Non-traditional Credit Scoring Using Alternative Data

Written by Di Princell

August 17, 2021

Be curious always! For knowledge will not acquire you; you must acquire it ~ anonymous

Come on, don’t judge me based on a few of my personal traits, it’s just not fair. Rather, look at the whole me, then decide if I am worthy.

And if you are evaluating my credit worthiness, impacting the rest of my life, the analysis better be meticulous, expansive, insightful, and timely. Thankfully, the lending industry is listening — credit scores are only part of a consumer’s financial footprint; alternative financial data has arrived empowering deeper insights and more equitable decisioning.

RIBBIT.ai combines 23 years of payment history with the complex field of data science analytics powering data initiatives into the lending space. Instead of relying on a restrictive credit score, RIBBIT.ai’s technology reviews a consumer’s recent transactional bank data.

The insightful process can generate thousands of measurable attributes from a bank account reflecting a consumer’s ability to purchase and pay for products/services. RIBBIT.ai’s non-traditional credit scoring harnesses the predictive power of open banking and payment data delivering smarter and more equitable lending decisions.

RIBBIT.ai’s Chief Data Scientist, Steven Thompson, has more than 15 years of analytics experience in financial, retail, energy, and transportation industries and has effectively led data initiatives at multiple companies. Thompson’s expertise in machine learning, data engineering/enhancement, product development, and sales analytics consulting supports RIBBIT’s data-driven products. Thompson reports, “The speed at which RIBBIT is revolutionizing the marketplace is impressive and invigorating and I am thrilled to be leading their data-driven/AI platform.”

Shawn Princell, CEO of RIBBIT.ai, comments, “Our decisioning products powered by data and supported with innovative AI are changing the dormant, lending landscape. Starting now, consumers and lenders are the recipients of an equitable, all-inclusive risk-decisioning process that covers an unprecedented 99% of bank accounts instantly. This represents a 45% lift over traditional credit and underwriting scores.

 

RIBBIT.ai’s bank analytics process identifies fraud and payment affordability risk based on an applicant’s bank account history. At the conclusion of the analysis, it yields 5 codes that recommend a course of action to minimize repayment risk. The trended attributes that are measured provide a comprehensive, holistic view of a consumer’s financial wellness rather than a static picture. Definitely, a game changer for lenders and consumers!

Stay tuned . . .

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