Nisun. $NISN. Monetizing the "Historical Credit Footprint

2021: The Peak of Profitability

In 2021 (according to the 20-F annual report), Nisun achieved record-breaking figures within its financing segment:

Net Income: It ranged between $35 million and $40 million USD.

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Based on financial reports and regulatory filings, the scale of Nisun's operations can be estimated with high precision. Between 2020 and 2024, Nisun worked with roughly thousands of small and medium-sized enterprises (SMEs).

Here is the breakdown of how to arrive at this figure:

1. SME Financing Scale

During the peak of its financing operations (2022–2023), the SME financing segment generated over $100+ million in annual revenue.

Since the average loan size for a Chinese SME in this sector typically ranges in the low hundreds of thousands of dollars, Nisun must have served at least hundreds to low thousands of unique companies to achieve such revenue volumes.

2. Supply Chain Trading and Integration

In the trading segment—where Nisun generated revenues of approximately $250 million in 2024—the partner network is even broader.

Nisun acts as an "integrator" between upstream producers (farmers/manufacturers) and downstream buyers (retailers/franchises).

In projects like the Henan Wanbang logistics hub or the KFC franchise operations, Nisun has direct or indirect reach into hundreds of specific local suppliers and outlets.

3. Institutional Database Memory

In its SEC filings from December 2025, Nisun emphasizes that its new "Nisun AI" strategy is built upon previous relationships.

The company states it has already established an "extensive network in the agricultural and industrial sectors."

This "contact database," which Li Guo and Xin Liu are now digitizing, includes historical data on payment discipline and transactions for the thousands of entities with which they have conducted at least one trade or financial transaction in the past.

Why this number matters for the "100+ companies" goal

When Li Guo mentions needing "100+ active companies on the software," he views this as the critical mass for the network effect to take hold. They have a massive pool to choose from—if their historical database contains 2,000 firms, they only need to "convert" 5% of their legacy base to the new software to reach their goal and prove to funds like UBS and Squarepoint that the model is viable.

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 In the context of Nisun International's strategic pivot announced in late 2025, the company is systematically transforming its legacy of SME financing relationships into a high-margin AI and data ecosystem.

Here is how Nisun is technically and strategically extracting value from its past supply chain data:

1. Monetizing the "Historical Credit Footprint"

For years, Nisun acted as a direct lender. During this time, they accumulated deep, non-public data on thousands of SMEs (Small and Medium Enterprises) in China.

The Data Lake: They have historical records of transaction volumes, seasonal cash flows, and default risks of specific players in the egg, grain, and KFC supply chains.

The Transition: Instead of using this data to decide whether Nisun should lend money, they now feed this data into their AI models to create "Credit Scoring as a Service." They sell this validated risk profile to traditional banks that are otherwise too afraid to lend to these small players in 2026.

2. The "Software-First" Client Migration

Nisun is leveraging its past role as a "Banker" to become a "Software Provider."

The Hook: Past borrowers are already integrated into Nisun’s network. Nisun "onboards" these firms onto their SaaS (Software as a Service) platform by offering them the "digital identity" they need to qualify for bank loans in the current tight economy.

Data Extraction: Once a company uses Nisun's software for daily operations (ordering, logistics, payments), Nisun gains real-time data visibility. They move from knowing what a company did last year to knowing what they are shipping today.

3. Creating a "Supply Chain Control Tower"

By connecting over 100+ firms from their historical database, Nisun creates a Data Flywheel:

Aggregated Insights: By seeing data from both the supplier (e.g., a corn farmer) and the buyer (e.g., a KFC franchise), Nisun’s AI identifies inefficiencies that neither party can see alone.

Efficiency Arbitrage: Nisun extracts "efficiency data"—knowing exactly when a shipment will be late or where prices are dipping. They profit by charging "technology fees" for the AI-driven optimizations that save these companies 10–15% in costs.

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On December 18, 2025, the Board of Directors of Nisun officially approved a strategic plan for a complete exit from the areas of SME financing, supply chain financing, and related transaction services.

In practice, it now looks as follows:

The end of the internal treasury (Skin in the game).

Nisun no longer lends its own money. What they are doing now is a so-called "Asset-Light model." Instead of saying, "Here are our millions," they say, "Here is our software and our data. We will connect you with a bank or a fund that will lend to you, and in return, we will take a brokerage fee and a technology service fee."

So, what does that "relationship from the past" look like? When I wrote about "100 new companies," I meant that Nisun is utilizing its existing contact databases from the time when they were still lending to them.

In the past: Nisun was the banker for these companies.

Today: Nisun is their software provider and consultant. Companies are not joining the new software to get money directly from Nisun, but rather to be able to obtain any financing at all in 2026.

Traditional Chinese banks in 2026 are extremely cautious, and without the digital data provided by Nisun, these small companies would not receive a bank loan at all.

Transformation as a necessity, not a choice. In the SEC filing from December 2025, management admitted that the SME financing segment showed "deteriorating prospects." Simply put, lending its own money to small companies in the current Chinese economy has become too risky (due to high default rates).

That is why Li Guo arrived: His task is not to save the financing business, but to flip the company into a pure IT and AI service provider.

Summary: You are right; they are no longer printing money directly. Now they are selling an "entry ticket" to other people's money (banks) through their software. By doing so, they eliminate the risk of borrower default, transferring it to the banks while keeping the margin for the technology.

If Nisun AI manages to scale its platform to hundreds or thousands of companies, a fundamental economic shift occurs, known in technology as the Network Effect. In the Chinese context of 2026, this has three very specific consequences for Nisun:

1. "Self-learning" AI (Data Flywheel) With every additional company, Nisun's AI model gains more data on prices, logistics routes, and payment discipline.

With 10 companies: The system only passively monitors data.

With 100+ companies: The AI begins to spot anomalies (e.g., "Company A in Shanghai is buying eggs at a higher price than the regional average").

The Result: Nisun can start selling predictive consulting to these companies. The AI will tell them: "If you switch to the supplier we have in our system just 50 km away, you will save 12%." This makes the platform "addictive" for companies because it generates real money for them.

2. Dramatic margin growth (Operating Leverage) This is the moment that interests institutional funds (UBS, Squarepoint):

The costs of software development are nearly the same for Nisun whether it is used by 10 or 500 companies.

As the number of companies grows, every additional dollar of revenue goes almost entirely to profit because the company does not have to buy more vehicles or warehouses (the Asset-Light model).

This will allow Nisun to report much higher profitability in 2026, even if total revenue grows more slowly.

3. The Position of "Digital Guardian" (The Gatekeeper) Once there is a critical mass of companies in the system, Nisun becomes an indispensable partner for banks.

If a bank in China wants to lend money to a small farmer, it doesn't ask the farmer; it asks Nisun AI: "What is this company's score in the system?"

In practice, Nisun begins to control the flow of money in the entire sector without having to lend a single dollar itself. It merely collects a "toll" for connecting safe borrowers with banks.

Why are Xin Liu and Li Guo talking about this?

For them, the "100-company threshold" is a milestone where Nisun becomes a technological monopoly in a specific region or vertical (e.g., egg distribution or the operation of KFC franchises).

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https://selectionnewyork.blogspot.com/2026/01/nisun-nisn-nasdaq-due-diligence-in-form.html

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