Your App Knows Their Balances. Now Show Them Their Moves.
A PFM app holds the user's side of every ledger — loan rates, balances, idle cash. RateAPI adds the live market side from 3,700+ credit unions, and every balance becomes a recommendation: refinance and save $78/month, fund the car at 5.99%, move the cash earning nothing.
How do personal finance apps turn rate data into recommendations?
The recommendation is the delta between what the user has and what the market offers. Your app already knows the current APR and balance. One call to RateAPI returns the live best offers for that exact scenario — state, product, amount, term — and the difference is the advice: monthly savings on a refinance, the true financing cost of a planned purchase, or the yield gap on idle savings. Monitors make it proactive: your app speaks up when acting is actually worth it.
- Refi radar: user's rate vs live market, with computed monthly and total savings.
- Purchase planning: price the car or home a user is saving for with today's real financing, not a guess.
- Deposit side too: savings, money market, and CD APYs for the cash your app can see sitting still.
Related: auto refinancing alerts, refi break-even math, and the data methodology.
Dashboards Tell Users What They Have. Users Stay for What To Do.
Every PFM app can render the pie chart. Retention comes from moments where the app said something the user acted on and won: "refinance now, save $2,800." Those moments need the market side of the equation — live, state-specific, product-specific rates — and that data has been scattered across thousands of lender websites that don't want to be scraped.
Without it, apps fall back on national averages (wrong for the user's state and scenario) or affiliate feeds (recommendations shaped by who pays, which users can smell).
RateAPI is the market side, unshaped. Live offers from 3,700+ credit unions, ranked by true cost, with a source link on every number and no affiliate thumb on the scale — so your recommendations read as advice, because they are.
The User's Loan In, the Recommendation Out
// Your app knows the user's loan: 7.5% APR, $28,000 left, TXPOST https://api.rateapi.dev/v1/decisions { "decision_type": "financing", "context": { "geo": { "state": "TX" } }, "product_request": { "product_type": "auto_loan", "intent": "refinance", "amount": 28000, "term_months": 48 }}{ "summary": { "recommended_action": "financing_available", "best_apr": 5.99, "estimated_monthly_payment": 649, "total_providers_analyzed": 847 }, "actions": [{ "offers": [ { "rank": 1, "credit_union_name": "Example Credit Union", "apr": 5.99, "term_months": 48, "monthly_payment": 649 } // vs the user's 7.5% — your app computes and shows the delta ] }]}Make it proactive with monitors
One monitor per user scenario. Your webhook fires when the market crosses the threshold — with the best offer and computed savings in the payload.
// Make it proactive: alert when refinancing clears a thresholdPOST /v1/monitors{ "name": "User 12345 auto refi alert", "decision_context": { "state": "TX", "intent": "refinance", "amount": 28000, "product_type": "auto_loan" }, "conditions": [{ "field": "monthly_payment", "operator": "change_pct", "value": -10 }], "webhook_url": "https://yourapp.com/webhooks/rates"}Built for Recommendation Engines
Refinance Scenarios
Auto and mortgage refinance intent is priced separately from purchase — the comparison your user's current loan actually deserves.
Monitors & Webhooks
Threshold-based alerts with signed webhooks. Your app messages the user when savings clear a bar you set — not on a schedule.
Purchase Planning
Price a planned car or home purchase with live financing — turn a savings goal into a funded plan with a real monthly number.
Savings & CD Yields
The deposit-side recommendation: show what idle cash could earn across the same 3,700+ institutions.
Evidence Per Number
Every rate carries a timestamp and the source page it was read from. Your recommendation UI can show its work.
No Affiliate Skew
Zero affiliate revenue, no lead selling, rankings by true cost. Your users' trust in the recommendation stays yours.
Frequently Asked Questions
Common questions from personal finance app teams
How do personal finance apps use rate data to make recommendations?
A PFM app already holds the user's side of every ledger: loan balances, current APRs, idle cash. What it usually lacks is the market side. RateAPI supplies live, ranked offers for the same product, state, and amount — so "your auto loan is at 7.5%" becomes "the market is at 5.99% for your scenario; refinancing saves about $78/month." The recommendation is just the delta, and the delta is now computable.
How do refinance alerts work?
Create a monitor with the user's scenario (state, product, amount, refinance intent) and a condition such as "monthly payment drops 10% below today's market." When the market crosses it, your webhook receives the best offer with computed monthly and total savings. Users hear from your app exactly when acting is worth it — not on a weekly digest schedule.
Can we help users plan a future purchase, not just refinance?
Yes. The same decisions endpoint prices a planned purchase: a user saving for a $35,000 car or a $250,000 home gets today's real financing cost — best APR and exact monthly payment — so your app can turn "saving for a car" into a funded plan with a real number, and re-price it as the market moves.
Does this cover savings yields too?
Yes. RateAPI tracks savings, money market, and CD APYs across the same 3,700+ credit unions. An app that sees idle cash earning 0.01% can show what the market actually pays and where — the deposit-side version of the refi recommendation.
Do you sell our users as leads?
Never. No affiliate revenue, no lead selling, no paid placement — rankings are by true cost only. Your users' trust in your recommendations is the product; we don't have an incentive that competes with it.
What does integration look like?
One POST to /v1/decisions per recommendation, or a monitor per user scenario for proactive alerts. The free tier (20 requests/month, instant key, no sales call) is enough to prototype the recommendation flow; paid tiers cover production volumes.
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