How ChinaMarket uses digital tools to make cross-border sourcing faster and more accessible for smaller businesses
A rack of colourful scarves. PHOTO: UNSPLASH
The 5th RCEP (Shandong) Import Commodities Expo opened this week at the Linyi International Expo Center, bringing together more than 5,300 buyers and over 400 exhibitors from 48 countries. Alongside the scale of the event, a quieter shift was visible in how trade itself is being organised.
ChinaMarket, the official platform of Linyi Mall, used the expo to show how sourcing is moving from manual coordination to software-led systems. On the first day, it hosted procurement matchmaking sessions and signed agreements with buyer groups from Argentina, South Korea and Ghana. But the focus was less on the deals themselves and more on the mechanism behind them.
The platform operates as a structured network of verified manufacturers, grouped by industrial clusters. Instead of buyers searching supplier by supplier, the system uses data and AI tools to match demand with production capacity. At the expo, this process was made visible through real-time data screens and guided sourcing sessions, where procurement teams connected directly with factories across categories such as building materials, textiles and electronics.
"Sourcing suppliers separately was time-consuming and inefficient. ChinaMarket accurately matches our needs and recommends reliable factories, saving us considerable effort," commented an Argentine buyer.
The underlying problem being addressed is not new. Cross-border sourcing is often slow, fragmented and dependent on intermediaries. What is changing is how that process is being compressed. By combining supplier verification, demand matching and communication into a single system, platforms like ChinaMarket aim to shorten sourcing cycles. They also reduce uncertainty in procurement decisions.
Financing is another layer where the model is evolving. Even when suppliers and buyers are matched efficiently, access to capital can still slow transactions down. Small and medium-sized firms often face constraints around payment terms and access to credit in international trade.
ChinaMarket’s “data + order financing” model links transaction data with financial services, allowing funding decisions to be tied more directly to verified orders rather than external collateral. In practice, this shifts part of the risk assessment from institutions to platform-level data.
The company is also extending this structure into agricultural supply chains. At the expo, it signed an agreement with a local government in Yinan County to build a digitally managed agricultural belt. The model combines sourcing at origin with platform distribution, with an emphasis on traceability for buyers across RCEP markets. This reflects a broader attempt to standardise supply visibility in sectors that are typically less digitised.
Geographically, the platform has been expanding into Southeast Asia. It has launched a digital marketplace in Malaysia and established operations in Indonesia, including support for government-linked procurement projects. These moves suggest a focus on embedding the platform within regional trade flows rather than operating as a standalone marketplace.
"We aim to be a 'super connector' between Chinese industrial belts and global markets", said Quan Chuanxiao, Chairman of Depth Digital Technology Group and ChinaMarket. "By digitizing the cross-border trade process, we solve trust and efficiency issues, making it simpler, faster, and more reliable for overseas buyers to source from China".
What emerges from the expo is less about a single platform and more about a shift in infrastructure. Trade is gradually moving toward systems where discovery, verification, negotiation and financing are handled within integrated digital layers. The question is not whether sourcing can be digitised, but how reliably these systems can scale across industries where trust and execution still depend on physical outcomes.
Keep Reading
Quantara AI launches a continuous platform designed to estimate the financial impact of cyber risk as companies move beyond periodic assessments
Updated
March 17, 2026 1:02 AM
A person tightrope walking between two cliffs. PHOTO: UNSPLASH
Cyber risk is increasingly treated as a financial issue. Boards want to know how much a cyber incident could cost the company, how it could affect earnings, and whether current security spending is justified.
Yet many organizations still measure cyber risk through periodic reviews. These assessments are often conducted once or twice a year, supported by consultants and spreadsheet models. By the time the report reaches senior leadership, the company’s systems may have changed and new threats may have emerged. The way risk is measured does not always match how quickly it evolves.
This gap is where Quantara AI is positioning its new platform. Quantara AI, a Boise-based cybersecurity startup, has introduced what it describes as the industry’s first persistent AI-powered cyber risk solution. The system is designed to run continuously rather than rely on occasional assessments.
The company’s core argument is straightforward: not every security weakness carries the same financial consequence. Instead of ranking issues only by technical severity, the platform analyzes active threats, identifies which company systems are exposed, and estimates how much money a successful attack could cost. It uses statistical models, including Value at Risk (VaR), to calculate potential losses. It also estimates how specific security improvements could reduce that projected loss.
The timing aligns with a broader market shift. International Data Corporation (IDC) projects that by 2028, 40% of enterprises will adopt AI-based cyber risk quantification platforms. These tools convert security data into financial estimates that can guide budgeting and investment decisions. The forecast reflects growing pressure on security leaders to present risk in terms that boards and regulators understand.
Traditional compliance and risk management systems often focus on meeting regulatory standards. Vulnerability management programs typically score weaknesses based on technical characteristics. Consultant-led risk studies provide detailed analysis, but they are usually performed at set intervals. In fast-changing threat environments, that model can leave decision-makers working with outdated information.
Quantara’s platform attempts to replace that periodic process with continuous measurement. It brings together threat data, internal system information and financial modeling in one system. The goal is to show, at any given time, which specific weaknesses could lead to the largest financial losses.
Cyber risk quantification as a concept is not new. What is changing is the expectation that these calculations be updated regularly and tied directly to financial decision-making. As cyber incidents carry clearer monetary consequences, companies are looking for ways to measure exposure with greater precision.
The broader question is whether enterprises will shift fully toward continuous, AI-driven risk analysis or continue relying on periodic external assessments. What is clear is that cybersecurity discussions are moving closer to financial reporting — and tools that estimate potential loss in dollar terms are becoming central to that shift.