AI financial fraud in Southeast Asia — Korea is next

What was discussed

Frederick Chung has spent more than ten years on financial crime response for banks. He works with the Royal Thai Armed Forces, ASEAN Interpol and Malaysian regulators, and runs a bank consortium in Thailand himself. Introducing himself, he said he liked that Bloom's name runs from AI Blue to Bloom, then added that his own work, unfortunately, is on the AI Blue side.

He opened with a Chinese government public-service ad. A mother is on a video call with her daughter, who says money is tight this month and asks her to send some. Just before she hits send, the doorbell rings, and her real daughter is standing there. On the laptop, someone is still speaking with her daughter's face and voice. That a government makes an ad like this to warn its own citizens tells you how common it is. Then came the number that landed hardest: cloning a voice takes four seconds.

What changed the room's understanding was that the clone is not a whole voice profile, only tone and timbre. Four seconds gets the tone; a real person then speaks through a voice changer, carrying their own delivery in that tone. That is why live deepfake calls feel natural. A person is talking, not a machine. The voices mostly come from social media, and robocalls are used to record answers that are then sold on the dark web. Unless you are a public figure, he said, keep your profiles private. Korea, exporting K-pop and entertainment, has the world's most mature voice-cloning models; Thailand is harder to clone simply because there is less content.

At the flashier end sits deepfake investment fraud: videos of a public figure promoting a coin, seeded across social media and boosted with ads. In Singapore a Zoom call impersonating the prime minister ran live. It is not the most common attack, but it takes the most in one go. The most common is still the impersonation call, government or police or immigration or a company, and the root is data leakage. He got a robocall impersonating Singapore's immigration authority himself, and knew it was fake the moment it asked whether he spoke Chinese. The real authority would not need to ask.

The direction shifted when it stopped being about individuals. A security lead at a large company posted that he was going on holiday, with a public profile. The attacker cloned his voice from his videos, called an employee while he was away, and asked for an OTP because his credentials were locked. The voice matched and the details of where he was matched, so the employee reset it. They caught it soon after, but too late. Ten minutes is enough to plant a backdoor, and the actual breach came through it six months later.

Whether any of this gets detected was the next question. In a controlled environment, detection runs above 99 percent. In the real world it drops to 50 or 60, because network quality strips out the signal detection depends on, which is why fraudsters deliberately hide behind poor networks. The problem is the volume a bank processes daily: everything not filtered out has to be reviewed by a person, and no bank can staff that. Ninety-nine percent is a fine number, he said, but asked whether it is operationally practical, the answer is no.

So he looks at the device rather than the face. In a digital economy, what matters is not the person but the phone they are holding. Model and screen resolution, whether it is jailbroken, whether developer mode is on. Behavioural biometrics sit on top: a payment from a phone face-down on a desk charging is a problem. Hesitation while typing a name counts, because nobody thinks about how to spell their own. Asked whether a bank can tell if the caller is really the customer, he said honestly, no. The ID number and the last digits of the card have all already leaked.

Stopping fraud, he said, is not about fighting fraudsters but about stopping the movement of money. However much is stolen, if it cannot be withdrawn nobody bothers. The problem is that no single bank can do this. The same phone can defraud bank A and bank B will not know. But if A passes the device fingerprint to B, B can freeze the account the moment that phone logs in, and every other account the phone has touched surfaces with it. One device exposes the whole mule network. The UK shares a device-ID fraud list across member banks, and Singapore legislated the sharing of phone and account numbers. In Thailand, 30 people from 11 banks formed the industry's first fraud consortium.

Why banks are only now coming together had a clean answer. Nine years ago he took the idea to a tier-one bank and was told their fraud losses were only $30,000, when a single case in the news had run to millions. He later learned why: the customer had made the transfer themselves, so it was not the bank's liability and never went on the books as fraud. Then Southeast Asian regulators changed the rules so banks bear 50 percent of the loss whether or not the customer was defrauded. The arithmetic changed completely. After an evening of technology, what settled it was accounting.

Asked whether Korean banks are ready, he said they have started investing in detection, and what lags is not the technology but the regulation that would let customer data be used for fraud prevention. Collecting behavioural biometrics and device data is legal in Korea, but banks will not take on the privacy risk. Thailand has already been through this order of events: fraud grew beyond control and the central bank required banking apps to collect identifying data. It takes a large loss to move regulation. In Europe and the US transfers take two or three days, and that delay helps. Korea and Southeast Asia settle immediately, leaving no window to reverse anything.

On the last question, what he most wanted to know about the Korean market, he said he wanted to understand what Korea's real fraud problem is, and added that fraud is moving toward Korea because enforcement across Asia is tightening. It ran large in the Philippines, moved to Thailand when enforcement arrived, and moved to Cambodia when the Royal Thai Armed Forces cracked down. Which means that if it feels like this kind of fraud has not reached us, that may not be because we are safe, only that our turn has not come.

Today a payment is checked for whether a human is behind it. Once AI agents pay on our behalf, the question changes: you have to verify that this is genuinely an agent you own. Handing an agent credentials and permissions opens a new attack surface. People stayed on well after the session ended.