Attorney Shao Shiwei | How Are Virtual Currency Fraud Cases Adjudicated? Do They Necessarily Constitute Fraud?—Examining the Scope for Defense Through Five Key Determinants

Five Key Questions Determining the Direction of the Case

From practical experience, whether such cases are characterized as fraud often depends on a comprehensive assessment of several core issues.

1. Were Users Deceived by the Platform?

The assessment of such cases must first return to the starting point: Was the investment behavior of users (investors) caused by deception by the platform operators, agents, or other involved parties?

In practice, we typically assess the investor’s actual state of knowledge from the following aspects:

Duration of investment. If an investor has participated in trading for one or two years, or even longer, they usually have a considerable understanding of the platform’s operational model, fund flows, and risk characteristics. It is difficult to argue that they remained in a state of "being deceived" throughout such a long period.

Existence of profit records. If an investor has never made a profit, or could not withdraw funds after making a profit, the characteristics of being deceived are more apparent. However, if the investor has made profits and successfully withdrawn funds, it indicates that the platform was not a "one-way street" (taking in funds without allowing withdrawals). Subsequent losses by the investor may be the result of continued participation in trading, rather than being caused by platform fraud.

Ability to make autonomous decisions. In many cases, we see statements in investors’ testimonies such as: "Sometimes I did not follow the trade-leading teacher’s advice; if he suggested buying long, I would buy short." This indicates that the investor did not mechanically execute the teacher’s instructions but possessed the awareness for independent judgment and the ability to make autonomous decisions.

If many people traded for one or two years, or even made profits, but only claimed to be "deceived" after incurring losses, this is a point that defense lawyers need to prominently highlight to investigators in judicial practice.

For example, in a case previously handled by Attorney Shao involving a digital collectibles platform accused of fraud, we raised a key question during communications with the procuratorial organ: Did users participate in transactions under misleading circumstances, or did they choose to continue investing after understanding the rules? Around this point, we further introduced the analytical perspective of "investor cognitive state." It was precisely at this level that investigators were prompted to re-examine the transaction model of the case:

—Was deception actually implemented against users, or did users voluntarily participate in transactions despite being aware of the risks?

Ultimately, the case was not characterized as fraud (➡️ Related Reading:Successful No-Guilt Defense in Fraud Case | From Facing Over Ten Years of Imprisonment to Acquittal!）。

2. Is the Platform’s Data Genuine or Fabricated?

A crucial issue in such cases is whether the platform’s data is genuine or artificially fabricated.

In some cases, technical personnel explicitly state that the platform’s K-line trends were connected to real-time market data from a certain exchange, rather than being generated by the platform itself.

If this can be proven, then investors’ profits and losses stem more from market fluctuations themselves, rather than the platform "controlling wins and losses" in the backend, leading to a significantly different legal assessment. At the evidentiary level, one must examine: Can it be proven that the data was accessed in real time? Were there functions in the backend to modify data? Even if such functions existed, is there evidence proving they were actually used to manipulate transaction results?

This point constitutes a significant dividing line in legal characterization.

Conversely, if it can be proven that the data was generated in the backend or that profits and losses could be manually intervened, the nature of the case would undergo a fundamental change.

3. How Were Losses Actually Incurred?

Many family members wonder: Since users suffered losses and reported the case, does this confirm that the platform was engaging in market manipulation, profiting from client losses ("ke sun"), or operating a "rug pull" scheme?

However, in specific cases, we often proceed to further determine: How exactly did the losses occur?

For instance:

• Was there high-frequency trading (frequent buying and selling)?

• Was high leverage used (borrowing money to trade cryptocurrencies)?

• Were there frequent deposits and withdrawals, or chasing rising prices and cutting losses on falling ones?

These factors themselves significantly amplify losses. Even without platform manipulation, long-term high-frequency operations have a much higher probability of loss than profit.

In case files, we even see victim statements such as: "Sometimes I listened to the teacher, sometimes I didn’t, and sometimes I even did the opposite." In such scenarios, it is difficult to claim that the losses were entirely "controlled" by one party.

