3 Ways You Should be Using AI in Your AML Compliance

Did you know that the financial services industry is leading the charge in AI adoption? In fact, in 2023, financial services organizations invested an estimated $35 billion in AI technologies. Banks were at the forefront, accounting for about $21 billion of that sum.

One area in which AI can make a big difference is anti money laundering (AML) transaction monitoring, a crucial regulatory requirement for financial institutions. The goal of AML is to detect suspicious behavior and stave off money laundering or terrorist financing activities.

According to Fact.MR, AI-based AML solutions are in high demand, and the market is expected to grow at a CAGR of 16.2% between 2023 and 2033.  AI is becoming the tool of choice for combating money laundering, bank fraud and other illicit activities. 

In this blog, we’ll examine three ways financial institutions can begin using AI-powered AML systems right away to reduce false positives, simplify know-your-customer (KYC) processes, and automate customer due diligence.

Reduce False Positives

AI-powered AML systems take transaction monitoring to the next level by learning from past data and continuously improving their detection skills. Traditional AML systems rely on strict rules that often result in a flood of false positives, leading to inefficiencies and wasted resources  – about 95% of alerts are false positives. That means financial institutions spend a lot of time investigating transactions that actually don’t pose a threat.

AI changes this by recognizing patterns and behaviors in transaction data, making it easier to tell the difference between legitimate activities and real threats. For instance, machine learning models can spot complex relationships within data that traditional systems might miss, reducing false positives by up to 30%, according to McKinsey. Since they don’t have to focus on false alarms, analysts can spend their time investigating genuine risks, improving the overall efficiency of AML processes.

What’s more, AI systems are always learning and adapting to new, sophisticated tactics. The dynamic nature of AI helps to minimize false positives while strengthening an institution’s ability to keep pace with the rapidly evolving threat landscape. 

Simplify Perpetual KYC

Unlike traditional KYC, which often involves periodic reviews at set intervals (e.g., annually or bi-annually), perpetual KYC integrates ongoing monitoring and real-time updates into the customer due diligence (CDD) framework. Historically, this process has required significant manual effort. 

AI simplifies perpetual KYC; by continuously scanning and analyzing changes in customer behavior, public records, and other data sources, ensuring real-time updates to customer risk profiles. In this way, institutions always have the most current customer information, reducing the chances of compliance breaches and the associated penalties and eliminating the need for periodic manual reviews.

AI-driven KYC solutions can pull data from various sources – social media, government databases, financial records and more – to create a comprehensive view of a customer. They help maintain accuracy and reliability of the customer profile, and can flag changes that might indicate increased risk. PwC reports that institutions using AI for KYC processes can save up to 40% in costs while ensuring high compliance standards.

Automate Customer Due Diligence

Verifying and analyzing customer information to assess risk levels – what’s called Customer Due Diligence (CDD) – is vital for AML efforts. However, traditional CDD processes are often slow and prone to errors due to the sheer volume of data. AI transforms CDD by automating data collection and analysis from various sources, making the process more efficient and accurate.

AI systems pull data from government databases, watchlists, and public records to verify customer identities and assess risk levels. For example, AI can cross-reference customer information with sanction lists to ensure compliance with international regulations. Accenture found that AI can automate about 30% of due diligence tasks and augment another 20%, speeding up the onboarding of new customers.

AI also enhances transaction monitoring by analyzing patterns, geolocation data, and social media activity to spot anomalies that might indicate financial crimes. This automation reduces manual effort and human error, leading to more reliable due diligence outcomes. AI also continuously monitors customer activities, updating risk assessments in real-time, ensuring that any changes in behavior are promptly flagged for further investigation.

Get the AI Advantage for Successful AML Compliance

AML will continue to be a critical activity for financial institutions due to the increasing complexity of financial crimes and the tightening of regulatory requirements globally. Ensuring robust AML processes helps banks mitigate risks associated with money laundering, maintain compliance with evolving laws, and protect their reputations.

Fortunately, AI can offer transformative solutions that address critical challenges in AML strategies – and you can start implementing those solutions today. Download DataVisor’s ebook, “The AI Advantage: Enhancing AML Strategies for Financial Institutions,” to learn how.