Loan applications have to clear a long list of rules — how quickly a decision gets made,
whether the credit score and debt-to-income ratio meet underwriting guidelines, whether the right
price and discounts were applied, and whether the required disclosures went out on time.
Reviewing each one by hand is slow and inconsistent, and as volume grows it gets easier for something to slip through.
This tool automates that review. It checks every application against eighteen built-in controls
and shows the exceptions in a clear, interactive dashboard, so teams can see where problems cluster
and address them consistently. The example here uses lending data, but the same idea of setting the rules once,
then let the tool check every record, works anywhere data needs to be validated against a consistent standard.
PythonDashPandasPlotlyFlask
Problem
A single loan application can touch a dozen or more rules, from decision deadlines and underwriting thresholds to pricing and disclosure timing. Checking each file by hand is slow, and two reviewers can come away with two different answers. The larger the volume, the more likely it is that a missed deadline or a miscalculated disclosure goes unnoticed — exactly the kind of gap that creates audit, compliance, and operational risk.
Approach
I built an automated workflow in Python that runs eighteen specific controls against each application that covers decision timing, underwriting guidelines, pricing and membership discounts, and the disclosures sent at each stage. When an application breaks a rule, the tool records what went wrong and rolls the findings up into a dashboard that breaks the exceptions down by control, severity, and category. Visitors can generate a sample dataset, run the audit, and explore the results themselves.
Outcome
Instead of spot-checking files one at a time, reviewers get a consistent,
repeatable view of where the exceptions lie, which controls trip most often, how serious they tend to be,
and which applications need a closer look. The charts are clickable, so the results can be narrowed down on the fly, and the full exception list can be downloaded as a CSV for follow-up or record-keeping. The same set of checks can be run again and again, making it easy to compare results over time.
Key Capabilities
Eighteen built-in controls: Covers decision timing, underwriting guidelines, pricing and discounts,
and required disclosures, ensuring the review stays complete and consistent.
Interactive dashboard: Summary metrics plus charts that break exceptions down by control, severity, and category, with the option to click a chart and filter the results.
Hands-on demo: Generate a sample dataset and run the audit yourself to see the full workflow from data to dashboard.
Downloadable results: Export the complete exception list to CSV for follow-up, reporting, or record-keeping.
Reusable framework: The same approach — define the rules once, then check every record — can be pointed at any dataset that needs to be validated against a consistent standard.