IME - Building a Credit Prediction Model Using AutoML
mohamed-khamis
Partner, L2 Admin, L2 Designer, Dataiku DSS Core Designer, Dataiku DSS & SQL, Dataiku DSS ML Practitioner, Dataiku DSS Core Concepts, Snowflake Advanced, Dataiku DSS Adv Designer, Registered, Dataiku DSS Developer, Dataiku Frontrunner Awards 2021 Participant, Frontrunner 2022 Participant Posts: 13 Partner
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.
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
Business Area: Accounting/Finance
Use Case Stage: Proof of Concept
Value Generated:
Credit Risk Model with high accuracy: F2-measure = 0.938.
Value Brought by Dataiku:
Built-in Analysis and AutoML tools.
Value Type:
- Reduce cost
- Reduce risk
- Save time