Thailand’s banking sector recently received a jolt when thousands of consumer accounts were abruptly frozen or had funds blocked in the Bank of Thailand’s (BOT) sweeping crackdown on mule accounts. Merchants reverted to cash-only sales, while ordinary people suddenly found themselves locked out of their accounts.

That the BOT had to resort to such a dramatic response highlights the scale of the nation’s scam problem. In Q2 2025 alone, more than 6 billion baht (USD 188 million) was lost to scams. In the first half of the year, 3 million accounts were suspended for suspected mule links to fraud.

A mule account is to scammers what a getaway car is to bank robbers. Without it, stolen money can’t vanish into the shadows to be spent or re-invested into the criminal enterprise. Recognizing this, BOT recently expanded its classification of mule accounts from three categories to five, drawing on victim reports to the Anti-Online Scam Center (AOC) and suspicious transaction reports (STRs) filed by banks to the Anti-Money Laundering Office. 

Willing mules: Selling access for cash

For scammers, recruiting mules is easy — desperation does the work. Vulnerable groups, including the homeless, are lured with promises of quick cash: 800 to 10,000 baht for a single account, or up to 50,000 baht a month for “full-time” cooperation. Since the crackdown began, even corporate accounts have been sold for staggering sums of 200,000 baht.

One ad on social media even offered 5,000 baht plus free food and accommodation in exchange for opening an account — proof of how brazenly this underground trade operates.

Unwitting mules: Deceived into fraud

Not all mules are complicit. Some are ordinary consumers tricked into helping a “friend” or “relative” move money for an emergency, such as a hospital bill. These accounts, used for everyday expenses, become entangled in scam networks without the owner realizing they’ve fallen victim.

When such accounts were suddenly frozen, nationwide panic spread quickly. To return to the robbery analogy: Think of The Italian Job — the Minis darting through alleys. Innocent consumers sometimes find themselves behind the proverbial wheel, not knowing they’re driving a criminal’s getaway car.

From reactive to proactive detection

Mule detection has historically relied on transactional red flags: high velocity of transfers, smurfing, many payors to one account, or amounts inconsistent with the customer profile. While useful, these rules generate noise, overwhelm investigators, and often surface only after the money is already gone.

The industry has since shifted toward proactive detection. Behavioral intelligence — monitoring how an account is operated, not just the transactions into and from it — can flag mule accounts days before the first suspicious payment arrives. By continuously analyzing more than 3,000 behavioral and contextual data points — from how a user types, swipes and navigates through a session, to device, network, application, and transaction metadata — machine learning models can spot anomalies days before the first suspicious transfer arrives.

What this could look like:

• Low data familiarity and unusual user behavior signal the account is being operated by someone other than its rightful owner: willing mules who sold access
• Repeated logins, balance checks, or refreshes indicate anticipation of an incoming transaction: willing “full-time” mule
• Hesitation, corrections, or segmented inputs suggest someone is acting under criminal instruction: unwitting mule

The road ahead

The BOT’s recent crackdown coincides with its directive to set a default daily transfer limit of 50,000 baht ($1,500) by year-end. While designed to protect consumers, the move has unsettled public trust, especially after the sudden account freezes.

The silver lining is flexibility: Banks may still raise or lower limits based on behavioral patterns, just as they can distinguish between “light brown” mule risks and clean accounts. This opens the door to more intelligent, customer-friendly defenses.

Thailand’s war on mule accounts shows that fraud prevention is no longer just about spotting scams at the surface. The battle is also in the back end — where money moves through layers of networked mule accounts, willing and unwitting alike. To keep consumer trust intact, banks must go beyond blunt freezes and reactive models, embracing proactive behavioral tools that shut down the getaway car before it leaves the scene.

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Further reading:

• Thailand’s crackdown on money mule accounts, fraud, and scams
• The forgotten AML gap: How to prevent money laundering with behavioral detection