Protect policy changes from conflicting edits.
The policy editor checks the latest saved state before applying a change, preserves unrelated edits and keeps a change history. An adjustment is a recorded action, not an invisible replacement.
Financial services · Lending
A clearer view of lending decisions.
Machine-learning loan scoring delivering $24,000 in annual savings, with a five-view lending dashboard.
The live HAL overview brings scoring activity and decision reporting together. Private operating figures are redacted; no settings were changed.
The context
A problem worth solving.
Loan scoring needs to fit into an existing application process, while the team needs visibility into scoring activity and loan performance.
My contribution
What I brought to the work.
Built a machine-learning scoring service integrated with the lender’s application system, plus a dashboard for reviewing scoring activity, approval thresholds, lead sources and loan performance.
The product
What it makes possible.
01Machine-learning API · $24K annual savings+
Machine-learning scores returned inside the existing loan application workflow, delivering $24,000 in annual savings.
02Overview · see the activity+
Review scoring activity and decision patterns in one place.
03Thresholds · manage policy+
Review and adjust approval thresholds, with a recorded history of changes.
04Lead Sources · compare channels+
Explore where applications come from and how each source performs.
05Funded History · follow performance+
Review funded-loan history with clear reporting periods and funding definitions.
06Scenario analysis · explore tradeoffs+
Explore modeled exclusions alongside observed outcomes. Scenarios support investigation; they do not prove the effect of a policy change.
At a glance / simplified product view
- 01Loan application
- 02Machine-learning scoring
- 03Decision & reporting
Inside the software
See the actual workspace.
Engineering choices
The decisions behind the interface.
Separate observation from simulation.
The analysis distinguishes observed funded outcomes from modeled exclusions. A scenario helps explore a decision; it is not presented as proof that a policy change caused an outcome.
These are documented implementation choices. Private policy settings, borrower information and model details are intentionally omitted.
Where it stands
The work keeps moving.
Scoring activity updates independently from imported loan-history data. Next steps focus on repeatable data refreshes and more precise links between policy changes and affected loans.