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How AI/ML-based credit scoring accelerated a lending application to become an industry leader

Behavioral Scoring - AND Solutions

SCORING IN MONGOLIA AT THE TIME
There were mainly two problems with credit scoring that were being used in Mongolia at the time. First, there was no automation used in scoring systems, and credit scoring had to be done manually for each loan request. Due to its heavy reliance on manual work and high operational costs, financial institutions could not offer lower amounts of loan products which as micro-loan. Second, limited types of data sources used by financial institutions, including credit history, proof of steady income, and collateral, meant that people who have no financial footprint or steady income could not afford a loan.

IMPLEMENTING THE FIRST AI/ML-POWERED CREDIT SCORING IN MONGOLIA
Our new credit scoring models solved these previously mentioned problems. With end-to-end automation, from data collection to processing and final approval, customers can finish the entire process within 5 minutes, all from their smartphone. We employed multiple models for this scoring system, including random forest and artificial neural network, and created a model that produced the most accurate results. This Scoring model continuously learns and updates itself based on new data sent from the loan management system.

COMBINING CREDIT AND BEHAVIORAL SCORING
Once we implemented credit scoring and acquired a sizable number of customers, we also implemented behavioral scoring to assess and identify high-value customers and upgrade their products with increased credit. Our behavioral scoring for LendMN utilizes customers’ various kinds of in-app activities, and more importantly, their repayment tendencies.

Result

  1. 96% repayment rate compared to traditional financial institutions being around 80%
  2. The application process under 5 minutes
  3. Market leader for micro-lending service
  4. Disbursing 180,000 loans monthly on an average

 

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