Credit Underwriting

“ Credit is a system whereby a person who can’t pay, gets another person who can’t pay, to guarantee that he can pay”

Overview

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.

Product Features

Diverse data ingestion compatibility

Detailed historical data exploration

Performance-oriented data transformation

Automated hyperparameter optimization

Multiple ML models validation and selection

NPA calibratable solution

Lending Platforms

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.

Insurance Sector

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.

Mortgage Underwriting

The product can be calibrated accordingly to assess borrower’s risk by evaluating the capacity, credit, and collateral of the customer.

Why Us?

  • 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

FAQs

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.