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Intelligent Credit Assessor - CREsselle

Machine Learning Model

Machine Learning Model

Published: October 2nd, 2018

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Intelligent Credit Assessor - CREsselle

Machine Learning Model


Pre-trained machine learning model to effectively assess credit grant requests that can be incorporated to automation.



Helping banks and other financial institutions to streamline their credit assessment processes making it more efficient, intelligent and future-proofed.
It can also help  individuals  to maximize their approval odds.
Credit Risk models play a key role in the assessment of two main risk drivers.
1) Willingness to pay and 2) Ability to pay.
These two fundamental drivers need to be determined at the point of each application to allow the credit grantor to make a calculated decision based on repayment odds, which in turn determines if an applicant should get a loan, and if so - what the size, price and tenure of the offer will be.
There are two types of risk models in general: New business risk, which would be used to assess the risk of application(s) associated with the first loan that he/she applies. The second is a repeat or behaviour risk model, in which case the customer has been a client and applies for a repeat loan. In the latter case - we will have additional performance on how he/she repaid their prior loans which we can incorporate into our risk model.


Version 2018.2 


The project was built using the following technologies.
Visual Studio with .NET Framework 4.6.1
Azure Machine Learning Studio - Multi-class neural Network