Assessment of the probability of default of a legal entity, depending on the availability and content of the data from public sources and the credit history.
This decision is particularly useful for assessing the risk of issuing a credit to borrowers whose credit history is either uninformative or absent.
API development and implementation;
10,000 free reports for testing;
Monthly monitoring of the quality of model distribution;
Model calibration during one year after implementation, in case of distribution changes.
To build an individual scoring, the following is required:
Sample for model training. The required number of records is 10,000 or more;
Sample markup into “good” and “bad”;
Description of the business process for scoring application;
Description of data for model training.
Details and terms of development are negotiated individually.