Crypto trading platform BingX is reporting a sharp increase in demand for AI-assisted trading tools, with daily requests to its AI Skills Hub rising from 2.85 million to 30 million over five months. The approximately 953% increase points to growing interest in using conversational AI to access cryptocurrency market data, monitor trading positions, review account balances and manage orders. The development highlights how AI assistants are beginning to move beyond financial research into practical digital asset trading workflows.

BingX Connects AI Assistants With Crypto Trading Infrastructure

BingX is expanding the intersection of artificial intelligence and cryptocurrency trading through its AI Skills Hub, an API-based toolkit designed to connect AI coding assistants with the exchange’s trading infrastructure. The platform allows users to interact with BingX through natural-language prompts rather than manually navigating multiple interfaces or building custom API integrations.

According to usage figures released by the company, daily requests increased from 2.85 million to 30 million over five months, representing approximately 953% growth. BingX attributes the increase to rising demand for AI-assisted access to market information, account management and trading operations.

The AI Skills Hub works with development assistants including Claude Code , Cursor and Windsurf. Its library contains nearly 30 skill modules covering perpetual futures, spot trading, coin-margined contracts, copy trading, account management and technical analysis.

Rather than requiring users to understand the underlying API structure, the toolkit translates conversational instructions into interactions with supported exchange functions. These include retrieving market data, checking positions, reviewing balances and placing orders.

This approach reflects a broader shift in financial technology: AI interfaces are increasingly being positioned as an additional access layer between users and complex financial platforms.

Trading Activity Becomes a Significant AI Use Case

The usage breakdown shared by BingX offers some insight into how traders are applying conversational AI.

Trading-related activity represented 40% of the platform’s top 10 request types, while market data APIs accounted for another 26%. These figures suggest that users are not limiting AI assistants to general cryptocurrency research. They are also using them to retrieve operational information and interact with trading accounts.

The distinction matters. Financial AI applications have historically concentrated on information retrieval, research and customer support. Connecting AI assistants to execution capabilities introduces a more consequential use case, where an instruction can potentially lead to a financial transaction.

BingX says its platform incorporates confirmation requirements and safeguards intended to keep users in control of trading activity. However, the company has not disclosed detailed figures on order execution through AI, transaction volumes generated by the toolkit, or the specific approval mechanisms used across different trading operations.

Those details will become important as AI-powered financial interfaces develop. Natural-language commands can reduce friction, but they also introduce questions around instruction interpretation, authentication, authorization and accidental execution.

For cryptocurrency exchanges, these considerations are particularly important because digital asset markets operate continuously and can experience rapid price movements.

AI Trading Raises Questions About Security and Oversight

One important aspect of the announcement is the toolkit’s approach to execution permissions. BingX’s publicly available AI Skills Hub repository documents a confirmation mechanism for production write operations, including order placement, cancellations and transfers. Users must enter a confirmation instruction before these operations are executed in the live environment. The repository also describes masking mechanisms for sensitive API credentials. GitHub+1

These controls address some of the risks associated with connecting AI assistants to financial accounts, but they do not eliminate them. API permissions, credential storage, access management and the accuracy of AI-generated instructions remain important considerations. Users also need to understand which operations are read-only and which can change their financial positions.

The distinction between an AI assistant recommending an action and an exchange executing it is particularly important. An incorrectly interpreted instruction could have direct financial consequences, especially in leveraged cryptocurrency trading.

BingX has not provided independent audit results or detailed security performance metrics for the AI Skills Hub. Its reported usage growth should therefore be viewed as an indicator of platform activity rather than evidence of improved trading outcomes, profitability or risk management.

AI Adoption Is Reshaping Financial Services

The development fits into a wider financial services trend. AI assistants are increasingly being used to interpret financial information, support customer interactions and simplify access to complex services.

A 2025 McKinsey global banking survey of more than 30,000 consumers across seven countries found that 51% used generative AI, while 23% used it for banking or finance-related activities. The findings suggest that conversational AI is becoming relevant to financial decision-making, although consumer adoption does not necessarily translate into adoption of automated trading. McKinsey & Company

BingX’s approach takes this interaction a step further by connecting AI assistants to exchange APIs. Instead of simply explaining cryptocurrency market movements, an assistant can retrieve account information and initiate supported trading operations.

This model also creates opportunities beyond individual traders. BingX says affiliate partners are using the toolkit to analyze trading activity, transaction volumes and commission data, with some building customized dashboards. Such integrations could help trading communities and platform partners develop operational tools without building every exchange connection from scratch.

For the wider fintech ecosystem, the model resembles the evolution of embedded finance infrastructure. APIs allow financial capabilities to operate within external applications, while AI skills provide a conversational interface for accessing those capabilities. The combination could influence how digital asset platforms, payment providers and financial technology companies approach developer access and user experience.

However, BingX’s toolkit is currently focused on its own exchange ecosystem. It should not be confused with open banking infrastructure, which generally enables regulated sharing of financial data between institutions and authorized third-party providers.

What Comes Next for AI-Enabled Crypto Platforms?

Competition in AI-powered financial technology is likely to extend beyond the quality of conversational interfaces. Reliability, execution controls, API coverage, interoperability and transparent permission management will help determine whether these tools become dependable components of trading infrastructure.

The integration of assistants such as Claude Code, Cursor and Windsurf also reflects a growing developer-oriented approach to financial services. Rather than building standalone AI applications, exchanges can expose their existing infrastructure through standardized skill libraries that developers and users can connect to their preferred assistants.

For BingX, the next test will be whether its reported request growth translates into sustained engagement, broader developer adoption and measurable improvements in the trading experience.

The 953% increase is a notable usage signal, but request volume alone does not reveal how many active traders use the toolkit, how often orders are executed or whether AI-assisted interactions produce better decisions.

The broader development is nevertheless significant: cryptocurrency exchanges are beginning to position AI assistants not just as research tools, but as interfaces for accessing live financial infrastructure. That shift could become an important part of the next generation of digital asset trading platforms.

Market Landscape

AI-powered financial interfaces are moving from information retrieval toward transaction execution. BingX’s AI Skills Hub illustrates this transition by connecting conversational assistants directly to cryptocurrency exchange APIs.

Three developments are shaping this market:

• Embedded financial access: APIs are allowing trading and account-management capabilities to operate through external applications.
• Agentic financial workflows: AI assistants are progressing from answering market questions to initiating actions that require authorization and oversight.
• Developer-led financial infrastructure: Skill libraries and API integrations are reducing the technical barriers to building customized trading interfaces.

The competitive opportunity lies in making these integrations secure, interoperable and reliable rather than simply increasing the number of AI interactions.

Top Insights

• BingX reported daily AI Skills Hub requests growing from 2.85 million to 30 million over five months, representing approximately 953% growth.
• Trading-related requests accounted for 40% of the platform’s top 10 request types, indicating demand for AI-assisted trading operations.
• Nearly 30 skill modules support spot trading, perpetual futures, copy trading, account management and technical analysis.
• Production trading operations require explicit confirmation, according to BingX’s publicly available repository, adding an approval layer to AI-assisted execution.
• Affiliate partners are using the toolkit to develop custom dashboards for monitoring trading volumes, commissions and account activity.

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