Building a Location-Based Loan Comparison Tool: Lessons from the Fintech Trenches

Source: Dev.to

The Problem

Most loan comparison sites treat the US as one big market, but lending is hyper‑local. Interest rates, lender availability, and regulations vary wildly by state and city.

• Someone searching for “bad credit loans Atlanta” has completely different options than someone in rural Montana.
• Georgia has specific payday‑lending laws.
• Local credit unions serve specific ZIP codes.
• Online lenders have state‑by‑state licensing.

Ignoring location means showing users options they can’t actually use.

The Tech Stack

For the MVP, I kept it simple:

// Basic structure
const userProfile = {
  creditScore: 620,
  location: {
    city: 'Atlanta',
    state: 'GA',
    zip: '30301'
  },
  loanAmount: 5000,
  purpose: 'debt_consolidation'
};

The matching algorithm weights three things:

1. Lender availability – Does this lender operate in the user’s state?
2. Credit requirements – Does the user meet minimum thresholds?
3. Loan terms – APR ranges, amounts, repayment periods

Database Design

The lenders table got complicated fast:

CREATE TABLE lenders (
  id SERIAL PRIMARY KEY,
  name VARCHAR(255),
  min_credit_score INT,
  max_credit_score INT,
  states_available TEXT[], -- Array of state codes
  min_loan_amount DECIMAL,
  max_loan_amount DECIMAL,
  apr_min DECIMAL,
  apr_max DECIMAL
);

The states_available array was key. Some lenders operate in all 50 states, others only in 12, and a few are single‑state only.

The Matching Query

SELECT * FROM lenders
WHERE $1 = ANY(states_available)
  AND min_credit_score = $3
ORDER BY apr_min ASC;

Simple, but effective. Users see only what they actually qualify for.

Lessons Learned

• Compliance is everything. Every state has different disclosure requirements; California alone has multiple regulatory bodies overseeing different loan types.
• Data freshness matters. Lender terms change weekly. I built a scraper to monitor partner pages, but manual verification still catches things automation misses.
• Mobile‑first isn’t optional. 73 % of our traffic comes from phones. People search for loans during lunch breaks, not at desktop computers.
• Trust signals convert. Adding real user reviews and BBB ratings increased click‑through by 40 %.

What’s Next

Currently exploring:

• ML model to predict approval likelihood
• Real‑time rate API integrations
• Personalized recommendations based on similar user profiles

The fintech space moves fast, but the fundamentals stay the same: help users find what they actually need, where they actually are.