A thin-file lender drives profitability by improving credit risk model accuracy by 20 GINI points

Within 2 months, credit card approvals increased by 8% while gross credit losses decreased by 5%
Digital lender in the Middle East provisioning credit cards to blue collar workers

Country: United Arab Emirates

Company Size: 500 – 1,000 Employees

Type: Fintech, Retail Lending

Products: Credit Cards

Results after 3 months

<1 week

to develop and deploy new credit models


Increase in Approvals which reversed rejection rates

The Challenge

A digital lender in the Middle East was struggling to reverse high rejection rates and high first month default rates. Credit Bureau information was limited for their target segment of blue-collar workers, resulting in low accuracy credit risk models, which took 6 months to develop and deploy.

The Solution — creditX

  • Ensemble ML algorithms for higher predictive accuracy
  • Full automated solution seamlessly connected to Lender’s data warehouse and LOS system
  • Ability to create credit models for thin-file customers using historical data
  • Implementation completed in 12 weeks

The Results

+20 pts

Increase in GINI accuracy

< 1 week

to develop & deploy new credit models


Increase in approval rates


Reduction in gross credit losses

High Return of Investment

Within 2 months of using creditX, our customer gained an ROI of 17X and increased profitability resulting from the combination of more loans disbursed and lower loss rates.

Higher Operating Efficiency

The end-to-end process of developing and deploying credit models used to take 6 months and involved a team of 8 persons. It is now managed by 2 persons and takes less than a week to complete, allowing the team to focus on other value-adding projects.

“A game-changer”’s solution, creditX, is a game changer in credit modelling that supports our Bank’s digital transformation journey.

– Ti Eng Hui, CEO, Baiduri Bank

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