By 2026, it became clear that traditional credit scoring, based on the analysis of financial statements from previous periods, is no longer suitable for e-commerce. This is due to its slowness and static nature, which do not reflect the dynamics of online business, where key indicators change every day.
In response, predictive analytics using artificial intelligence comes to the forefront. This approach focuses not on the seller's past income, but on their future earning potential. This transformation is gradually changing the role of banks and financial institutions: they are no longer just regulatory bodies and are increasingly becoming partners in the development of e-commerce.
From the seller's perspective, the situation appears challenging. The business is growing, volumes are increasing, products are successfully sold on marketplaces, and all operational data is available in real-time. However, when it comes time to request financing, banks still:
- require collateral,
- rely on outdated financial data,
- neglect seasonal fluctuations, changes in product characteristics, and customer preferences.
As a result, access to capital is either limited or comes too late—often after the peak season has ended, when it was most needed.
On the other hand, banks and funds have also faced problems. E-commerce was perceived as a high-risk category, data from marketplaces was fragmented and poorly standardized, and risk assessment for small businesses often required manual analysis and took a lot of time. This made mass financing for sellers economically inefficient.
However, modern AI scoring technologies are changing this picture. Now, analysis is conducted based on hundreds of parameters in real-time, allowing businesses to be viewed as dynamic systems rather than as sets of static figures in reports.
The main components of such analysis include, first and foremost, the dynamics of buybacks and returns. Algorithms can detect deviations in purchasing habits even before they impact revenue. A sharp increase in returns or a change in order structure serves as a warning signal for potential risks.
Analysis of reviews and ratings is also important. The tone of comments can significantly affect sales forecasts, and AI can distinguish between short-term emotional reactions and systemic issues related to the product, logistics, or service quality.
Demand forecasting is another important aspect. The model analyzes inventory levels, seasonal fluctuations, competitors' pricing policies, and advertising actions, which allows for assessing whether the current assortment can sustain growth without disruptions and cash gaps.
All this data forms the so-called "digital DNA" of the seller—a dynamic business profile that is constantly updated and reflects its current state.
It is the presence of such a digital profile that enables fintech platforms to make lending decisions in minutes rather than weeks. Financing ceases to be a one-time operation and becomes a manageable growth tool that adapts to the needs of the business.
A key trend of 2026 has become revenue-based financing. More and more funds and neobanks are ready to provide capital without collateral and strict payment schedules—in exchange for a percentage of future sales. For the seller, this radically changes the approach to working with debt: repayments are adjusted to sales volumes, reducing the risk of cash gaps and allowing the business to scale during seasonal peaks—before Prime Day, Black Friday, or sales—without additional debt obligations.
Thanks to AI models, risk has become measurable and manageable. For stable sellers, this has led to a reduction in interest rates to historically low levels in the field of e-commerce financing. Banks and funds, in turn, have the opportunity to assess risks more accurately, safely allocate liquidity, and conduct ongoing monitoring of businesses even after providing financing.
Thus, financing is no longer a "blind" process—it becomes transparent and controllable.
In conclusion, it can be said that AI scoring has established itself as the foundation of a new financial architecture for e-commerce. The seller's "digital DNA" becomes a universal language of trust between sellers, banks, and investors. Those who learn to work with this language will gain access to scalable capital. Those who continue to use outdated methods of assessing online businesses risk being left out of the rapidly evolving digital economy.
Kylych Kutaiev
Business Mentor
Founder of iSistant (AI platform for e-commerce lending)
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