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Banco Popular

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Background

Banco Popular (Popular) sought to offer the unbanked population an alternative to predatory lenders charging 60% to 300%. These solutions would create genuine customer value, foster economic growth and benefit the bank. Popular also wanted to mainstream clients via credit reporting and a migration path of right-fitting products and services.

At the time, Popular’s connection with the unbanked was mainly through non-bank offices offering payroll, check cashing, bill pay and money transfers. Charting new territory, Popular was prepared to learn and adapt towards a solution.

Challenge

  • No conventional underwriting documentation
  • Most customers have limited or no formal borrowing experience
  • Many customers fear banks – feel they won’t be treated fairly
  • Educational attainment, literacy and financial literacy are limited
  • Small loan size require high efficiency to achieve acceptable ROE

Solution

We assessed all available data sources associated with non-bank office customers. The centers had five major data collection and storage points.

  1. ID verification – image recognition & government ID lookup systems
  2. Main point of sale – associated verified IDs with all direct transactions
  3. Money transfer & third-party POS – ID, time & amounts passed to bank
  4. Office security system – time, location & image recognition
  5. Loan applications

Models were created by using these data sources to assess work, financial, life behavior patterns, accountability, community connectedness and strength of references. Models used a combination of ML and expert modeling to rapidly evolve performance and improve loan applications. Credit approval was instant, subject to reference verification. Pre-approvals were presented at bank POS. First time loans were small. Subsequent types included unsecured and vehicle secured. Customers were extended custom solicitations for traditional bank relationships.

Results

  • The initial model for clients with no lending history had an average PD of 6% (this is 3 to 4 times smaller than PD at predatory lenders)
  • Models quickly improved to an average PD under 3%
  • Unsecured interest rates were between 13% – 28%
  • Even with small loans and short terms, the program operates profitably due to high automation, low operating costs and advanced analytics

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