The Bank Secrecy Act requires businesses like banks and insurance sellers to follow rules to stop illegal money activities, such as money laundering. Many businesses don’t know about these rules and could face serious problems if they don’t follow them. It’s a good idea for businesses to prepare for these issues before facing big legal troubles, especially since the government is closely watching businesses now.
The Purview of Money Laundering
Money laundering is hiding illegal money, and it’s valued at $1.6 trillion a year, which is 2.7% of the world economy. The criminals are getting smarter at doing this, like in one operation called the Russian Laundromat, which pulled $20.8 billion out of Russia from 2010 to 2014. The police estimate that around 500 people were involved and they’ve found over 100 bank accounts linked to this. These techniques are difficult to trace since the money is invested in the economy by criminals and becomes difficult to track. They aim to clean the money so that it can be used.
Usage of Advanced Tools by Banks
Banks are employing new technology such as AI to detect and foresee money laundering, which is where individuals conceal funds from criminal behavior. This issue affects everyone, including the community and the economy. Banks have to be careful for suspicious transactions, but they sometimes wrongly identify harmless ones, making it more difficult to capture the actual bad ones. Banks therefore have to remain vigilant in order to protect individuals and the economy.
Implementing AML Systems Can Be Challenging
Implementing anti-money laundering systems is challenging for small businesses due to compliance issues. Manually monitoring customers is labor-intensive and costly, while automated AML software can be easily evaded by money launderers. AML software often has a limited number of rules, leading to either too many or too few red flags being raised. Specific rules are needed to help examiners monitor red flags effectively.
Moreover, in 2021, a firm faced an $8 million penalty for excessive suspicious activity notices due to an AML monitoring system issue. Companies are required by law to submit SARs when red flags are detected, failure to do so can result in additional sanctions.
Subsequently to that, research shows that 40% of AML software lacks proper explanation systems for justifying SAR reports. SAR reports can be slow due to the requirement of filing them outside the AML system, which can be a challenge for small businesses under expanded financial institution definitions since 2018.
Technological Solutions for Money Laundering
Cognitive Computing
Cognitive computing is the process by which one can help computers understand us better by sorting through tons of information that, at times, does not make perfect sense. Such would enable something as simple as having our emails answered with personalized messages and help answer tricky questions.
Analysis of Graph
Graph analytics is really useful because it enables us to determine how individuals are related and discover key patterns within the data. It’s good at dealing with complicated relationships and can even identify whether two people who seem really different could potentially be the same individual. Additionally, it helps detect relationships between documents in Asset Management.
Machine Learning Technology
Machine learning technology is concerned with pattern identification and rule generation from data, with significant implications for AML, according to Fair Isaac Corporation. The technology is increasingly being accepted in the financial services industry, with more sophisticated applications that are similar to artificial intelligence. Risk scoring is one of the means through which machine learning can improve the industry by transcending pre-defined rules.
Transaction Monitoring
Transaction monitoring is important for organizations to prevent crime and follow the rules. Older systems often fail to catch suspicious activities, leading to many false alerts every day. Transaction monitoring software helps manage risks and compliance by spotting and reporting suspicious activities. Current systems often struggle to grow and predict issues, making it hard to assess risks fully. Non-money data, like behavior and device details, can provide extra context to transactions that are often missed. Transaction monitoring software is vital for tackling money laundering and managing fraud effectively.
Cloud Computing
Virtual private clouds can simplify how to manage data both within and outside an organization, which is really convenient for AML processes such as getting to know your customer and determining beneficial ownership. With cloud computing, you have easy access to, organize, and improve your data, as well as it enhances risk-scoring due to intelligent systems that improve as they learn from the data. In addition, concerns regarding data security within cloud-based AML systems are being addressed, allowing for easier evasions of convoluted and cumbersome systems.
