Explore 5 technology trends in Anti-Money Laundering or AML compliance.
Even though 2021 was a year on a roller coaster ride with pandemic pausing lives and economies, the finance sector was at the dawn of technological innovations.
Banks are slowly adapting to the latest and innovative technologies, such as FinTech, RegTech, RPA, artificial intelligence (AI), and machine learning. These technologies bring a paradigm shift and give rise to tailored financial products, automation, and risk-averting financial predictive models.
Earlier traditional AML monitoring relied heavily on manual investigations, but the rapid penetration of technology makes the changing Anti-Money Laundering or AML compliance requirements challenging.
As a result, banks and financial institutions leave no stone unturned in implementing automated and intelligent systems. The ultimate goal of these automated solutions is to achieve a balance between transparency, effectiveness, AML compliance, and efficiency.
In this article, we explore five technological AML compliance trends and understand some pressing challenges financial institutions face when ensuring AML compliance.
5 AML compliance technological trends
Some key technological trends that are likely to disrupt AML compliance are:
- Adoption of AML software
With 2-5% of global GDP being laundered annually and with the odds of catching cybercriminals against companies, detecting money-laundering activities is critical for business success. But detecting such criminal activities without using technology is next to impossible.
That’s why businesses rely heavily on AML software that helps a business meet all regulatory and legal environments to reduce financial crimes. From monitoring each transaction to ensuring compliance management, this AML software is your best bet and should be implemented as a part of a risk-based approach.
If you need some help with your AML compliance, SEON’s software is an excellent plug-and-play solution. The software offers a storehouse of safety features and provides a real-time identification verification system. What’s more interesting is that SEON’s software sends real-time alerts and monitors users’ actions based on suspected customer behavior.
Having AML software in your kitty can help your business comply with legal regulations.
- Use of Internet of Things (IoT) for KYC
To ensure AML compliance, financial institutions are likely to rely on IoT coupled with AI for completing Know Your Customer or KYC processes. Companies use smart wearables, tablets, and smartphones to create a digital customer portfolio.
It provides information about a customer and gives an in-depth knowledge of their financial behavior.
With banks being able to complete KYC in real-time using a person’s downloaded data, AI software can help financial institutions identify high-risk customers.
With banks paying close to $30 billion in penalties since 2009 because of their inefficiency in stopping financial crimes, implementing safe IoT solutions can help curb the time-consuming collection and validation of every customer’s data.
- Use of robotics for investigation
As the cost of compliance in AML is witnessing an upward trend because companies are hiring investigation teams, companies are finding alternatives to reduce this unnecessary cost.
Enters robotics.
Robotics uses digital robots to automate work. Today, businesses use Robotic Process Automation or RPA that helps in an investigation from start to finish.
Financial institutions can use RPA to perform validation of their customer information by extracting data from documents, accessing databases, merging data from different locations, and collecting social media information. Also, financial institutions can use RPA to compile customer information and provide an overview of the customer’s data.
Another place how RPA is helping in AML compliance is assessing customer risk. An analyst spends many hours gathering data from regulatory bodies and other websites when assessing customer risk. Interestingly, banks can employ bots for sending automated SMS and email about their products, thereby reducing marketing expenses.
One solution to reduce this time would be to deploy bots to collect information about a particular customer from these websites without human intervention. Another robot connects to the AML compliance system and sends a notification to encounter adverse information during this process.
When banks and financial institutions use bots for AML compliance, it leaves an audit trail that helps conduct due diligence.
- Focus on graph analytics and technology
Most AML compliance systems use relational databases that store all customers’ information in columns and rows. While a relational database is excellent for indexing and searching required information, it cannot connect the dots and identify relationships within the dataset.
The problem becomes multifold because analysts have to join several tables to find a potential connection to prevent financial fraud.
That’s where companies are slowly focusing on graph databases. A graph database can store information in nodes instead of tables. Analysts can quickly identify the connection between these nodes, making it a faster and efficient process than running queries across tables joined together.
Graphs can reduce false positives in AML alerts and identify false negatives.
Companies are also using graph technology to automatically extract real-time red flags from different transactions.
The output an analyst gains from graph analytics and technology helps an analyst assess, construct, and fine-tune monitoring rules for capturing unethical activities. This empowers an analyst to uncover complex financial transactions and identify complex money flow through various products.
- Take advantage of machine learning
Unlike traditional computer programming, where a programmer specifies the rules, machine learning is about pattern detection. The system can create, acquire, and learn its own rules based on data and patterns in machine learning.
Also, companies widely use ML to reduce false positives by investigating alerts and transaction monitoring. With most AML compliance alerts resulting in false positives, the ML system can uncover the false positives by studying past and present patterns.
Apart from analyzing suspicious activities, ML systems can classify risks based on critical, high, medium, and low so that companies can take action on high-risk alerts and resolve them without wasting any time.
In the coming years, ML will further increase the efficiency of customer due diligence (CDD) and the KYC process. ML programs are better adept at detecting anomalies in customer behavior, which makes the KYC process smoother and more efficient. These programs only analyze the customer’s action during transactions and, based on that, help a business find financial irregularities.
5 Most Pressing AML compliance challenges
Here are some pressing AML compliance challenges a business might face:
- Digitalization of product and complex payment streams
One major challenge facing financial institutions is keeping up with the ever-changing nature of new financial products. Despite ML, AI, and RPA, many operational and compliance decisions require a company to enter, manage, and organize data manually.
So, maintaining the ultimate balance by choosing the tools that deliver real-time risk analysis is challenging.
- Sophisticated criminals and their records
From rapid digitization, AML threats are slowly increasing, and financial criminals benefit from these innovations and are becoming more innovative.
Even criminals use sophisticated tactics to break into software systems and access critical financial information. Also, tracking virtual currencies is increasingly becoming complex and financial criminals use it as a vehicle for money laundering.
- Changing global regulations increases pressure on AML compliance
The government increases layers of regulation across jurisdictions to find potential money laundering cases and other financial crimes.
Also, as the world has already witnessed some shocking money laundering scandals such as Russian Laundromat, regulators are increasing scrutiny to prevent such instances from reoccurring.
- Increasing volume of data
Another pressing challenge that banks and financial institutions face is the breadth and volume of data. With the world expected to generate 175 zettabytes of data by 2025, financial institutions find it challenging to manage the mammoth of data.
In such scenarios, it’s common for financial institutions to get overwhelmed and fail to harness the true value of the data at their disposal. Interestingly, as companies save transactional data in legacy systems, joining the dots and ensuring risk analysis becomes challenging.
What does the future hold?
Money laundering in any form is complex and unconstitutional. With banks, businesses, and financial institutions being prone to financial crimes because of their global presence and variety of products, markets, and delivery channels, ensuring anti-money laundering compliance is the only choice in a company’s arsenal.
So, in the coming years, the cutting-edge AML compliance department is likely to witness a dramatic but pleasant change.
The way companies investigate suspicious activities, focus on automatic monitoring, and conduct KYC will no longer be the same. Companies are likely to automate their investigation process using machine learning and RPA.
Graph technology and analytics will help banks join the dots and find patterns that help identify relationships between two data sets. Also, many companies are slowly using anti-money laundering software to combat financial crimes stringent laws.
Though this change is likely to put an additional burden on technology vendors and financial institutions, it will stop financial crimes. With risk and opportunities always co-existing, financial institutions need to ensure regulatory compliance while considering customers’ best interests.
Together we can achieve innovation in the AML compliance space.
