Traditional credit scoring methods rely on narrow, often outdated sets of data and are in desperate need of improvement. The traditional data that banks and FIs depend on to score customers excludes the unbanked population of 3+ billion people across the globe, and forfeits an incredible opportunity for growth.

The Consumer Financial Protection Bureau (CFPB) acknowledges this opportunity, stating,

“Alternative data from unconventional sources may help consumers who are stuck outside the system build a credit history to access mainstream credit sources.”

In fact, CGAP’s extensive research underlines how essential non-traditional credit scoring is to the future of banking and financial inclusion. Senior Financial Sector Specialist, Maria Fernandez Vidal found that,

“[…] statistical models in emerging markets […] standardize and improve lending decisions,” and allow customers to be scored based on statistical analysis instead of relying on subjective judgements of loan officers.

Fortunately, non-traditional credit scoring methods allow the unbanked to create a viable credit history on which financial institutions can rely. Fintechs have created this space by rigorously testing data sources paired with machine learning.

The Bank for International Settlements (BIS) investigated what machine learning can do for credit scoring and discovered, 

“[…] the [credit scoring] model based on machine learning and non-traditional data is better able to predict losses and defaults than traditional models.”

This utilization of machine learning means banks and FIs can depend on predictive financial behavior models and customers can depend on their data to build their credit. These real-time credit models are not only strengthening FIs understanding of their customers, but are also allowing them to engage new customer segments.

Pngme’s embedded data infrastructure layer is powered by 4M real-time financial data points from one click and de-risks lending operations for banks and FIs with accurate credit scorecards.

With a real-time credit model, pulling data from sources like USSD transactions, as well as GPS and device metadata, you can gain powerful insights into your customers and bring on new customers that have been traditionally unserviceable through traditional credit models.  

Pngme’s technology is enabling this crucial industry shift to adopting real-time credit models. Our embedded data layer drives the ability to discover new customer segments and confidently back lending decisions in real-time. Gain a deeper understanding of your customers’ financial identities and unlock new opportunities for our product road map with Pngme. 

Interested in learning how to build real-time credit models with your data? REQUST A DEMO