Robotic Process Automation (RPA)
RPA is an excellent AML compliance tool, allowing software robots to mimic human behavior in a predetermined sequence. This approach addresses system inefficiencies by increasing speed and enabling repetition, with data remaining isolated. Nevertheless, there can be difficulties with exception handling or tasks that involve data analysis and pattern recognition. To succeed in an effective implementation, it’s important to employ state-of-the-art technology, analytics, and data handling that meet the standards and compliances of industries and future alterations.
Know Your Customer (KYC)
Know Your Customer (KYC) or Know Your Business (KYB), also referred to as Identity Verification, is the first step in learning who a customer is. It helps in risk management and combating money laundering. Companies must validate a customer’s identity before proceeding. The process of KYC is to carefully screen customers and assess the risk of transacting with them when they open an account, and in follow-up transactions. It’s crucial to protect businesses and good customers without overcomplicating it. The right solution allows for differentiating good customers from fraudsters, builds trust, prevents fraud, and complies with the regulations.
Customer Due Diligence Tool (CDD)
A web-based tool that allows users to input and score data for all existing and new customers and accounts in order to comply with Know Your Customer (KYC) regulations. It also captures additional information on customers and accounts, including who actually owns them, officers or directors of the company (for non-persons and financial institutions), power of attorney, co-signatures, and more involved.
Key Challenges for Anti-Money Laundering Systems
Banks handle a lot of information, like rules that require them to know their clients. As clients do more online activities, this job becomes harder. Banks are now providing more online services because banking transactions have increased a lot in the last ten years. Criminals often use wire transfers, which makes it tough to keep track of these transactions.
The 2020 Anti-Money Laundering Act added new rules to help with this issue, like new reporting requirements. However, these rules make things more complicated because money laundering often involves banks from different countries. Fraud prevention teams also find many false alarms, which slow down their programs, cause mistakes, and lead to unhappy customers due to delays in service
AML AI Benefits
- Artificial intelligence solutions enhance the detection of threats by processing enormous amounts of information, identifying patterns, and acknowledging anomalies, and hence making it harder for offenders to alter fraud patterns.
- AI-powered AML systems can lower operating costs by decreasing the volume of false-positive alerts that must be reviewed. Most alerts can be cleared at a root level, saving staff resources, with 90% to 95% not needing to be investigated further.
- AI facilitates compliance and governance by stopping money laundering activity before it attracts regulatory attention. In the UK and US, there are designated regulators that inspire banks to utilize AI in their anti-money laundering systems. Banks are now applying AI and machine learning for risk identification tests.
Downside of AML in Technology
Money laundering starts when illegal money is deposited in a bank. This leads to a complicated process of moving money between banks and businesses, making it hard to trace where the money came from. The money goes through many middlemen and companies, making it tough to know who actually owns it. To fight money laundering, banks spend about $40 billion every year to outsmart criminals. Criminals are getting better at money laundering using technology and online markets, thinking they won’t get caught. As countries move to faster payment systems, it becomes easier for criminals to quickly transfer illegal money across borders.
New types of money, like bitcoin, are also used for money laundering. In 2019, $2.8 billion of illegal money was laundered with bitcoin, up from $1 billion the year before. Bitcoin exchanges need rules to know their customers and prevent criminals from using their services while protecting honest users.
Technology can help companies stay ahead of criminals. AI and machine learning can spot patterns in illegal activities, and companies that use these tools are already seeing benefits. While AI isn’t new, recent improvements have made it better at making decisions than humans in many situations. In the future, businesses will use AI along with cloud technology to be more accurate, efficient, and safe.
However, to use these technologies safely, we need good laws, proper rules, and understanding of the pros and cons of these technologies by everyone involved, including banks and regulators. Technology itself is not good or bad; it depends on how people choose to use it.
Conclusion
The anti-money laundering (AML) rules and Bank Secrecy Act (BSA) require banks to fight money laundering and protect the economy. AI and machine learning technologies improve AML systems by processing patterns and eliminating false positives. Small businesses do not find it easy to implement these systems and are forced to find a balance between automation and manual screening. With the developing money laundering tactics, financial organizations must stay vigilant and take prophylactic action to be compliant and protect the economy and the community.
