Credit Risk & Repayment Analytics System – DAQI (Fintech)

Designed and implemented a complete data-driven credit risk and repayment analytics framework for DAQI, a fintech providing invoice-based loans to MSME retailers.

Project scope & achievements:

Sales Funnel Analytics: Built an automated pipeline to track merchant onboarding → credit approval → disbursement → repayment stages, helping identify drop-off points and conversion bottlenecks.

Risk-Based Lending: Developed machine learning credit-scoring models using bureau, GST, and transactional data, improving loan approval accuracy by 15% while lowering defaults.

Repayment & Portfolio Insights: Engineered custom repayment metrics (DPD, bounce-rate, utilization, roll-forward) to flag early-risk customers — raising early-warning detection by 20%.

Data Visualization & Reporting: Created Tableau dashboards for portfolio health, cashflow cycles, and collection trends; enabled real-time tracking for the management team.

Operational Impact: Automated end-to-end analytics, reducing manual underwriting time by 35% and allowing proactive portfolio management.

This system helped DAQI transition from manual, rule-based approvals to a scalable, analytics-first lending platform integrating ML and business intelligence.