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IME - Building a Credit Prediction Model Using AutoML

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 prediction Model.
  • The bank has constraints on data privacy, so we trained our model on public German data.

 

Business Solution:

Using Dataiku AutoML.

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Adopted Features:

  • Attribute 1: (qualitative) Status of existing checking account
  • Attribute 2: (numerical) Duration in month
  • Attribute 3: (qualitative) Credit history
    • A30: no credits taken/all credits paid back duly
    • A31: all credits at this bank paid back duly
    • A32: existing credits paid back duly till now
    • A33: delay in paying off in the past
    • A34: critical account/other credits existing (not at this bank)
  • Attribute 4: (qualitative) Purpose
    • A40: car (new)
    • A41: car (used)
    • A42: furniture/equipment
    • A43: radio/television
    • A44: domestic appliances
    • A45: repairs
    • A46: education
    • A47: (vacation - does not exist?)
    • A48: retraining
    • A49: business
    • A410: others
  • Attribute 5: (numerical) Credit amount
  • Attribute 6: (qualitative) Savings account/bonds
    • A61: ... < 100 DM
    • A62: 100 <= ... < 500 DM
    • A63: 500 <= ... < 1000 DM
    • A64: .. >= 1000 DM
    • A65: unknown/ no savings account
  • Attribute 7: (qualitative) Present employment since
    • A71: unemployed
    • A72: ... < 1 year
    • A73: 1 <= ... < 4 years
    • A74: 4 <= ... < 7 years
    • A75: .. >= 7 years
  • Attribute 8: (numerical) Installment rate in percentage of disposable income
  • Attribute 9: (qualitative) Personal status and sex
    • A91: male: divorced/separated
    • A92: female: divorced/separated/married
    • A93: male: single
    • A94: male: married/widowed
    • A95: female: single
  • Attribute 10: (qualitative) Other debtors / guarantors
    • A101: none
    • A102: co-applicant
    • A103: guarantor
  • Attribute 11: (numerical) Present residence since
  • Attribute 12: (qualitative) Property
    • A121: real estate
    • A122: if not A121: building society savings agreement/life insurance
    • A123: if not A121/A122: car or other, not in attribute 6
    • A124: unknown / no property
  • Attribute 13: (numerical) Age in years
  • Attribute 14: (qualitative) Other installment plans
    • A141: bank
    • A142: stores
    • A143: none
  • Attribute 15: (qualitative) Housing
    • A151: rent
    • A152: own
    • A153: for free
  • Attribute 16: (numerical) Number of existing credits at this bank
  • Attribute 17: (qualitative) Job
    • A171: unemployed/ unskilled - non-resident
    • A172: unskilled - resident
    • A173: skilled employee / official
    • A174: management/ self-employed/ highly qualified employee/ officer
  • Attribute 18: (numerical) Number of people being liable to provide maintenance for
  • Attribute 19: (qualitative) Telephone
    • A191: none
    • A192: yes, registered under the customer’s name
  • Attribute 20: (qualitative) foreign worker
    • A201: yes
    • A202: no

Custom Metrics

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Business Area: Accounting/Finance

Use Case Stage: Proof of Concept

 

Value Generated:

Credit Risk Model with high accuracy: F2-measure = 0.938.

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Value Brought by Dataiku:

Built-in Analysis and AutoML tools.

Value Type:

  • Reduce cost
  • Reduce risk
  • Save time
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Version history
Last update:
2 weeks ago
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