Day: January 26, 2024

In today’s digital world, banks and financial institutions must work harder than ever to protect the assets of clients. Since the 2008 financial crisis, regulatory change increased 500% and these changes have translated to increased spending of up to 60% on compliance. While modern technology makes it easier for bad actors to commit financial crimes, the good news is that banks and other financial institutions can also use technology to improve their financial programs.

What is AI and Machine Learning?

Artificial intelligence (AI) is technology that can replicate human-like behavior, such as learning, planning, and problem solving. Machine learning is a subset of AI, which takes data (typically massive amounts) and sorts through it to find patterns. Over time, machine learning becomes better and better at what it does (hence the “learning”).

AI and specifically machine learning, is becoming more common across many industries, and finance is no exception.

Across the industry, AI technology is being used to:
• Improve data management
• Reduce human error
• Minimize false positives
• Prevent fraud and money laundering

Improved Data Management

No matter which sector of the financial industry you work in, it’s almost guaranteed that you’re managing massive amounts of data. When you implement AI technology you bring all that data together. Patterns that previously may have been almost impossible to see now become clear.

Bringing together all your data and using AI technology to sort through it can show where people or processes struggle, but even more importantly it can help businesses understand why they’re struggling. The ability to add context to human data is the difference between a series of data points and behavioral data that can help you improve processes. For example, knowing that something comes up in a disclosure report is helpful, but highlighting common denominators appearing consistently in disclosure reports tells you far more.

The use of behavioral data is already common, but it’s implementation will likely only increase in the coming years. No matter the size of your organization, whether you’re looking for technology to improve operations, email archiving, or compliance, considering AI offerings that improve data management can help your organization save time and money.

Reducing Human Error

Human error costs regulated industries billions annually. Due to its complex and highly regulated nature, asset management is especially susceptible to issues of human error (and these errors can often be quite costly). While eliminating human error completely is impossible, increased use of machine learning and AI can help minimize it.

Compliance officers working in finance are required to handle massive amounts of data relating to everything from transactions to company operations. The amount of data is more than any human could review on their own, which is why capable technology is so necessary. While more data typically increases the chance of a human making a mistake, it generally decreases the chances of machine learning technology making a mistake, since the more data the technology has, the more it can “learn” and the better it can become.

Minimize False Positives

Arguably one of the most time-consuming aspects of compliance is addressing false positives. With rule-based alert systems the rate of false positives can be incredibly high since the system applies a rule across all situations and cannot take into account different circumstances or contexts.

Any time an alert is raised, a compliance officer must personally review it. False positives are therefore a waste of resources since the time and energy of the compliance officer must be redirected from other tasks to address the alert. Additionally, bringing in a compliance officer when not necessary opens the situation up to human error, which as discussed previously, is a major concern in regulated industries such as finance.

An excellent example of machine learning compliance in action is email review. A rule-based alert system will search outgoing emails for specific keywords and will typically flag many false positives, which a compliance officer must then review. In contrast, an AI-based technology can learn as it goes and over time will raise fewer false red flags.

Prevent Fraud and Money Laundering 

Since machine learning can identify data points as unusual in different scenarios, as opposed to applying the same criteria across the board, it’s ideal for identifying fraud and money laundering activities. For example, credit card companies often notify cardholders when fraudulent activity is suspected on their card. If you have a credit card, you can probably attest to how much this fraud prevention technology has improved in recent years, with higher accuracy in identifying fraudulent activity. This increased accuracy is thanks in large part to machine learning technology since the more the technology can become familiar with patterns the better it can detect anomalies.

Technology that learns as it goes is especially necessary when it comes to fraud and money laundering, since those attempting to commit fraud or launder money are constantly adapting their tactics in response to new regulations. For example, anti-money-laundering (AML) policies require that transactions greater than $10,000 to sanctioned countries must be reported and analyzed. Many money launderers have become aware of this rule, and in order to avoid detection often keep transactions right below this $10,000 mark. Machine learning technology can learn and update its search criteria in response to this change. Instead of searching for transactions over a certain amount it can use multiple screening tools to stay ahead of money launderers and flag potentially fraudulent transactions, even if they fall below the $10,000 mark.

The Takeaway