Fintech Lender overcomes high rejection rates for female borrowers with AI-Powered credit scoring

Higher accuracy credit scoring boosted approvals by 15% and reduced losses by 10%
Digital lender in the Middle East provisioning personal loans

Country: United Arab Emirates

Company Size: 500 – 1,000 Employees

Type: Fintech, Retail Lending

Products: Personal Loans

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 experiencing disproportional high rejection rates for female blue collar borrowers, stifling their growth. Their credit risk scorecards took 6 months to build and had low GINI accuracy.

The Solution — creditX

  • Ensemble ML algorithms for higher predictive accuracy
  • Bias reduction techniques automated in scorecard development
  • 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 with option to add Credit Bureau data where available
  • Implementation completed in 12 weeks

The Results

+25 pts

Increase in GINI accuracy

< 1 week

to develop & deploy new credit models


Increase in approval rates


Reduction in gross credit losses

Bridging the Gender Divide

Within 3 months of using creditX, our customer saw a large uptick in approval rates amongst female borrowers, with a significant reduction in loss rates. This improved financial inclusion while increasing profitability.

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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