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New report highlights risk of money laundering in Lithuania

| By Daniel O'Boyle
Lithuania’s land-based casino industry faces a “very high” risk from money laundering, while the remote gambling sector faces a high risk, according to a risk assessment carried out by the country’s government.

Lithuania’s land-based casino industry faces a “very high” risk from money laundering, while the remote gambling sector faces a high risk, according to a risk assessment carried out by the country’s government.

Risk levels for each aspect of Lithuania’s gambling industry were determined through “data from a variety of international and national sources”, international studies and reports and statistics provided by a sample of gambling businesses. The country’s Financial Intelligence Unit, supported by supervisory and law enforcement authorities, also contributed to the study.

According to the report, the risk of money laundering for casinos, defined as the “attractiveness and ability of criminals to exploit a specific medium to launder criminally obtained property” was rated as the maximum 4 on the report’s 1-4 scale.

“The casino sector is exposed to risk because the activity is based on a significant number of cash flows, which is attractive for organized crime groups, PEPs [politically exposed persons] and those coming from high-risk countries to launder money,” the report explained. “The Modus operandi is easy to implement as it requires basic planning and basic knowledge of how gambling systems work.”

The risk level for slot machine parlours, betting and online gambling were all rated as a 3, indicating a high risk.

“The online gambling industry is attractive for ML due to the high volume and fast execution of transactions (including cross-border transactions) as well as low identification requirements, which allows criminals to easily convert illegal funds into legitimate gambling earnings,” the report concluded.

For lotteries, the risk was rated as 2, indicating a medium risk.

The report also rated the vulnerability of each medium to money laundering, based on the “totality and effectiveness of measures aimed at preventing the realization of a money laundering risk, taking into account the scale and perception of the risk”. Once again this used a 1-4 scale, with a 4 indicating the most vulnerable.

For land-based casinos, betting and slot parlours, this was rated as a 3 for high vulnerability.

“Casinos allow only cash operations and do not verify the source of funds,” the government said said. “In addition, the sector has difficulties in performing customer due diligence (there is no evidence that all casinos would employ systems to identify related transactions of players), sanction checks and PEP identification.

“Also, some casinos have customers from sanctioned or high-risk countries, such as Iran and Syria. However, casinos are the most aware of money laundering risks in comparison with other gambling sectors.”

The report added that the country’s Gambling Supervisory Authority was ill-equipped to deal with the threat of money laundering in casinos.

“The number of investigations carried out by supervisory authorities during 2016-2018 is insufficient and not proportional to the risk of the sector. This might be caused by the lack of human resources of the supervisory authority, which has only three employees dedicating only 15% of their time for [money laundering and terrorist financing] supervision within the sector.”

The report suggested allowing casinos, slot parlours and betting to use debit cards for more easily traced transactions, setting limits on cash deposits, the usage of player cards to track each player’s gambling activity and “secret shopper” style inspections from the Supervisory Authority.

Remote gambling and lotteries were both rated a 2 for medium vulnerability, though the report noted that some online operators have stricter anti-money laundering requirements than others.

The report marked the second time Lithuania had completed a National Money Laundering and Terrorist Financing Risk Assessment, with a previous iteration in 2015.

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