Aaron Ricadela | Content Strategist | August 28, 2024
Money laundering refers to the ways individual actors or criminal groups inject proceeds from their illegal activities into the global financial system to make them look like they were legitimately earned. US banks spend about $25 billion a year on processes to fight money laundering, and fines levied on banks worldwide for failing to prevent it topped $6 billion in 2023.
Criminals are getting more sophisticated in evading controls, while banks are finding it tough to pinpoint real money laundering actions because the great majority of alerts their tracking software raises for investigation are actually connected to benign transactions. Those false positives waste effort and money.
Now, financial institutions are starting to supplement or replace anti–money laundering (AML) software based on predefined rules with more sophisticated AI-powered software. This software is better at finding hidden patterns in transactions and relationships among people and companies, more thoroughly screens for suspicious activity, and more effectively scores customers based on their risk of money laundering. The result can be fewer false positive alerts, greater protection from illegal actors and regulatory fines, and lower compliance costs.
Artificial intelligence is a set of statistical techniques that lets computers see relationships, make deductions, and predict scenarios based on patterns learned from large amounts of data. Financial services companies are using AI techniques to automate back-office processes, including fighting credit card fraud, personalizing product offers, making recommendations to sales teams, and combating money laundering.
Traditional rules-based systems that look for warning signs of criminal activity or suspicious transactions based on preprogrammed patterns are yielding to AI-based systems that can detect the behavioral hallmarks of money laundering. Historically, AML software has looked for red flags that could indicate criminal activity, as well as complementary information such as a bank customer’s appearance on an international sanctions list, bank deposits just under the threshold requiring reports to the government, or transfers of amounts out of an account that are similar to those recently paid in.
The challenge is that criminals employ evolving tactics to launder their proceeds in what appear to be legitimate financial transactions. In addition to setting up shell companies to make ownership harder to trace, they invest in existing companies that do most of their business in cash and then inflate their revenues. They also deposit their cash in small amounts across several financial institutions and funnel cash through countries with lax regulations. That means that traditional AML methods are often ineffective while generating a very high number of false positives that can cost banks upwards of tens of millions of dollars a year.
AI-based systems can detect hidden transaction patterns among networks of people, compare behaviors with those that are historically common for an organization or its peers, assign risk scores to customers based on their past activity and Know Your Customer (KYC) information, and triage events to close investigations that are low risk. Fraud detection for transactions, electronic payments to vendors, AML, and KYC are among the top five AI use cases in financial services, according to research from AI chip maker NVIDIA.
Key Takeaways
Banks are under intense pressure to root out increasingly sophisticated money laundering techniques and avoid steep fines while keeping their regulatory compliance costs under control. By replacing rules-based software tools with AI-based AML applications, banks can improve their identification of suspicious activities by up to 40%, McKinsey & Company reports, while substantially reducing their number of false positives.
AI approaches include applying machine learning to scoring customers to predict their propensity to perpetrate a financial crime. AML applications also use unsupervised learning, in which a machine learning system isn’t shown labeled examples and gleans relationships itself from raw data, to identify changing customer behaviors and more accurately capture risk. AI systems can incorporate models of expected behavior, which flag deviations and replace fixed rules. AI-based AML tools also triage rules-based scenario events to automatically close or deprioritize low-risk investigations.
When businesses or individuals want to open a bank account, banks perform risk assessments. That includes asking prospective customers a series of questions about their work, residence, income sources, and how they plan to move money. Banks also make sure prospective customers aren’t on international sanctions lists that would prohibit them from transferring funds. They also need to determine whether each individual is a so-called politically exposed person—a political figure or family member or close associate of one—which subjects them to more scrutiny. Banks then undertake KYC processes and score potential applicants for risk of money laundering or fraud.
The trouble is, some of the information depends on customers answering honestly, so financial institutions need automated ways of seeing if customers’ actual banking activity deviates from their stated intent. Traditional AML controls look at transaction data, including the international movement of funds, whether transactions of similar amounts are moving quickly among accounts, and whether customers have broken down large transactions into smaller ones. Criminals often move money to accounts in different countries whose AML regulations are less rigorous than where they reside. Another difficulty is that these behaviors can also have benign explanations.
AI-based systems are better at analyzing data to find patterns that human analysts and risk controllers alone can’t. The software can apply behavioral risk scoring to predict a customer’s propensity to commit a crime, run predictive models to show whether level-one investigations can be safely closed without escalation to more specialized teams, and simulate money laundering to assess the effectiveness of transaction monitoring systems. This can reduce the number of alerts that don’t signal actual money laundering activity, helping reduce compliance costs. Generative AI technology can help banks summarize initial assessments of risk and draft suspicious activity reports for law enforcement.
