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Krediwall
Open-source fraud firewall for credit-funded UPI payments
An open-source fraud firewall that runs inside a lender's payment switch. Every credit-funded UPI payment gets ALLOW, REVIEW or BLOCK in microseconds, based on limit burn and credit-line age that generic fraud tools never see. Apache-2.0, zero dependencies.
The problem. In India, banks now fund UPI payments from credit lines. UPI is push-&-final: once a payment completes, it can't be recalled. So when a fresh credit line gets drained by a mule ring, the loss lands on the lender's own capital.
Generic fraud tools can't see what makes these attacks obvious: the sanctioned limit, how fast it's being burned, & how old the credit line is. Only the lender holds that.
What Krediwall does. It runs in-process inside the bank's payment switch and returns ALLOW, REVIEW or BLOCK on every credit-funded UPI authorization, in microseconds. It combines lender-only signals with India's DoT fraud-intelligence feed (FRI). It makes no outbound calls & stores identifiers only as keyed-HMAC digests, so nothing leaves the bank's perimeter.
A replay harness, so your fraud team calibrates thresholds on your own labelled traffic
npm install @krediwall/*, zero runtime dependencies
Try it in a minute: npm run demo shows the engine stopping a mule drain step by step.
I'd love feedback from anyone who's worked on a payment switch, a fraud team or a credit product.
What signal would you add? What would stop you from running this in shadow mode next week?
About Krediwall on Product Hunt
“Open-source fraud firewall for credit-funded UPI payments”
Krediwall was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #97 on the daily leaderboard. An open-source fraud firewall that runs inside a lender's payment switch. Every credit-funded UPI payment gets ALLOW, REVIEW or BLOCK in microseconds, based on limit burn and credit-line age that generic fraud tools never see. Apache-2.0, zero dependencies.
On the analytics side, Krediwall competes within Open Source, Fintech, Developer Tools and GitHub — topics that collectively have 680.9k followers on Product Hunt. The dashboard above tracks how Krediwall performed against the three products that launched closest to it on the same day.
Who hunted Krediwall?
Krediwall was hunted by Kapil Pawar. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Krediwall including community comment highlights and product details, visit the product overview.
Kapil Pawar
Hey Hunters 👋 I'm Kapil, & today we're open-sourcing Krediwall.
The problem.
In India, banks now fund UPI payments from credit lines. UPI is push-&-final: once a payment completes, it can't be recalled. So when a fresh credit line gets drained by a mule ring, the loss lands on the lender's own capital.
Generic fraud tools can't see what makes these attacks obvious:
the sanctioned limit, how fast it's being burned, & how old the credit line is. Only the lender holds that.
What's in the box (Apache-2.0):
Rules for 6 patterns: velocity spray, mule drain, first-payee large, structuring, dormant reactivation, odd-hours activity
Every verdict ships with the evidence behind it, ready for a review queue or an FMR filing
HTTP gateway, Prometheus metrics & Grafana dashboards
A replay harness, so your fraud team calibrates thresholds on your own labelled traffic
npm install @krediwall/*, zero runtime dependencies
Try it in a minute:
npm run demo shows the engine stopping a mule drain step by step.
I'd love feedback from anyone who's worked on a payment switch, a fraud team or a credit product.
What signal would you add?
What would stop you from running this in shadow mode next week?