Case Study: goeasy Leverages Predictive Modeling to Improve Lending Portfolio

Canadian Financial Services Organization partners with GDS Link to Accelerate Non-Prime Leasing and Lending Services

Recently, GDS Link had the opportunity to partner with goeasy, an alternative finance company based in Ontario, Canada. goeasy operates in two segments: easyfinancial, which provides lending to non-prime borrowers, and easyhome, which sells home furnishings and durable goods on a rent-to-own basis. goeasy reached out to GDS Link, to discuss the possibility of utilizing Predictive Model Markup Language (PMML) to support the growth of goeasy’s loan portfolio.

About goeasy

With a wide variety of financial products and services including unsecured and secured installment loans, goeasy aspires to help put Canadians on a path to a better financial future, as they rebuild their credit and graduate to prime lending. Customers can transact seamlessly with easyhome and easyfinancial through an omnichannel model that includes online and mobile, as well as through 400 leasing and lending locations across Canada supported by more than 1,900 employees. Throughout the company’s history, it has served over 1 million Canadians and originated over $3.3 billion in loans, with 1 in 3 customers graduating to prime credit and 60% increasing their credit score within 12 months of borrowing.

The Challenge

In order to support the growth of their loan portfolio, goeasy was building robust adjudication and lending models. The models and algorithms were translated to pseudo-codes and detailed technical requirements, but this resulted in increased process lengths, which in turn produced a wider margin of error.

In essence, goeasy needed a solution that would allow for:

  • Faster, and less manual, model implementations
  • Simplified deployment
  • Easier requirements gathering
  • Reduced requirement translation, saving time

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To learn how GDS addressed this challenge, and the results it produced for goeasy, download the Case Study below:

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