The Challenge

The fintech was operating a loan marketplace with a manual matching process: applications were reviewed by the ops team, assessed against lender criteria in a spreadsheet, and submitted to one or two lenders by email. The process was slow, inconsistent, and couldn't scale. Adding a new lender to the network required weeks of development work. With application volumes growing, the manual approach had become a ceiling on funded volume and a source of significant application leakage.

What We Built

We built the multi-lender loan marketplace from scratch. The routing engine evaluates each application against real-time lender appetite signals—credit score band, requested amount, loan purpose, geography, product type, LTV ratios, and live lender capacity—and routes through configurable waterfall, simultaneous multi-submit, or best-offer comparison modes. All lender integrations are maintained through a single abstraction layer so adding a new provider requires no changes to the core platform.

Key Capabilities Delivered

• Intelligent routing across 40+ lenders with complex, overlapping eligibility criteria
• Waterfall, simultaneous multi-submit, and best-offer modes—all configurable
• Real-time lender capacity and appetite signals factored into routing decisions
• Lender abstraction layer: adding a new provider is a configuration task, not a development project
• Borrower-facing offer comparison with transparent rate and term breakdown
• Full audit trail on every routing decision and lender submission

The Outcome

Funded rate improved significantly after switching from manual matching to automated routing. The time to add a new lender dropped from weeks of development to a configuration task. The platform now operates with 40+ active lender integrations in production—a network size that was operationally impossible with the previous manual approach. Application leakage reduced as each application is matched to the most suitable eligible lender rather than defaulting to the ops team's best guess.