuth flow. Auto-sync daily to keep data fresh.

Phase 2

Match

Automated transaction matching with suggested matches. Categorize as contact payment, firm payment, or expense. Exclude irrelevant entries.

Phase 3

Verify

Human-verified reconciliation. Final review ensures audit-ready compliance. Every cent accounted for to meet Bar Association standards.

User Flow

Organizing the Chaos

Ensuring every cent is accounted for to meet Bar Association audit standards.

1 Connect
Connect Credit Card Link firm accounts
Import Transactions Auto-sync daily
2 Match
AI

Automated Transaction Matching

Triage

Match to existing payment

Categorize as new payment

Exclude transaction

Classify
Contact Payment
Firm Payment
Contact Expense
3 Verify

Human Verified

Final review ensures audit-ready reconciliation

05

Phase 1: Frictionless Bank Integration with Plaid

Leveraging Plaid OAuth to ensure secure, real-time data sync while maintaining the user's accounting context. The goal wasn't just to connect a bank, but to make the user feel safe while doing it.

Connecting PracticePanther to Plaid loading screen

Connecting PracticePanther to Plaid

Plaid Link flow showing consent, phone verification, institution selection, and bank login

Plaid Link flow - consent to bank selection

Chase OAuth redirect for secure bank authentication

Chase OAuth redirect - secure bank login

06

Phase 2: Design Iterations - Matching

Refining the matching logic through 2 rounds of cross-functional validation. The matching UI went through two major iterations before arriving at the final solution, each shaped by user testing and engineering feedback.

1

High Density Table View

Tested & Pivoted

Approach

I intentionally mirrored the grid-based approach used by our primary competitors to see if an industry-standard mental model would reduce the learning curve. A full-width data table with expandable rows revealed four action tabs: Review Match, Manual Match, Create New Payment, and Exclude.

Iteration 1: Table with inline Review Match tab

Review Match tab

Iteration 1: Manual match with inline grid search

Inline grid matching

Iteration 1: Exclude tab with reason field

Exclude with reason

2

Modal-Based Matching

Tested & Pivoted

Approach

To solve the clutter problem of the grid, I pulled matching into focused modals with tabs: Suggested Match, Find Match, Create Match, and Exclude. We introduced smarter auto-matching that instantly flagged exact matches. Each workflow got dedicated space and clearer information hierarchy.

Iteration 2: Suggested Match modal with transaction details and ranked matches

Suggested Match modal

Iteration 2: Find Match tab with multi-filter search

Find Match with filters

Iteration 2: Create Match with Contact Payment, Expense, and Firm Payment categories

Create new match

Iteration 2: Exclude transaction with required reason field

Exclude transaction

The Pivot: From Modals to Split-Panel Drag-and-Drop

Users consistently preferred seeing both sides - imported transactions vs PracticePanther payments - all in one view. I pivoted to a single-screen split-panel design with drag-and-drop matching, satisfying the user's need for a holistic view while streamlining the technical architecture.

Before

Modal-heavy, tab switching, context loss

After

Single screen, split panel, drag-and-drop matching

07

Final Solution: Split-Panel Reconciliation

The final design is a drag-and-drop reconciliation interface where users visually match bank transactions to PracticePanther payments on a single screen.

Final Solution: Split-panel reconciliation with Bank Transactions (left) and PracticePanther Payments (right)

Split-panel reconciliation - Bank Transactions (left) vs PracticePanther Payments (right)

Drag-and-Drop Matching

Users drag bank transactions from the left panel onto PracticePanther payments on the right. A green border and "Valid Match" indicator provides clear visual confirmation before committing.

Drag interaction showing Contact Payment, Firm Payment, and Expense classification options

Drag interaction with payment type classification

Matched transactions with hover options for payment creation

Hover reveals payment creation options

Payment Creation Options on Hover

Hovering on a transaction card reveals options to define it as a contact expense, contact payment, or firm payment. This keeps the interface clean while providing quick access to classification actions.

Drag-to-scroll interaction with Hold to Scroll Down tooltip for long payment lists

Drag-to-scroll for navigating long payment lists

Recent Matches & Undo

A "Recent Matches" dropdown shows the last several matches with a one-click "Unmatch" option - directly addressing user feedback about needing to undo wrong matches and fear of irreversible errors.

Recent Matches panel with Unmatch buttons

Recent Matches panel

Firm Payment creation form with Deposit, Withdrawal, and Transfer tabs

Firm Payment creation - Deposit, Withdrawal, Transfer

User Feedback
Users found the drag-and-drop interaction intuitive and felt it added "delight" to their daily workflow.
Low learning curve by leveraging familiar payment patterns
Excitement for using this layout to handle complex bulk matching in the future
Development Feedback
Highly achievable by utilizing existing component libraries for drag-and-drop logic
Maintains system consistency by reusing established payment and expense modules
Much more manageable scope - avoided performance risks of previous iterations
08

Phase 3: The Final Audit

After matching, users are funneled to the core reconciliation dashboard - a familiar, high-trust environment. This final step allows for a seamless review and verification of all processed payments, ensuring 100% data integrity before completing the workflow.

09

Outcome & Key Learnings

Impact

Moving from CSV to Plaid was more than a technology upgrade - it fundamentally changed how law firms manage trust accounting.

Enterprise Adoption Unblocked

Positioned PracticePanther to compete with Clio and MyCase in trust accounting

Manual Work Eliminated

Replaced CSV reconciliation with automated Plaid-powered bank feeds and intelligent matching

Revenue Driver Launched

Foundational feature for Panther Accounting Plus, driving enterprise subscriptions

40%
Increase in user confidence compared to earlier prototypes, successfully mitigating the "Fear of Errors" associated with manual bank reconciliation.

Key Design Learnings

01

Trust over Speed in Financial Tools

Users need verification states, confirmation steps, and undo capabilities before trusting a system with financial data. Prioritize correctness over efficiency.

02

Single-Screen Mental Model

Accountants want both data sources visible simultaneously. Split-panel matched how they already work - modal and tab-based approaches created too much context switching.

03

Iterative Pivots Build Better Products

Two full iterations revealed the initial approach was wrong. The drag-and-drop pivot was only possible because we tested early and listened to both users and engineers.

04

Domain Expert Collaboration is Non-Negotiable

Aligning with accounting SMEs was critical for strict legal trust-accounting compliance rules. Without their input, we'd have missed compliance-critical details.

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