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Why We Invested in Pave

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Next up in our Why We Invested Series is Pave. Pave provides financial institutions (“FIs”) with an analytics platform to serve their customers with more equitable financial products. Pave’s platform offers AI-powered analytics and scoring solutions that provide consumer cash flow insights to banks, credit unions, fintechs, and debt capital providers.

The prevailing problem in evolving credit risk models is twofold: outdated reliance on historical credit behavior without considering current financial indicators and the lack of resources/capabilities for firms to incorporate new data sources effectively. The current state of credit risk models presents significant challenges as defaults continue to rise and traditional approaches fail to adapt to modern financial dynamics. Today, there are 53M Americans with little to no credit history, hindering them from accessing credit. In addition, a recent survey by LendingTree found that 42% of Americans were denied financial products due to their credit score in the last year. Over the last 25 years, credit models have primarily relied on historical credit usage behavior, heavily relying on the FICO Score, which is based on credit usage data, not the behavior of consumers. FICO score has been the only widely accepted credit scoring model since 1989. However, these models come with inherent limitations that hinder their ability to assess creditworthiness and predict default risks accurately.

The secondary challenge lies in the inefficiency and costliness of analyzing and leveraging new data sources. Creditors face significant hurdles in ingesting, enriching, and unifying diverse financial data from multiple sources. Aggregating all new forms of data would require substantial ongoing investments in software engineering, data engineering, and machine learning. Building and maintaining thousands of data pipelines, developing models and scores, and ensuring data quality would require a sophisticated data science team that many creditors lack, making the continual analysis of this new data impractical, and FICO remains the standard. Even though there are options to replace FICO, these options predict a consumer’s risk level to serve consumers but fall short of the nuanced ways to serve consumers.

Pave’s innovative platform offers AI-driven analytics and scoring solutions, delivering valuable insights to financial institutions, fintech companies, and lending providers. This technology empowers banks, credit unions, and debt capital firms with an in-depth understanding of consumers’ cash flow and financial capacity. By simplifying the extraction of financial data from diverse sources, Pave expedites the creation of forward-looking financial products. The platform caters to risk and product teams, facilitating streamlined processes to decrease manual underwriting expenses, evaluate repayment capabilities, optimize loan rates, and spot early signs of payment delinquency.

At the heart of Pave’s capabilities is its robust API, which enables seamless transmission of data from sources like banking aggregators, credit bureaus, payroll systems, and cryptocurrency platforms. Pave then consolidates, refines, and transforms this information into usable cash flow metrics, risk attributes, and scores. These resources can be seamlessly integrated into developers’ models and applications.

Raymond and Ema Rouf form a dynamic entrepreneurial duo, blending their expertise in building enterprise analytics and their passion for expanding financial access. With a combined experience of over two decades, they have jointly founded and co-founded two previous ventures. Ema and Raymond’s most notable company is GraphScience, an advertising optimization platform. GraphScience’s transformative approach translated into remarkable returns on Facebook ads for major retail players like Nordstrom and Urban Outfitters, culminating in its acquisition by Centro in 2013. Raymond’s foundation in Economics, Mathematics, and Computer Science from the University of Urbana-Champaign, coupled with Ema’s background in Sociology from UC Irvine, underscores their complementary skill sets. Their shared journey as both business partners and a married couple reflects their unique ability to combine their talents, drive, and vision to create impactful ventures in the fintech arena.


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