Family members involved in a cryptocurrency scam? First, check out these 5 critical questions that could determine life or death.

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Author: Attorney Shao Shiwei

For family members of a criminal case, when a loved one is suddenly filed for investigation under a virtual currency fraud case, they are often at a loss.

On the one hand, the case itself involves specialized content such as virtual coins, platform trading, and leading clients (“front-running” or “coaching”), which makes it hard to understand right away;

On the other hand, the feedback from the outside is often rather simple—“This is basically fraud.”

But when handling such cases in practice, it turns out that this is not a simple single act by a particular individual; it is often a chain with clear organization and division of labor:

There is a platform person in charge, responsible for overall system build-out and fund operations;

There are technical personnel, responsible for system development and maintenance;

There are business personnel, responsible for external promotion and developing agents;

There is an agent team, responsible for recruiting people and converting clients;

And there are also lecturers—coaching teachers—who guide trading in the livestreams or online communities.

From the outside, these roles seem to revolve around operating the same platform,

but when it comes down to specific individuals, the specific steps each person actually participates in, the information each person has access to, and their understanding of the overall model are often completely different.

And it is precisely because of this that, in specific cases, not everyone is evaluated in the same way, and it is impossible to simply treat everyone as fraud.

However, for the parties involved, they often can only see the part of the work they are responsible for. They do not understand the overall structure, and it is difficult for them to determine how their conduct will be judged under the law. It is even harder to come up with targeted defense ideas in the first instance.

It is also under these circumstances that many cases appear to have already been characterized on the surface, but in individual cases, there is still room to fight to varying degrees—including obtaining a finding of no criminal liability, reducing the offense to a lighter crime, and even the possibility that the conduct does not constitute a crime.

Based on Attorney Shao’s experience in handling cases like these, below, from several key dimensions, we provide some ways of thinking for family members who encounter virtual currency fraud cases as reference.

1. Five key questions that determine the direction of a case

From case-handling experience, whether such a case will be recognized as fraud often depends on a comprehensive assessment of several core issues.

1. Has the user been deceived by the platform?

In judging these cases, we first need to go back to the starting point—whether the user’s (investor’s) investment behavior was caused by deception by the platform, agents, or other implicated persons.

In practice, we usually determine the investor’s real state of understanding from the following aspects:

Length of time in investing. If the investor has been participating in trading for as long as one or two years, or even longer, then they typically have a fairly substantial understanding of the platform’s operation model, fund flows, and risk characteristics. It is hard to say that throughout this long period, they have continuously been in a state of “being deceived.”

Whether there is a record of profits. If the investor has never made a profit, or after making profits they are unable to withdraw, then the signs of deception are more obvious. But if the investor has once profited and successfully withdrawn, that indicates the platform is not “take in only and never allow out,” and the investor’s subsequent losses may be the result of continuing to participate in trading, rather than being caused by platform fraud.

Whether they can make autonomous decisions. In many cases, we can see that in the investor’s transcript they mention: “Sometimes I didn’t listen to the leading teacher’s advice. He advised me to buy up, so I bought down.” This shows that the investor is not mechanically executing the teacher’s instructions; they have independent judgment and the ability to make autonomous decisions.

If many people did it for one or two years and even had profits, but only after finally incurring losses do they think they were “deceived.” In judicial practice, this is exactly something that defense lawyers need to重点提醒 case handlers to pay attention to.

For example, in a digital collectibles platform fraud case previously handled by Attorney Shao, when communicating with the procuratorate, we focused on posing a question: Was the user participating in trading under misguidance, or did they choose to continue putting in funds after understanding the rules? Based on this, we further introduced an analytical perspective of the “investor’s state of cognition.” It was precisely at this level that prompted case handlers to reexamine the trading model of that case:

—Was deception carried out against the user, or did the user, despite knowing there were risks, still voluntarily participate in trading?

Ultimately, that case was not recognized as fraud (➡️ Related reading: A successful fraud acquittal defense case | From facing more than 10 years of imprisonment to a no-guilt closure!).

2. Are the platform’s data true or false?

A very key issue in these cases is: Are the platform’s data actually real, or are they artificially made up?

In some cases, technical personnel explicitly explain: the platform’s K-line trends are real-time market data sourced from a certain exchange, not generated by the platform itself.

If this can be proven, then the investor’s profits and losses would come more from the market’s own fluctuations rather than the platform “controlling wins and losses” in the back end, and the assessment of the case would differ significantly. When落实 to the evidence level, you need to check: can it be proven that the data was connected in real time? Is there a function in the back end to modify data? Even if such a function exists, is there evidence showing that the function was actually used to manipulate the trading results?

This point is, in terms of characterization, a very important dividing line.

On the other hand, if it can be proven that the data is generated in the back end, or that profits and losses can be manually interfered with, then the nature of the case will undergo a fundamental change.

3. How did the losses actually arise?

Many family members wonder: if users have losses and file a report, does that mean the platform really had a “house” position, “ate the victims’ losses,” or even a “píxì” pan (a scheme)?

