AI-Powered Credit Scoring System
Client Partner: TrustCapital Fintech · Build Timeline: 5 Months
Project Tech Stack
Allocated Engineering Pod
- Data Scientistx1
- Backend Developerx2
"Our credit decisions are now automated, safe, and take seconds."
James Peterson
Chief Risk Officer
1. Context & Business Goal
The Core Challenge: TrustCapital's manual loan reviews took up to 3 days, causing low conversion rates on web portals.
The Agreed Objective: Train machine learning models to assess user risk and approve loans instantly.
2. Technical Research & Discovery
Audited loan history datasets to select safe, unbiased training features. Prior to writing any code, our team compiled reference data frameworks and mapped API endpoints to identify speed limits.
3. System Architecture Design
Layout Scheme: Simple dashboard for applicant details showing credit score breakdowns and risk metrics.
Database Infrastructure Setup: AWS Sagemaker hosting classification models, connecting with Node.js APIs.
4. Production Implementation Lifecycle
Data Preprocessing
Cleaned historical credit datasets, removing bias features.
Model Optimization
Trained Random Forest classifiers, scoring 94% loan approval accuracy.
5. Validated Business Outcomes
- ✓Reduced loan application reviews to under 2 seconds.
- ✓Lowered default rates by 14% using risk classification models.