A 0→1 product replacing a brittle Excel workflow with a purpose-built review tool — reducing decision time and giving QuickBooks Capital a data foundation for auto-decisioning.
2 minute overview of the final product and key interactions
All critical data points — FICO, cash balance, EBITDA, negative events, and analyst notes — visible on a single screen. The 10-second gut check that used to take 15 minutes across 17 tabs.
12 months of QBO-connected financial data — bank balances, receivables, inventory, payables, and net worth — auto-populated and sortable without manual data entry.
Auto-generated P&L with expandable line items. Revenue, COGS, operating expenses, and EBITDA flow down to summary margin cards at the bottom.
Full credit picture at a glance — 8 tradelines, utilization bars, payment status badges, and 12-month payment history heatmaps for instant pattern recognition.
Credit card transactions cross-referenced against QBO records to catch discrepancies and verify spending patterns match reported income.
Transactions grouped by payee — Square deposits, Stripe transfers, supplier payments, payroll — with monthly breakdowns across the full review period.
Current month summary with total credits, debits, net change, and transaction count. Each transaction auto-tagged and compared to prior month trends.
Revenue, Expense, and Fee tags color-coded for rapid scanning. Adjust toggles let underwriters include or exclude transactions from the final health calculation.
DDA-derived data vs QBO data side by side — gross revenue, adjustments, net totals, and monthly averages. The source of truth for the lending decision.
Full audit trail — who excluded a transaction, when, why, and the exact dollar impact. 4 adjustments, -$7,250 net impact, all traceable for compliance.
Interactive walkthrough of the full financial review flow
Lending grew 2x compared to pre-pandemic levels in FY22, but 60%+ of loan applications still required manual review. The underwriting team was working overtime — including weekends and holidays — just to stay within their 2-day SLA. With originations expected to double again, the bottleneck was about to become a wall.
The average time for an underwriter to pick up an application ranged between 2–8 days. Time to Decision for manually reviewed apps averaged 8.9 days. Customers were waiting too long and submitting documents repeatedly.
I was the sole designer embedded on this initiative, partnering with a PM, engineering lead, and cross-functional stakeholders across underwriting, policy, and compliance.
Our team spent a month deep-diving into the underwriter experience. We performed follow-me-home interviews, reviewed existing process documentation, and mapped the end-to-end underwriting journey. We identified the Financial Review — where analysts manually review 12 months of bank transaction data — as the most manual, time-consuming, and high-impact area to improve.
~30% of all applications require an underwriter to review 12 months of bank data. This process takes anywhere from a few minutes to an hour. The average: 20 minutes per review.
The existing tool was an Excel spreadsheet the team called "the Checklist" — over 1,000 data points across 17 tabs. Through 4 stakeholder discussions, a card sorting exercise, and 3 prototyping sessions with underwriting, policy, and compliance, we distilled those 1,000+ data points down to fewer than 100.
We gave 6 underwriters a deck of 94 cards — each representing a data point from the existing Checklist — and asked them to group them by how they actually use them during a review. The clusters that emerged directly shaped the tool's information architecture.
We mapped every step of the manual review process — from opening the application to submitting the decision — to identify pain points, redundant steps, and opportunities to surface the right data at the right time.
Interactive prototype — scroll and zoom to explore the full journey map
Before jumping into design, we established principles to guide priorities and tradeoffs throughout the project.
The first concept presented all transaction data in a flat table — mirroring the Excel mental model. After testing with three underwriters, we learned they scan by category first, not chronologically. V2 introduced collapsible expense groups with summary cards, which matched their actual workflow.
Before diving into transaction data, underwriters need a quick signal: is this applicant generally healthy or are there red flags? The dashboard surfaces FICO, risk level, cash balance trends, EBITDA pass/fail, and negative events — all above the fold. If something looks off, they know exactly where to investigate next.
In Excel, analysts had to build pivot tables and conditional formatting to investigate bank data. Here, transactions are automatically grouped by payee. Click to expand and see every individual transaction underneath. Sortable columns and search make it easy to find specific activity across months of statements.
In the old workflow, underwriters manually created formulas and highlighted cells to adjust calculations. Now every transaction has a category tag, an include/exclude toggle, and automatic recalculation. Missing a transaction? Add it through the modal. Every adjustment is logged.
Revenue, COGS, operating expenses, and other expenses roll up into Gross Profit, EBITDA, and Net Income rows. Expandable sections let analysts drill into line items like payroll, rent, and marketing. Summary cards below give a quick read on averages and margins.
Utilization bars with color gradients make it immediately obvious which accounts are over-leveraged. Payment history heatmaps show 12 months of on-time vs. late payments. Type badges (Credit Card, LOC, Auto, Term) help analysts quickly scan the credit landscape.
Previously, underwriting decisions and adjustments lived in individual Excel files with no audit trail. Now every exclusion, addition, and re-categorization is captured with who, what, when, why, and the net impact on calculations. This was a key compliance requirement and also enables the data modeling that powers future auto-decisioning.
This is a working prototype of the iBOSS financial review tool — populated with sample data. Scroll through tabs, expand transaction groups, and see how the audit trail captures every adjustment.
Interactive prototype — click through tabs and expand sections to explore
The team was averaging 8.9 days to decision, missing a 2-day SLA, and working weekends to keep up. Here's what changed after iBOSS rolled out to the pilot group.