Credit Sales and Risk Scoring: A FinTech Innovation

Faten Ben Bouheni, Manish Tewari (), Andrew Salamon, Payson Johnston and Kevin Hopkins
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Faten Ben Bouheni: Department of Finance and Real Estate, Menlo College, Atherton, CA 94027, USA
Manish Tewari: Department of Finance, Menlo College, Atherton, CA 94027, USA
Andrew Salamon: Bloomberg, San Francisco, CA 94105, USA
Payson Johnston: School of Business, University of Tennessee Southern, Pulaski, TN 38478, USA
Kevin Hopkins: Economist, Kevin Hopkins Inc., Sandy, UT 84092, USA

FinTech, 2025, vol. 4, issue 3, 1-29

Abstract: This paper explores the effectiveness of an innovative FinTech risk-scoring model to predict the risk-appropriate return for short-term credit sales. The risk score serves to mitigate the information asymmetry between the seller of receivables (“Seller”) and the purchaser (“Funder”), at the same time providing an opportunity for the Funder to earn returns as well as to diversify its portfolio on a risk-appropriate basis. Selling receivables/credit to potential Funders at a risk-appropriate discount also helps Sellers to maintain their short-term financial liquidity and provide the necessary cash flow for operations and other immediate financial needs. We use 18,304 short-term credit-sale transactions between 23 April 2020 and 30 September 2022 from the private FinTech startup Crowdz and its Sustainability, Underwriting, Risk & Financial (SURF) risk-scoring system to analyze the risk/return relationship. The data includes risk scores for both Sellers of receivables (e.g., invoices) along with the Obligors (firms purchasing goods and services from the Seller) on those receivables and provides, as outputs, the mutual gains by the Sellers and the financial institutions or other investors funding the receivables (i.e., the Funders). Our analysis shows that the SURF Score is instrumental in mitigating the information asymmetry between the Sellers and the Funders and provides risk-appropriate periodic returns to the Funders across industries. A comparative analysis shows that the use of SURF technology generates higher risk-appropriate annualized internal rates of return (IRR) as compared to nonuse of the SURF Score risk-scoring system in these transactions. While Sellers and Funders enter into a win-win relationship (in the absence of a default), Sellers of credit instruments are not often scored based on the potential diversification by industry classification. Crowdz’s SURF technology does so and provides Funders with diversification opportunities through numerous invoices of differing amounts and SURF Scores in a wide range of industries. The analysis also shows that Sellers generally have lower financing stability as compared to the Obligors (payers on receivables), a fact captured in the SURF Scores.

Keywords: credit sales; FinTech; invoice financing; risk scoring; return; internal rate of return; diversification (search for similar items in EconPapers)
JEL-codes: C6 F3 G O3 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jfinte:v:4:y:2025:i:3:p:31-:d:1704560

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