IME - Building a Credit Risk Analytics Model with High Prediction Accuracy

mohamed-khamis

Name: Mohamed AbdElAziz Khamis Omar

Title: Head of Data Science

Country: Egypt

Organization: IME

IME is a key market player in Data Management solutions. To learn more: www.infme.com

Awards Categories:

• Data Science for Good
• Moonshot Pioneer(s)

Business Challenge:

• 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.

Business Solution:

Using Dataiku AutoML.

Business Area: Accounting/Finance

Use Case Stage: Proof of Concept

Value Generated:

Credit Risk Analytics Model with high prediction accuracy; AUC ROC = 0.974.

Value Brought by Dataiku:

Built-in Analysis and AutoML tools.

Value Type:

• Reduce cost
• Reduce risk
• Save time