In previous posts, we’ve examined specific instances of how artificial intelligence (AI) could be used to thwart global financial crime, and how the use of AI-derived insights by financial institutions could dramatically improve their overall compliance programs.
Preventing financial crimes will not only save the banking industry millions, if not billions, of dollars while keeping regulators happy, but it would save scores of lives and improve our global economy through the adoption of new technologies.
Money laundering is not a victimless crime. The money generated from immoral crimes is often laundered through our global banks. The “clean” funds are then used to support the lavish lifestyles and future crimes of those responsible for destroying the lives of millions. Consider that:
- Drug traffickers create an average $100 billion of illicit cash flows from the estimated six million people in the U.S. who are addicted to illegal drugs.
- Human traffickers made $150 billion in 2016 from the estimated 20 million humans trafficked globally, 80 percent of which are trafficked for the sex trade.
- Small money movements utilized by terrorist organizations, such as ISIS, for their operational use of “foreign fighters” often go undetected by the banks current transaction monitoring systems (TMS). Numerous reports over the past several years have documented that many terrorist attacks that have occurred in Europe have cost under $10,000.
NGOs, such as Addiction Policy Forum, Polaris and Liberty Asia, are actively working to putting an end to these vicious crimes, but we can all do more.
For instance, an investigation recently released by The Washington Post and 60 Minutes is a high-profile example of the widespread discussion around the role government and the pharmaceutical industry plays in curtailing and equally enabling the opiate crisis.
Discussion ranges from the need for tougher prosecution of opiate distributors and careless pain treatment centers, to offering affordable and improved treatment for those suffering from addiction. Yet, these debates routinely stop short of the role of the illicit supply operations (drug cartels) and economic realities of having cheap heroin and other opioids readily available.
This is where the banking regulators and financial industry must be called to the battlefield, making it more difficult for illegal drug operations keep heroin prices low and from easily enjoying their ill-gotten profits with impunity.
Estimates from The World Bank indicate up to five percent of global GDP is laundered annually through banks, equaling close to $2 trillion dollars. Yet, per the United Nations Office on Drugs and Crime (UNODC), less than one percent of global illicit financial flows are currently seized by authorities.
To add insult to injury, many financial criminals have perfected their ability to go undetected within our global banking system, and have familiarized themselves with the processes and legacy technology, such as TMS, utilized by compliance teams and anti-money laundering (AML) investigators to thwart criminal efforts.
Smart money launderers know the rules commonly programmed into TMS and, therefore, know how to avoid them. In fact, an estimated 50 percent of financial crimes trafficked through the banking system pass through TMS undetected.
Regulations on the topic continue to be rolled out, at the state, national and global levels. These include NYSDFS 504, FinCEN 5th pillar and the EU Funds Transfer Regulation. Despite these regulatory efforts, turning the tables on financial criminals requires more relentless, penetrating scrutiny.
It’s reasons such as these that Congressman Ed Royce (CA) has recently proposed to strengthen the anti-money laundering (AML) and countering terrorism financing (CTF) system in the US. The draft legislation, the Anti-Money Laundering Modernization Act, seeks to modernize the system by improving information-sharing within banks and, more importantly, calling on the use of AI and machine learning by regulators.
Advancements in data science such as AI and machine learning were born to fight global crime and to stop financial criminals in their tracks. AI-based solutions can easily analyze massive amounts of data, and quickly identify where exceptions or anomalies exist, unveiling sophisticated intelligence networks which are utilized as a flow of communication between fellow fraudsters and money launderers.
AI systems have progressed to the point where large volumes of transactional and other sources of financial data can be culled, consolidated, analyzed, and scored for risk so that investigators can make more accurate determinations of suspicious activity.
No financial institution wants to be used by criminals seeking to further their illegal and immoral money laundering schemes. The time is now for our legislators, regulators, and covered institutions to adopt the emerging technologies of AI and machine learning in the fight against global financial crime.
Packaged financial crime solutions are built from the ground up by proven AI experts working alongside experienced AML professionals. Unlike other AI for AML approaches, packaged AI solutions are quick to implement and are continuously updated by the solution provider based on input from multiple customers and regulators.
Serving almost 20 million customers, the bank was concerned about the risks associated with false negatives that its current AML compliance technology was missing. Intent on driving financial crime out of its operation, the bank began searching for a solution that could enhance its existing rules-based transaction monitoring system (TMS) and minimize the risk related to undiscovered financial crime.
While all agree the promise of AI for AML is great, taking the steps to select and implement AI within an institution is relatively new. So, what is the best approach for implementing AI and machine learning into your efforts to combat financial crime?