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Machine Learning in Credit Scoring

Description:

Implement supervised machine learning techniques in order to further understanding the process in which a client will be granted a credit and be denied a credit. This process is denoted as credit scoring, it is a wide methodology used by banks which assigns each prospect client a score from 300 to 850, being 850 the highest score a client can receive. Credit scoring is used to evaluate the potential risk that granting a client a credit poses on credit lenders. A credit score is based on an individual' credit report, which considers both numerical and categorical variables, such as the status of the existing credit account, the credit amount, number of existing credits at the bank, among others. Ultimately, credit lenders use such score to determine which clients will be granted credit loan under a predetermined interest rate and credit limit.

Members:

Isabella Arango Restrepo Sophia Catalina Giraldo Castrillón Valentina Yusty Mosquera
Isabella Arango Restrepo Sophia Catalina Giraldo Castrillón Valentina Yusty Mosquera
iarangor1@eafit.edu.co sgiral55@eafit.edu.co vyustym@eafit.edu.co