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Fintech / Specialty Lending

A Specialty-Finance Lender

Manual risk review. No ML. Reports took 30 minutes to run.

The Problem

This specialty-finance lender's risk team was manually reviewing every MCA loan application. Reports took 30 minutes to generate. There was no scoring system — decisions relied on analyst judgment with no data backstop.

Every loan was a fresh start. No historical benchmarking. No automated flags. The team was spending most of their time gathering data instead of making decisions.

What I Built

  • →ML risk scoring pipeline (logistic regression + random forest) with prior relationship decay, fuzzy name normalization, and Bayesian priors
  • →Reduced BigQuery API calls from 840 to 6 per pipeline run
  • →Automated QBO + HubSpot data sync with reconciliation layer
  • →Partner performance views with TVT baseline comparisons

The Outcome

  • ✓Report runtime: 30 minutes → 26 seconds
  • ✓745 automated tests covering the full scoring pipeline
  • ✓Risk team now reviews flags, not data

Tech Stack

PythonBigQueryGCP Cloud RunHubSpot APIQuickBooks API

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