Application Scoring

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.

Contents
Identification
Borrower’s current ID data and the history of changes made to it.
Scoring
A value from 0 to 1 that determines the probability of late payments of over 100 UAH that are at least 90 days past due within a 6-month period on a credit being considered.
Default probability based on scoring
Model
Target function
Late payment of over 100 UAH at least 90 days past due within a 12-month period
Predictors
Credit history characteristics and partner application data
Method
Gradient boosting
0.79
ROC-AUC
0.49
KS
Application Scoring includes:

Model development;

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.

Working with the scoring

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.

XML
XML format is used to work with scoring.
 
General structure of interaction:
 
Http Method
 
POST
 
Request URL
 
 
Test URL
 
 
Request   
 
 
Response
 

 

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Application sent. Please expect during the day you will be contacted.