By the beginning of 2026, it became obvious that the classical credit scoring model no longer corresponded to the reality of e-commerce. The approach based on the analysis of accounting statements for previous periods turned out to be too sluggish for a market where turnover, demand and customer behavior change on a daily basis. As a result, the e-commerce business found itself in a situation of chronic shortage of affordable and timely financing.
Traditional scoring has been replaced by predictive AI analysis, which fundamentally changes the very philosophy of risk assessment. Its key difference is that it does not analyze historical figures, but the seller's future ability to generate revenue. This shift is transforming the role of banks and foundations: financial institutions are gradually ceasing to be external supervisors and increasingly becoming partners in online business growth.
The problem for sellers has long been the gap between the real dynamics of the business and the demands of creditors. Even with stable sales and transparent operational data, banks continued to:
- demand a deposit,
- rely on outdated reporting,
- ignore seasonality, product cards, and customer behavior.
As a result, financing either became unavailable or arrived late — after peak seasons, when the need for capital was maximum.
The situation also remained problematic for banks. E-commerce was perceived as a high-risk segment, marketplace data was fragmented, and manual assessment of small businesses made mass lending economically inefficient.
Modern AI scoring bridges this gap. It analyzes a business in real time as a live system using hundreds of parameters: the dynamics of repurchases and refunds, the structure of orders, customer behavior, reviews and ratings. Algorithms are able to detect anomalies long before they manifest themselves in a drop in revenue, turning risks from reactive to predictable.
A separate block of analysis is related to demand forecasting. AI models compare inventory balances, seasonality, competitors' pricing policies, and advertising activity, allowing you to assess the sustainability of growth and the likelihood of cash gaps. Together, these data form the so—called "digital DNA" of the seller, a dynamic business profile that is constantly updated and reflects its actual operational status.
It is the presence of such a digital profile that allows fintech platforms to make credit decisions in minutes, rather than weeks. Financing ceases to be a one-time transaction and turns into a managed growth tool that adapts to the seller's business model.
Revenue-Based Financing has become one of the key trends in 2026. More and more funds and non—banks are providing capital without collateral and a fixed payment schedule in exchange for a percentage of future sales. This reduces the risk of cash gaps and allows you to scale during peak periods without debt pressure.
Thanks to AI models, risk has become measurable and manageable. For steady sellers, this has led to lower interest rates to historic lows for e-commerce. For financial institutions, to more accurate pricing of risk and constant monitoring of the business after the issuance of financing.
As a result, AI scoring ceased to be an auxiliary tool and became the basis of a new financial architecture of e-commerce. The seller's "digital DNA" is turning into a universal language of trust between sellers, banks, and investors, opening up access to scalable capital to those who know how to work with data.
