Risk-Based Approach to AML
Money laundering is the process of taking illegally obtained financial gains and making it appear as if they come from a legitimate source. According to the United Nations Office on Drugs and Crime, this crime accounts for somewhere between $800 billion and $2 trillion of annual global transactions.
Unless you’re Walter White, the process of laundering money probably does not involve an actual washer and dryer, but legitimate (or legitimate-looking) financial institutions who can process the money in a way that “cleans it up” for the criminals who can’t be caught carrying large cash sums.
To list just a few different tactics, money laundering can involve: creating a cash-based business (a “front”) into which money can be funneled; breaking up deposits into small amounts and depositing them into multiple accounts (smurfing), or using a person to transport the cash to a foreign country to be deposited in a foreign account (a cash mule).
The problem we will be looking at here is that many criminals use financial institutions for money laundering without the financial institutions being aware of it. And ignorance of the illegal activity is not usually an acceptable excuse to the governmental authorities. Therefore security measures need to be put in place by financial institutions to avoid having “dirty” money run through their systems, but what can be done? Criminals are a crafty bunch who have all the incentives in the world to not get caught. Plus, avoiding detection is, in many ways, easier now that so much banking is done online.
One solution is a particular kind of Anti-Money-Laundering (AML) tactic known as the risk-based approach to AML which deals with spotting the risk that money laundering may occur before it ever takes place.
What is AML?
In 1970, The United States passed the Banking Secrecy Act which requires institutions to report suspicious activity to the Department of the Treasury using a suspicious activity report. Then, in 1986, money laundering was finally made illegal. These laws, regulations, and procedures that were – and are continuing to be – established make up what we refer to as Anti-Money-Laundering (AML). The fines for not following AML regulations and requirements can be massive (going up even into the billions depending on the situation), so staying compliant has its own incentive.
What is a risk-based approach to AML?
As previously mentioned, a risk-based approach to AML is one that involves identifying suspicious activity and the risk involved in working with certain kinds of clients so as to prevent money laundering before it occurs. Artificial Intelligence and computer learning, monitoring each transaction of every account for suspicious activity, and accurate customer checks are all important aspects of the risk-based approach to AML.
Really knowing your customer when they open an account with your institution is an important first step of the risk-based approach to AML that involves finding out what kind of business they do and what kind of clients they might, and do, have. IDScan.net has the perfect identity verification technology for both in-person and online checks. Reach out to us if you would like to learn more about what we can do to keep your company Know Your Customer compliant for the risk-based approach to AML.
Why is the risk-based approach to AML superior?
A risk-based approach to AML is flexible and allows each company to make adjustments as they see increases in certain kinds of activity or behavior. The risk-based approach to AML does not seek to merely comply with a list of requirements for prevention in a one-size-fits-all manner. Instead, it is proactive and seeks to identify and prevent money laundering from occurring. This does not mean that government regulations do not have to be met, of course. Rather, companies can look at potential clients and use the risk-based approach to AML to answer the questions: “Is this avenue too risky?” And if there is a lot of risk involved, “Can we set up more protections around this particular client or kind of client?”
Facets of a risk-based approach to AML
Setting up those protections can happen in a couple of different ways. Artificial Intelligence and computer learning are heading the charge as two of the most innovative and increasingly effective ways to implement a risk-based approach to AML. For example, many companies are taking advantage of internal cloud storage to capture patterns in data input and then use computers to recognize and alert to those patterns.
Monitoring each transaction is also an important aspect of a risk-based approach to AML as this is where patterns are found. For example, we mentioned “smurfing” earlier which is when someone makes many small deposits in a few different accounts so as not to tip anyone off to the illegal activity. Computers and/or individuals checking in on the transactions would have a better idea of if this is a one- or two-time occurrence or if this behavior makes a pattern.
Lastly, being Know Your Customer compliant is another important facet of a risk-based approach to AML. Verifying with as much accuracy as possible that a customer or client is who they say they are is an important part of preventing fraud, which can make money-laundering easier, but it also helps to establish what kinds of behavioral patterns you can expect to see from this person in the future. Helpful questions include ones about the client’s geography, vulnerabilities, infrastructure and regulations.
Money laundering is the process of taking illegally obtained financial gains and making it appear as if they come from a legitimate source.
AML is the set of laws, regulations, and procedures first established in 1970 to detect and prevent money laundering in the U.S.
A risk-based approach to AML is one that involves identifying suspicious activity and the risk involved in working with certain kinds of clients so as to prevent money laundering before it occurs.