Understanding Anti-Money Laundering Using AI
With the discovery of artificial intelligence and its subsequent implementation across many industries, a huge opportunity for businesses providing banking and financial services has entered the stage. One of the biggest challenges for banks is to fight financial fraud and money laundering, which is why understanding anti-money laundering using AI is so important.
Anti-Money Laundering Costs & Inaccuracies
Anti-money laundering (AML) requires a lot of investigation and accurate reporting, which is mainly performed by compliance professionals with the assistance of software. Unfortunately, it’s been reported that the false positive rate for suspicious activity at most banks is 95-99%. This means that only 1-5% of reported suspicious activity is the result of actual money laundering efforts.
Due to these large false-positive rates, the average AML compliance cost for banks and other financial firms can be quite high. A study by LexisNexis shows that the cost of AML compliance depends mostly on the size of the assets under management (AUM):
- $1 billion or less AUM: $850,000 AML cost
- $1-100 billion AUM: $7.4 million AML cost
- $100 billion or more AUM: $15.8 million AML cost
Considering the vast majority of those AML costs are completely wasted, banks that explore anti-money laundering using AI through experimentation and innovation can cut costs dramatically. Cutting costs and reallocating resources can bring major advantages to any institution, especially those participating in the financial services industry.
So, how can artificial intelligence help a bank’s bottom line by improving their AML efforts?
3 Major Applications for Anti-Money Laundering Using AI
With AI being so new in the financial services industry, we’re just now cracking the surface of what’s possible to implement. However, there are some clear applications for anti-money laundering using AI that show major promise.
1. AML Alerts and Reports
Automating AML alerts and reports so compliance officers can have a clear view of the current and potential money laundering risks is massively important. Through the use of AI, it’s possible to provide automatic alerts that are more accurate resulting in improved speed and a reduction of a bank’s false positive rate. AI also can support investigative processes through better data interpretation and link analysis.
Giving AML compliance teams the power of AI means they can focus their energy on interpreting and reporting instead of worrying about false positives and inaccurate data. This can be a huge win for banks looking to cut costs on inefficient operations while freeing up their employees for more important, value-added tasks.
2. Client Risk Profile
Keeping accurate and up-to-date client risk profiles can be a major challenge for banks due to the vast number of clients and all of their unique activities. When classifying a client as low, medium, or high-risk, this information must not only be timely but also true. Artificial intelligence is capable of classifying all sorts of phenomena due to its flexibility and ability to handle large amounts of data.
Ensuring a bank’s clients are properly classified throughout the client life cycle is both a compliance requirement and a job that can be performed using AI. By training an AI agent to understand the proper risk classifications to categorize a particular client at any given time, an immense amount of resources within a bank’s compliance team can be freed up.
3. Ultimate Beneficial Ownership
Weaving through complex webs of data on shareholders, directors, associates, and beneficial owners can be a huge task for compliance teams. Artificial intelligence, however, can swim through this data with ease and extract incredibly insightful and actionable data resulting in more accurate risk assessments regarding an individual as well as organizations.
This has been one of the most challenging aspects of AML compliance due to the global financial system becoming more accessible to people as well as the emergence of cryptocurrencies. Linking actions and finding patterns that undercover true ownership can be incredibly costly especially if performed incorrectly. Artificial intelligence can uncover illuminating information from nuanced, complex data that’s highly interlinked across many domains.
The Role of Natural Language Processing
Using AI to improve efficiencies and cutting costs when confronted with the many challenges of AML means combing through a ton of complex data. Not only that, but this data commonly comes in the form of text and sometimes voice. Being able to properly analyze this data means choosing the right AI tools for the job, which in this case is natural language processing (NLP).
Natural language processing is a field of study in AI that seeks to improve the ability of computers to process and analyze large amounts of data derived from human languages. With the help of deep neural networks (DNNs), NLP has become a powerful technology used to better understand text and speech. Popular DNN architectures for NLP include recurrent neural networks (RNNs) and long short-term memory networks (LSTMs).
The power of NLP is that it can process very large amounts of data and to discover complex patterns within natural language that could have been difficult to find otherwise. People, along with more traditional analytical techniques, are in no way capable of producing actionable information derived from this type of data at the level AI can.
One of the ways NLP can help in AML is detecting strange banking transactions on a global scale. By learning from the innumerable transactions performed by clients around the world, AI can alert AML compliance teams of irregularities and anomalies as well as observable patterns commonly found among money launderers.
Another way NLP can improve the effectiveness of a bank’s AML compliance team is by detecting unusual interactions between corporate entities across the globe. Money laundering commonly takes place between various corporations, which can involve a lot of paperwork that can have hidden patterns of behavior difficult to detect. AI using NLP techniques can recognize these behaviors by reading the text and finding the underlying patterns and links between various data.
By being overcome with a tsunami of data, banks run the risk of overlooking serious potential threats while at the same time spending money on operational procedures that are wasteful. A well-trained AI using NLP and DNNs can cut these risks and costs down substantially if used in the right way.
Freedom to Experiment and Innovate
It’s important now more than ever for banks and financial service firms alike to embrace the use of AI to improve upon their existing AML efforts. With financial criminals becoming more sophisticated due to ever-improving technology, there’s no reason for banks and other firms to ignore the use of AI.
Just like many technology companies today, the financial services industry will need to take the necessary steps to experiment and innovate when looking to overcome the challenges of financial fraud and money laundering. The age of discovery has passed when it comes to AI technologies, which now leaves us in the age of implementation. These mature technologies can be applied today to solve some of the biggest issues in banking.
Unfortunately, many banks have old, outdated systems that carry with them a huge amount of technical debt. The only way to overcome this is by cleaning it up and replacing it with more modern, powerful technology. Before long, AI will be in every corner of the banking world, so those who get on board sooner rather than later will likely have a big leg up in the future to come.
Author
Grant's a fintech content marketer and SEO strategist who works 100% remotely. With years of professional experience working alongside the largest fintech brands and marketing agencies, he's well-equipped to help grow his clients' businesses with effective content and SEO strategies.