By 2026, it became clear that traditional credit scoring, based on past financial reporting data, was no longer suitable for e-commerce. The problem lies in its slowness and static nature, which does not allow for an adequate assessment of the rapidly changing dynamics of online business. In a market where key indicators change daily, analyzing data based on quarterly reports is impossible.
Thus, predictive AI analysis has replaced traditional methods. Its main distinction is that it focuses not on the seller's past income but on their future ability to generate revenue. This change is gradually transforming the role of financial institutions: banks and funds are becoming not just controllers but partners that facilitate the growth of e-commerce businesses.
From the seller's perspective, their problems become evident. The business is expanding, sales volumes are increasing, products are consistently sold on the marketplace, and all operational data is current and available in real-time. However, when seeking financing, the bank still:
- requires collateral,
- uses outdated reporting,
- ignores seasonal fluctuations, product card dynamics, and customer behavior.
As a result, access to capital is either limited or arrives too late, after the peak season has ended, when it was most needed.
From the perspective of banks and funds, the situation also remained challenging for a long time. E-commerce was perceived as a high-risk sector, data from marketplaces was fragmented and poorly standardized, and risk assessment for small businesses required manual analysis, making mass financing of sellers economically unfeasible.
Modern AI scoring changes this paradigm. It analyzes hundreds of parameters in real-time, viewing the business as a dynamic system rather than a set of static figures in reports.
Key aspects of such analysis include the dynamics of purchases and returns. Algorithms can detect anomalies in customer behavior before they impact revenue. For example, a sharp increase in returns or a change in order structure can serve as an early warning signal of potential risks.
Additionally, analyzing reviews and ratings is crucial. The sentiment of comments directly affects sales forecasts, and AI can separate short-term emotional noise from systemic issues related to the product, logistics, or service.
A separate aspect is demand forecasting. The model correlates inventory levels, seasonal fluctuations, competitors' pricing strategies, and advertising activities, allowing for an assessment of whether the current assortment can sustain growth without disruptions and cash flow gaps.
All this data forms the so-called "digital DNA" of the seller—a dynamic business profile that is constantly updated and reflects its actual operational state.
The existence of such a digital profile allows fintech platforms to make credit decisions in minutes rather than weeks. Financing becomes not a one-time transaction but a managed growth tool that adapts to the needs of the business.
One of the main trends of 2026 has been revenue-based financing. More and more funds and neobanks are willing to provide capital without collateral and strict payment schedules—in exchange for a percentage of future sales. This fundamentally changes sellers' approach to debt obligations: repayments become adaptive to turnover, reducing the risk of cash flow gaps and allowing businesses to scale during periods of high demand—before Prime Day, Black Friday, or seasonal sales—without the burden of debt.
Thanks to AI models, risk has become measurable and manageable. For stable sellers, this has led to a reduction in interest rates to record lows in the history of e-commerce financing. Banks and funds, in turn, gain more accurate risk pricing, the ability to safely allocate liquidity, and continuous monitoring of the business even after financing is issued, rather than only at the decision-making stage.
Thus, financing ceases to be "blind" and transforms into a transparent and controlled process.
In conclusion, it can be said that AI scoring has ceased to be a supplementary tool and has become the foundation of a new financial architecture for e-commerce. The "digital DNA" of the seller becomes a universal language of trust between sellers, banks, and investors. Those who have mastered this language gain access to scalable capital, while those who continue to use outdated methods of assessing online businesses increasingly find themselves outside the rapidly growing digital economy.
Kylych Kutaev
Business Mentor
Founder of iSistant (AI platform for e-commerce lending)
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