Commonly used AI techniques in AML include deep reinforcement learning, generative adversarial networks (GANs), and graph neural networks (GNNs). GANs generalize from examples of money laundering learned from training data to find modified patterns as criminals adjust their approaches. GNNs look for relationships among people and entities learned during training, including previously unidentified ones. That analysis helps banks spot money laundering activities involving groups of criminal actors. Deep reinforcement learning can teach AI models to learn about new relationships among data points by teaching the system to seek positive feedback for making the right decision. Models can then adapt their transaction monitoring to changing strategies.
AI-based AML systems can learn as they go. For example, if AI software finds transactions that share characteristics and are very unlikely to be money laundering, it can make recommendations for changes to the master system that would let similar transactions through going forward.
A 2022 Bank of England report on AI concluded that “one of the reasons AI is important is that it can enable new use cases”—for example, tackling the problem of synthetic identity fraud, in which criminals create identities “from a jigsaw of real data…which may be difficult to identify by human analysts.” AI-based AML systems can also employ unsupervised neural networks to look at very broad sources of data, including computer IP addresses and patterns of behavior, to generate alerts.
Traditional AML systems need to be adjusted for optimal sensitivity to activity that could raise red flags. Generating too few alerts risks missing criminal activity—and drawing attention and fines from regulators. Too many alerts can swamp banks’ compliance staff, who need to review each flag and decide how to act on it. AI systems have shown they can generate nearly the same number of suspicious activity reports (SARs) but with a greatly reduced number of false positives. Read on for more on these and other AI benefits.
Applying AI to fight money laundering won’t work well if banks don’t have enough high-quality data to train models to deliver consistently accurate results. Banks also need to make sure they have the right talent on staff to train, fine-tune, and maintain AI models, and they need to consider customers’ data privacy when designing systems. AI can also be opaque: It’s not always clear how a generative AI system arrives at its answers. McKinsey advises banks to meet with regulators well in advance of developing an AI-based AML system. Read on for more on these and other AI limitations.
Banks are applying AML AI techniques when bringing customers onboard, monitoring their banking activities, and reporting suspicious behavior to authorities. The software can make processes faster and more effective by building behavioral profiles from sometimes-hidden patterns to better classify transactions, scouring documents and news for potentially risky customers, and speeding regulatory report writing.
Banks that want to redesign AML processes to incorporate AI techniques should first assess their data strategy, including the data they have. They need to consider how AI can be used across departments and workflows handling KYC, customer onboarding, and anti–money laundering. The resulting systems need to be assessed in context for suitability and evaluated for regulatory compliance. Read on to learn more about these and other steps.
AML requirements set by global authorities are evolving, while banks’ AML budgets are under pressure, making AI-powered analysis and automation more attractive. In the US, the Treasury Department's Financial Crimes Enforcement Network (FinCEN) is considering rules that would extend the Bank Secrecy Act to investment advisors, including requiring them to file SARs. Swiss regulator FINMA has been ordering banks to conduct more thorough AML reviews, and the EU’s new Anti–Money Laundering Authority is expected to introduce more direct supervision of up to 40 financial institutions.
Banks may look to double down on new AI-enhanced methods of fraud detection, as hiding money behind high-end cars, collectibles, jewelry, and art has made the practice harder to trace. Money launderers also use social media to recruit low-ranking workers to deposit money, making nefarious activity harder for traditional systems to root out.
Oracle Financial Crime and Compliance Management Cloud Service includes software engines that dynamically score entities and transactions for risk and freeze transactions to sanctioned entities or countries for immediate review by an analyst. It also has a full-featured case management capability designed for the way investigators work.
Oracle Financial Services Compliance Studio includes statistical analysis and supervised and unsupervised learning AI technology, which helps better understand and monitor risks and reduce the costs of maintaining a platform for compliance and countering financial crime.
Oracle’s approach helped one large multinational bank implement AI models within six weeks and generate 45% to 65% fewer alerts while still producing at least 99% of the number of suspicious activity reports it had when its alert volume was much higher.
Oracle Financial Services Compliance Agent is an AI-powered cloud service that lets banks test their transaction monitoring systems and simulate bad actors to stress test their AML programs, helping to reduce costs and regulatory risk. Oracle is also developing a generative AI component for its financial crime software to help write case narratives for reports.
Will AML be automated?
Banks are increasingly automating their AML processes using AI tools that can gather and process data from across departments. These tools complement and support the analysts and other workers who are responsible for the AML function.
What is generative AI in anti–money laundering?
Banks are using GenAI to look for related terms that aren’t hard-coded into the rules engines of traditional AML software, to identify tough-to-spot relationships among transactions, and to create narratives for suspicious activity and other reports.
What is intelligent automation in AML?
Intelligent automation is used to reduce the manual work involved in reviewing transactions an AML system incorrectly flags as fraudulent. It does that by applying new patterns the AI model learned to classify future transactions. This can help lower banks’ costs and improve accuracy.
注:为免疑义,本网页所用以下术语专指以下含义: