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

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:

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
Version history
Publication date:
09-09-2023 09:15 AM
Version history
Last update:
‎09-21-2022 02:29 PM
Updated by: