Revolutionizing E-commerce Financing and Digital Lending: The Impact of Alternative Data and AI in Credit Assessment

The opportunities for brands in the e-commerce space have grown significantly in recent years. During the second quarter of 2023, consumers spent over $277 billion on e-commerce purchases in the US — the highest quarter ever — and in 2024, experts anticipate more than 20 percent of all retail sales will be attributable to e-commerce.

E-commerce financing and digital lending have emerged within the growing e-commerce ecosystem to empower brands to take advantage of these opportunities. This alternative to traditional financial services supports growth by acknowledging the unique factors that shape the operations of e-commerce businesses.

To effectively evaluate financing risks in the e-commerce space, lenders are increasingly looking to technology-driven processes that can fuel data-driven decision-making. These processes, which include artificial intelligence and the use of alternative data, are revolutionizing credit assessment and underwriting.

How e-commerce financing is driving a paradigm shift in business operations

The shift to e-commerce involves much more than simply offering new sales channels. To effectively offer e-commerce solutions, brands must engage in new business models.

Higher volatility is a component of the e-commerce model, as changes in search algorithms and their impact on web traffic can significantly impact revenue and expenses. Competition can also spring up and impact profitability more rapidly in the digital marketplace than in traditional sales channels.

E-commerce models also typically take a different approach to inventory than traditional businesses. The “just-in-time” approach that facilitates e-commerce drop shipping processes allows for the agility online business needs and creates different demands in the area of capital and budgeting.

“These factors and others that differentiate e-commerce from traditional businesses can make it more difficult to access traditional financing options,” says Jay Avigdor, President and CEO of Velocity Capital Group. “Higher volatility, for example, adds challenges to financial planning and cash flow management, which are two categories that typically play a role in obtaining financing. Brands that operate exclusively online also lack the business assets lenders typically consider.”

Velocity Capital Group is a direct funding platform located in Greater New York that funds small businesses nationwide and has serviced more than 15,000 clients since its founding in 2018. Avigdor’s innovative technological approach to funding is setting new trends in the industry by merging finance with technology through automation, thus allowing a quicker and smoother process for merchants and brokers.

“To effectively serve the financing needs of e-commerce brands, lending institutions have adopted new models that take into account their unique circumstances and needs,” Avigdor explains. “These models not only assess risk differently, but also make financing available through new channels.”

How digital lending is transforming the borrowing experience

Digital lending is driven by the same advances in technology that have empowered record growth in e-commerce. Automated online loan applications result in greater accessibility and a faster decision-making process for borrowers. By replacing manual underwriting with algorithms, digital lending platforms can provide businesses with decisions in days, whereas the same requests could take weeks to process with conventional methods.

For lenders, digital platforms reduce costs by allowing payments, statements, account access, customer service, and loan management to occur digitally. Digital platforms also empower lenders to get a better perspective on risk.

“By utilizing huge datasets from across thousands of loans, digital lenders are also able to conduct more extensive risk modeling,” says Avigdor. “The data allows them to automatically and dynamically improve their fraud detection and credit risk models.”

The role of alternative data in credit assessment

Traditional financing models are built around the operations of traditional businesses by taking into account business credit scores and reports, which draw upon past payment history, debt, and collections. They also involve financial statements that reveal revenue, expenses, profitability, cash flow, and other financial performance metrics.

With e-commerce businesses, traditional metrics don’t tell the whole story. By looking to non-traditional data sources as part of their risk assessment process, digital lending platforms can get more clarity on the potential of e-commerce businesses.

“Digital lenders leverage the latest technology to incorporate non-traditional data, such as e-commerce sales and online traffic, into their evaluation process,” says Avigdor. “By combining that with standard credit data, they gain deeper insight into the client’s operations.”

With e-commerce, data on consumer demand — which plays a role in assessing credit risk — can be determined by data on digital marketplace traffic, conversions, review volumes, and social media analytics. Data on online advertising performance can reveal a business’s growth potential based on its effectiveness at deploying and scaling digital ads, while data on domain authority can speak to a brand’s reputability and algorithmic performance.

Leveraging artificial intelligence for enhancing underwriting processes

Digital lending platforms are able to enhance the underwriting process further by leveraging artificial intelligence. AI streamlines the process by empowering a quick analysis of relevant data from a wide range of sources and contributes to a better customer experience while lowering costs for lenders.

“AI empowers automation that provides e-commerce businesses with a loan decision at the rapid pace required in their industry to take advantage of opportunities,” says Avigdor. “AI algorithms streamline the approval process by allowing for rapid analysis of thousands of data points.”

AI can also be used in digital lending to develop personalized strategies for risk assessment and loan management. Using transaction history, repayment data, and customer behavior to train machine learning models increases the accuracy of underwriting by driving dynamic optimization. Using AI for predictive analysis of factors like repayment performance results in insights that can inform account management strategies.

A key concern with AI, however, is the introduction of biases into the lending process. To avoid this, lending institutions must find a way to strike a balance between AI and human engagement that supports responsible and ethical lending.

“When businesses leverage AI to enhance the decision-making process, there should always be a human element,” encourages Avigdor. “Standards must be set and performance reviewed regularly to ensure AI is adding to and not detracting from the process. Ideally, AI should manage 99.9 percent of the repetitive, copy-paste work and pull in data needed to make an informed decision. For the final decision, a human representative should always be involved in analyzing and assessing AI’s recommendations. That step is essential for safeguarding against AI biases and unreliability.”

Digital platforms are reshaping the lending sector in a way that promises to serve the unique needs of the evolving e-commerce industry. They bring the speed and flexibility needed to effectively facilitate loan requests, and by integrating AI, they empower lenders to gather and analyze the data needed to evaluate businesses with a limited financial history and non-traditional business practices. 

As the demand for e-commerce grows, digital lending will play an ongoing role in facilitating the launch and growth of businesses seeking to meet that need.

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