IME - Building a Credit Risk Analytics Model with High Prediction Accuracy
Name: Mohamed AbdElAziz Khamis Omar
Title: Head of Data Science
IME is a key market player in Data Management solutions. To learn more: www.infme.com
Data Science for Good
Credit Risk Analytics Module; Home Equity Mortgage loan fault perdition.
Due to data privacy of the bank, we trained the model on a generic dataset.
The data set HMEQ reports characteristics and delinquency information for 5,960 home equity loans. A home equity loan is a loan where the obligor uses the equity of his or her home as the underlying collateral. The data set has the following characteristics:
BAD: 1 = applicant defaulted on loan or seriously delinquent; 0 = applicant paid loan
LOAN: Amount of the loan request
MORTDUE: Amount due on existing mortgage
VALUE: Value of current property
REASON: DebtCon = debt consolidation; HomeImp = home improvement
JOB: Occupational categories
YOJ: Years at present job
DEROG: Number of major derogatory reports
DELINQ: Number of delinquent credit lines
CLAGE: Age of oldest credit line in months
NINQ: Number of recent credit inquiries
CLNO: Number of credit lines
DEBTINC: Debt-to-income ratio The goal of this use case is to build a model that borrowers can use to help make the best financial decisions.
Using Dataiku AutoML.
Business Area: Accounting/Finance
Use Case Stage: Proof of Concept
Credit Risk Analytics Model with high prediction accuracy; AUC ROC = 0.974.