Credit applications nowadays are typically assessed by automated systems and often approved or rejected within seconds, without human intervention. This loan scenario simulates such an automated loan decision pipeline in order to explore potential questions one may ask about the pipeline and its decisions.
In this scenario, a credit institution employs a loan application assessment process that relies on the risk factor of the loan application, which is calculated by a machine learning model. The model was trained from historic loan performance data and takes into account a variety of data:
In this demonstrator, a loan dataset was used to build the decision pipeline that provides recommendations on whether to approve or reject a loan application based on the characteristics of the borrower and the loan itself.
You can play the role of a customer applying for a loan by following the following steps:
|501501||$5000.00||36 months||credit_card||21 hours, 54 minutes ago|
|641794||$3600.00||36 months||credit_card||1 day, 6 hours ago|
|619309||$1600.00||36 months||home_improvement||2 days, 9 hours ago|
|518084||$6000.00||36 months||home_improvement||2 days, 9 hours ago|
|2130700||$9600.00||36 months||credit_card||1 week, 2 days ago|