Anyone familiar with the banking industry knows that money laundering, financing of terrorist / suspicious activity issues never abate. Regulators periodically issue new decrees, introduce reforms to combat these pesky yet very expensive violations that can cost banks, anywhere from millions to billions in penalties.
Wachovia, now part of Wells Fargo was accused of facilitating Mexican drug cartels through accidental wiring of more than $400 billion over 2004-2007 period. According to original estimates by United Nations Office on Drugs and Crimes, 2%-5% of global GDP or $800 billion USD to $2 trillion is laundered each year across the globe.
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In recent times, many solution providers have claimed embedding AI in their Anti Money Laundering (AML) offerings. Upon conducting further research, it has become apparent that most enterprise level solution providers are supplementing existing AML solutions with predictive analytics, dashboard overview while very few are actually offering AI enabled real-time monitoring capability or agile solutions that respond to future anomalous behavior.
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In uncertain and precarious times, most enterprises split into one of these two camps.
Those wanting to capitalize on lurking opportunities at any cost (arbitrage) and those wanting to retain customers while carefully helping them navigate through uncertainty. Which approach businesses adopt, is strictly their prerogative as the ultimate objective of any business is to maximize profit for their shareholders, ideally by keeping interest of all stakeholders (such as customers) in mind.
In dire times, it is not uncommon for enterprises wanting to fend for themselves but also wanting to capitalize (even if unfairly, unethically) on imbalances in demand and supply. To combat exploitation, unfair opportunistic advantages, consumers look up to regulators and compliance officers.
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