“ Credit is a system whereby a person who can’t pay, gets another person who can’t pay, to guarantee that he can pay”
Platform with an advanced and innovative approach for rapid credit decisions to accept the risk of lending individuals and to target the lowest default rate. End-to-end calibratable product visualizes data, highlight major drivers and tag probable defaulters.
Diverse data ingestion compatibility
Detailed historical data exploration
Performance-oriented data transformation
Automated hyperparameter optimization
Multiple ML models validation and selection
NPA calibratable solution
Banks and P2P lending platform can integrate this ready-to-use product to underwrite and examine credit risk for existing as well as target applicants.
The similar approach can be implemented with the different set of attributes and feature set. It will assist in determining potential risks that could cause a loss for the insurer.
The product can be calibrated accordingly to assess borrower’s risk by evaluating the capacity, credit, and collateral of the customer.
- Real-Time Response
- Easy upload/get results architecture
- Multi-model performance evaluation
- Comprehensive visualization
- Easily customizable at different levels
- Solution available on the cloud or on-premise deployment modes
How computational underwriting is better than traditional underwriting?
Computational credit underwriting solely depends on historical data and recognizing patterns and is easy to implement and deploy. It is way more fast to process and accurate compared to conventional underwriting approaches.
Can this product allow to switch between the different set of models for prediction?
Yes, the deployer can select a single or a group of models to evaluate and validate results. Even if required, the lending platform can avoid using complex models like the neural network and can continue with tree-based models for better explainability.