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

If you've ever tried to build anything in fintech, you know the rabbit hole goes deep. I recently worked on a project that matches users with lenders based on their location and credit profile. Here's what I learned.

The Problem

Most loan comparison sites treat the US as one big market. But lending is hyperlocal. Interest rates, lender availability, and even 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:

Lender availability - Does this lender operate in the user's state?
Credit requirements - Does the user meet minimum thresholds?
Loan terms - APR ranges, amounts, repayment periods

Database Design

The lender 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 50 states. Others only serve 12. A few are single-state only.

The Matching Query

SELECT * FROM lenders

WHERE $1 = ANY(states_available)

AND min_credit_score <= $2

AND min_loan_amount <= $3

AND max_loan_amount >= $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%.