Money laundering is a global, longstanding issue that demands attention due to the substantial and widespread problems it can cause. Besides negatively affecting the individuals and businesses falling victim to it, money laundering has broader detrimental effects on society.
Fortunately, money laundering is not an unconquerable obstacle. That’s because the financial industry often uses technology to gauge risk, better serve customers and more. More specifically, evidence suggests that artificial intelligence (AI) is useful for preventing money laundering. Here are several ways it helps.
AI spots suspicious patterns
One of the primary fears people have about AI is that it could take over human jobs and leave people unemployed — unless they can find ways to upskill and benefit communities and companies in other ways.
However, AI does not replace humans who work to prevent money laundering and other kinds of financial fraud. It’s still necessary for people to analyze information and see if it has characteristics that warrant further investigation.
AI could assist bank employees by sifting through large quantities of dataand detecting strange patterns they may miss without help. That’s because AI excels at examining massive amounts of information extremely quickly.As such, financial institutions often deploy AI to increase the productivity of human teams tasked with searching for things that could indicate money laundering occurrences.
AI works efficiently and could help save money
Banks spend billions of dollars to combat fraud and could face hefty fines for not doing enough to check for money laundering and similar schemes. So, many of them conclude that AI helps them save money by avoiding penalties in addition to the savings from better output mentioned above.
Financial institutions realize that investing in AI enables them to reduce or eliminate time-consuming processes. Some AI platforms evaluate tens of thousands of real-time transactions per second. If brands tried to have humans analyze all that information independently, those workers would likely have high error rates and always feel swamped.
Then, banks or similar companies could be fined for failing to notice suspicious things. They may also waste money and time because of the high turnover rates that happen when people feel inadequately supported due to a lack of resources. AI can address all these common issues and more.
AI complements traditional anti-money laundering education
It’s essential that individuals don’t assume depending on AI to fight money laundering means it’s no longer necessary to stay abreast of conventional ways to curb the crime. For example, people who work in the financial sector are often required to take classes that help them recognize the signs of money laundering and other circumstances surrounding potentially illegal transactions.
Using AI does not mean financial institutions should stop mandating that workers attend such training. An all-encompassing approach to money laundering equips financial employees to stay alert to tactics criminals may use. Putting money toward AI screening tools is a worthwhile step in a thorough strategy to cut down on money laundering.
Banks reduce false positives with AI
Financial brands waste precious time chasing false positives associated with money laundering. It’s necessary to assess how many resources to put toward a possible money laundering threat without becoming overextended. Moreover, they cannot afford to ignore alarming signals in case they represent actual problems.
Finding genuine problems within the false issues also becomes harder when money launderers have a variety of ways to accomplish their aims, including methods involving relatively small amounts of money.
For example, a criminal gang of approximately two-dozen members could require all participants to have accounts at one bank. Then, at a specified time, each person would wire $1,000 to a foreign account. Sending $1,000 abroad doesn’t trigger conventional money laundering screening tools because of the small amounts. However, they add up when considering the number of people involved in distributing the money.
Advanced solutions can help banking brands make deeper assessments about events that could constitute money laundering. Automated technologies, including AI, can reduce false positive alerts to less than 50% in cases where incorrect warnings once were at levels surpassing 90 percent.
Plus, analytics suites can find seemingly unrelated things that identify at-risk accounts. They may discover anomalies related to the information itself or its context, either with a single data point or several. The capability of AI to sift through data so quickly and find possible issues within it means financial brands can use it to cut down on false positives while devoting more time to the things deemed overly concerning by the AI tools.
AI equips financial institutions to better examine customers
As mentioned earlier, financial brands are at risk of getting fined if they fail to follow regulatory procedures. One category of such processes, called Know Your Customer (KYC) regulations, require banks to look for high-risk clients and investigate them thoroughly. AI helps the finance industryreduce liability associated with KYC.
It can establish consistency and go through automatic updates as regulations change, for starters. AI can also streamline things on the customer’s end, resulting in more positive and satisfying experiences. According to a 2017 report, 25 percent of customers abandon their applications due to problems stemming from KYC friction and months of onboarding. AI could present unnecessary delays that cause frustration.
AI tools that use natural language processing (NLP) can look at submitted documents in any language and extract the necessary information from them.
Additionally, NLP can automatically classify documents, thereby making it easier for financial professionals to verify they have all the required details to comply with KYC regulations. This is another instance where AI minimizes the dependence on manual processes while still allowing for the necessary scrutiny.
Although AI already shows abundant promise, some of its potential likely remains untapped. That means people should not assume they know all the ways to apply AI to anti-money laundering strategies or that a particular use case is impractical.
Individuals in the financial sector should continue to stay aware of the challenges the industry faces — related to money laundering and otherwise — and assess how AI might help. They also need to understand it may take time to see the full effects of deploying AI to reduce money laundering, especially if algorithms get smarter with ongoing use.