credit-scoring

To integrate credit scoring models into a FastAPI backend, you manage the ML model lifecycle, including feature engineering and data pipelines, exposing model inference through API endpoints for loan application workflows.

eKYC integration for a Flutter loan application works by facilitating secure identity verification via VNPT eKYC, allowing users to complete a verified assessment before receiving instant credit limit and loan term calculations.

Yes, you can use Firebase and Firestore for authentication in a Flutter credit scoring app to manage the user journey from initial sign-in through to the final loan offer within the application workflow.

The best way to calculate a credit limit without full verification is using the simulation mode, which provides a quick estimate and instant credit terms by leveraging AI-driven risk assessment without requiring eKYC.

Yes, this credit scoring solution supports GCP deployment, enabling you to host FastAPI backend services, manage ML model integration, and serve the Flutter mobile application infrastructure.

You need feature engineering and data pipelines for an AI-powered loan application to process raw applicant data into structured inputs, ensuring LightGBM and XGBoost models can accurately assess risk and determine credit limits.