But in specific cases, we often further determine: how did the losses actually occur?

For example:

Is there high-frequency trading (frequent buying and selling)

Is high leverage used (borrowing money to trade coins)

Are there frequent entries and exits, chasing rallies and cutting losses

These factors themselves significantly amplify losses. Even without platform manipulation, long-term high-frequency trading has a much higher probability of losses than profits.

Even in the case file, we may see statements from the victim: “Sometimes I listened to the teacher, and sometimes I didn’t. Even worse, I operated in the opposite direction—so it’s hard to say that the losses were entirely caused by one party ‘controlling’ them.”

From this, we can also see: there may be many possible reasons for a user’s losses, and they cannot be simply equated with being defrauded by a platform.

4. How is the income of the implicated persons constituted?

How the implicated persons profit is also a very important question.

In practice, we often distinguish where their income comes from.

For example, for the platform side: if its income mainly comes from trading fees and spreads (the difference between the buy price and the sell price), then this is a common profit model for trading platforms; in nature, it is closer to providing trading services.

But if the platform’s main earnings come from sharing in customers’ losses (i.e., “client loss”), or even if it directly intercepts customers’ principal, then its profit model has already changed. In assessment, it is more likely to lean toward the direction of fraud.

For example, for a role like a “lecturer,” if its income is limited to fixed course fees, course fees, or membership fees, it is usually still understandable as providing information or training services; but if its income is directly tied to customers’ losses—for example, taking commission based on the loss proportion—or even participating in sharing the “client loss” after “reverse call orders,” then that person’s role within the overall chain will be reevaluated, and the corresponding legal risks will obviously increase.

For example, in a certain exchange that was previously exposed online for publicly providing “client loss sharing” to its agents, it mentions that the “dividends” referred to are actually client loss sharing (the amount of customers’ losses, shared by the platform and agents in a 37 proportion). The more users lose, the higher the share the agent takes.

(Image source: online)

5. Can users’ money be withdrawn normally?

This is a defense point that is easy to overlook: whether investors’ funds can be withdrawn normally on the platform.

For example, in the chat records mentioned above, the agent asked, if the customer wins money (the platform loses), does the agent need to bear the loss? The agent suggested that the platform “directly freeze the withdrawal,” i.e., restrict users from withdrawing.

But in some cases:

Investors can freely deposit and withdraw funds

Someone even made money and successfully withdrew

Even if the platform changes versions, the funds can be transferred along with it

In such circumstances, the platform did not impose any substantive restriction on fund outflows, and investors still had a certain degree of control over their funds. It is precisely because of this that, when determining whether there is an “intent of illegal appropriation,” there will be significant controversy. It is hard to directly conclude that the platform’s purpose was to appropriate users’ funds.

And it is also based on this that, in practice, you can see situations where the surface model appears similar, but the handling outcomes differ clearly.

2. In similar cases, how do courts make their judgment?

In one virtual-currency-related case I came into contact with, although the prosecution accused the platform and related persons of constituting fraud, the court ultimately did not recognize it.

According to the reasoning in the judgment, the core focus was not on the superficial situations like “leading clients” or “losses,” but rather on several key facts:

Existing evidence cannot prove that the platform data is false

It is impossible to prove that the defendant can manipulate the real-time trading outcomes

The platform did not restrict withdrawals or other actions; users could freely deposit and withdraw funds, and there were also victims’ statements that they were able to profit through platform trading

With these facts unable to be proven, the key elements in fraud—“fabricating facts and concealing the truth” and “intent of illegal appropriation”—are difficult to establish.

Of course, each case has different circumstances, so you cannot simply apply a specific conclusion.

But at least this type of adjudication approach shows that the characterization of cases involving virtual currency trading is not only about looking at the surface model; you have to return to the evidence itself.

In a specific case, as long as there is uncertainty regarding key facts, there is often still room for defense.

3. Conclusion

From a practical perspective, the characterization of these cases often is not simply a question of whether it is “constituting a crime” or “not constituting a crime,” but depends on a comprehensive assessment of the specific circumstances.

Differences among different roles often directly affect the outcome of the assessment. For example, the platform side, technical personnel, business personnel, agents, lecturers, salespeople, and even the investors themselves may have clearly different circumstances in the specific communication content, fund flow direction, modes of participation, and their level of understanding of the overall model.

If the differences among these individuals are not communicated to the case handlers in a timely manner and fully clarified, they are often treated as a single whole in a one-size-fits-all way, which can cause the case’s characterization to move toward a more unfavorable direction.

Therefore, if a family encounters a situation like this, more important than repeatedly agonizing over “whether it’s fraud” is to sort out the key facts item by item as early as possible—including what was done specifically, how they participated, how the funds flowed, and whether they understood the overall model, etc.

In many cases, if these issues are not clarified in the early stage, it often becomes very passive to try to adjust the direction later, and may even miss more favorable possibilities for handling.

Special Statement: This article is an original work by Attorney Shao Shiwei. It only represents the author’s personal views and does not constitute legal consultation or legal opinions regarding any specific matter.