It wasn’t until after the Panama Papers were released that shell corporations (“shells”) gained their high level of notoriety. The Panama Papers revealed Mossack Fonseca’s activities in which the law firm was linked to numerous shell companies owned by politically exposed persons (PEPs) and other noteworthy clients. Notoriety or not, shell companies represent the highest risk customers and present many challenges for financial institutions’ (FIs) KYC and CDD efforts.

Money launderers, fraudsters, and other financial criminals often choose to launder their money through shell companies because of their ability to disguise account ownership and provide ample opportunity for tax evasion and tax avoidance. Larger FIs generally lack an appropriate mechanism to link common red flags or “rules” associated with “shells”, thereby weakening their KYC and CDD efforts.

However, advancements in data science such as artificial intelligence (AI)-powered solutions are currently available to assist FIs in addressing the correlated risk of unidentified shell corporations.

As evidenced by the Mossack Fonseca and other investigations, shells are often formed using the address of the incorporating entity or service. Although this is legal and done for legitimate domestic and foreign business regularly, the common incorporation address is a red flag for compliance teams to mitigate.

Based on QuantaVerse’s collective domain expertise investigating money laundering and large Ponzi schemes, criminals routinely incorporate several shell companies under different names at a common address, opening multiple bank accounts at the same FI. Since each of the “shells” have different authorized signers, the relationships between the shells goes undetected by the FI allowing the money laundering or other scheme to progress.

A good example from an actual case in which the names and data were masked is depicted below:

On January 5, a customer opened a retail account for “XYZ Company” at a Miami bank using the address 123 Main Street, Pembroke Pines, FL and e-mail On February 15, another customer opened a retail account at the same bank in another branch for “TRM Company” using the address 123 Main Street, Pembroke Pines, FL and e-mail The CIP and KYC systems never detected the links and the co-conspirators laundered approximately $2 million through the two accounts in approximately six months. 

AI-powered solutions presently available to FIs offer them the ability to quickly analyze addresses, phone numbers, e-mail addresses, P.O. Box numbers, and other Customer Information Program (CIP) information. AI solutions can instantly determine if a common address or other CIP data has been used for another customer.

Guarding against operational, legal, and reputational risk is a daily challenge for compliance leaders. Using AI solutions to detect and report basic KYC/CIP linkages is a cost-efficient and effective risk mitigation tool for compliance teams.


Recent Posts

What’s Driving AML Compliance Transformation in 2022 and Beyond?

A new report by Aite-Novarica (Aite) examines what’s driving transformation in anti-money laundering (AML) compliance. Specifically, the impact report examines the current AML ecosystem, key trends impacting financial institutions (FIs) and their AML compliance functions, and how they invest in technology and innovation to tackle today’s ever-evolving risk landscape.

read more