Thus, it can be seen that there are multiple possible causes for user losses, which cannot be simply equated with being defrauded by the platform.

4. What Is the Composition of the Involved Parties’ Income?

How the involved parties derived profit is also a very important issue.

In practice, we often distinguish: Where did their income actually come from?

For example, regarding platform operators, if their income mainly derives from transaction fees and spreads (the difference between buy and sell prices), this itself is a common profit model for trading platforms, and its nature is closer to providing trading services.

However, if the platform’s main revenue comes from sharing in client losses (i.e., "ke sun"), or even directly retaining client principal, then its profit model has changed, and the legal assessment is more likely to lean towards fraud.

Similarly, for roles such as "instructors," if their income is limited to fixed hourly fees, course fees, or membership fees, it can usually still be understood as providing information or training services; but if their income is directly linked to client losses, such as receiving commissions based on the proportion of losses, or even participating in the distribution of "client losses" after "reverse signaling," their role in the overall chain will be reassessed, and the corresponding legal risks will significantly increase.

For example, a certain exchange previously exposed online openly provided "client loss sharing" to agents, where the mentioned "dividends" referred to sharing client losses (the amount of client losses was split between the platform and agents in a 30/70 ratio); the more users lost, the higher the dividends agents received.

(Image source: Internet)

5. Could Users Withdraw Funds Normally?

This is an easily overlooked defense point: Could investors’ funds be withdrawn normally on the platform?

For instance, in the aforementioned chat records, an agent asked whether the agent would bear the loss if a client won money (meaning the platform lost). The agent suggested that the platform "directly block withdrawals," i.e., restrict user withdrawals.

However, in some cases:

• Investors could freely deposit and withdraw funds;

• some even made profits and successfully withdrew funds;

• even if the platform updated its version, funds could be transferred accordingly.

In such situations, the platform did not impose substantive restrictions on fund outflows, and investors retained a certain degree of control over their funds. Precisely because of this, there is significant controversy in determining whether there was a "purpose of illegal possession." It is difficult to directly conclude that the platform aimed to possess user funds.

It is precisely based on this point that, in practice, cases with seemingly similar models may result in significantly different outcomes.

How Do Courts Assess Similar Cases?

In a virtual currency-related case I handled, although the public prosecution organ accused the platform and related personnel of fraud, the court ultimately did not uphold this characterization.

Judging from the reasoning of the judgment, the core focus was not on superficial circumstances such as "trade-leading" or "losses," but rather revolved around several key facts:

• Existing evidence could not prove that the platform data was false;

• it could not be proven that the defendants could manipulate real-time transaction results;

• the platform did not restrict withdrawals, users could freely deposit and withdraw funds, and there were victim testimonies stating they made profits through trading on the platform.

Under circumstances where these facts could not be substantiated, the key elements of fraud regarding "fabricating facts, concealing the truth" and "purpose of illegal possession" were difficult to establish.

Of course, each case differs, and specific conclusions cannot be simply applied by analogy.

However, this type of judicial reasoning at least indicates that the characterization of virtual currency trading cases does not depend solely on superficial models, but must return to the evidence itself.

In specific individual cases, as long as there is uncertainty regarding key facts, there often remains room for defense.

Conclusion

From a practical perspective, the characterization of such cases is often not a simple question of "constituting a crime" or "not constituting a crime," but depends on a comprehensive assessment of specific circumstances.

Differences among various roles often directly affect the assessment outcome. For example, platform operators, technical staff, business personnel, agents, instructors, salespersons, and even investors themselves may differ significantly in specific communication content, fund flows, methods of participation, and the degree of understanding of the overall model.

If these individual differences are not communicated to investigators in a timely manner and fully elucidated, they are often lumped together as a whole, leading the case characterization in a more unfavorable direction.

Precisely because of this, if a family encounters a similar situation, it is more important not to repeatedly dwell on "whether it is fraud," but to clarify key facts item by item as early as possible—including what specific actions were taken, how one participated, how funds flowed, and whether the overall model was understood.

In many cases, if these issues are not clarified in the early stages, attempting to adjust the direction later often becomes very passive, and even more favorable handling spaces may be missed.