The challenge
The client offers instant loans priced off an applicant’s CIBIL score, and demand was outrunning the way decisions were made. Every application was assessed by hand, which made approvals slow, inconsistent from one reviewer to the next, and prone to error — exactly the friction a product promising instant credit can’t afford.
They needed underwriting that could run at volume without losing accuracy: instant eligibility checks, a defensible read on creditworthiness, automatic sizing of the approved amount, and decisions that stayed on the right side of lending regulation.
Our approach
We built a lending platform on a Node.js backend with a custom AI model at its core. Rather than lean on a third-party engine, we trained the scoring logic ourselves so the client keeps full control of how applicants are evaluated — the model weighs CIBIL score, repayment history and income patterns to reach a decision and set a loan amount with no human in the loop.
The result collapsed underwriting from days to seconds while sharpening accuracy on risk and eligibility. Because the backend was engineered for scale, the platform absorbs rising application volume without slowing down, and its risk assessment is framed around the relevant financial regulations.
