Regulation can be perceived as a burden, especially in industries that require managing evolving anti-money laundering (AML) and Know Your Customer (KYC) rules. But what if we’ve been thinking about it all wrong?
While regulation is often seen as a constraint, forward-thinking organizations are flipping the script—using AI to turn compliance into a strategic advantage. From startups to enterprises, those who embed regulatory insight and innovation into their operations are not only staying ahead of the rules, they’re redefining what good business looks like.
How AI can augment AML/KYC solutions
Modern financial institutions are facing all kinds of modern challenges, including identity theft, sophisticated cybercrime and social media-powered scams. Because of these and other more traditional issues like money laundering and terror funding, it’s becoming increasingly difficult to monitor for and ultimately identify risks in the financial world.
In the past, compliance professionals might rely on keyword matching and scouring relevant databases to find clients who could potentially pose a risk, such as those who were “wanted” or on watchlists; but this method will no longer suffice. Manual processes simply can’t keep up with the sophistication of modern fraud schemes or the sheer volume of data available to help make fraud determinations. Estimates project that only about 20% of internet data is structured, meaning it’s organized to be searchable, like sanctions, wanted, and watch lists. Additionally, that structured data is slow to be updated, so it isn’t always the most accurate or current.
To better detect suspicious individuals or entities, financial institutions should consider comprehensive, round-the-clock monitoring systems that can process and evaluate information from diverse digital sources of both structured and unstructured public data. Unstructured data is estimated to comprise up to 80% of data and includes news publications, government sources, court record aggregators, arrest record aggregators, and more. The unstructured data on the internet is always changing and being updated, meaning more data is being generated; so by analyzing the wealth of available unstructured data, AI can potentially help identify some risks earlier.
That always-on, sped-up detection by AI can help lay the groundwork for banks and other financial institutions to enhance their AML/KYC processes. Here are three impactful ways AI is improving effectiveness, operational efficiency and scalability:
- Real-time monitoring. One major benefit of AI-powered tools is their ability to quickly and accurately process huge amounts of data, from both structured and unstructured public sources, in real time, across the globe in various languages. Using large language models (LLMs), structural pattern recognition, natural language processing, and machine learning (ML), advanced AI systems can understand overall context and bring up relevant contextual data that may signal a red flag. This combination of sophisticated tech can help financial institutions uncover potential risks earlier so they can shorten response times and prevent a risk from becoming a full-blown problem. These cutting-edge capabilities go far beyond what traditional software solutions can do. Legacy software often relies on precise keyword matching and specific data parameters that don’t take all of the relevant information into account and can often produce inaccurate alerts. Since AI systems can be trained to understand the meaning and context surrounding language, banking organizations can use them to help minimize false positives as well as false negatives and enhance both the precision and effectiveness of monitoring procedures. Though false negatives often fall into the shadow of false positives, some of the greatest risks come from failing to bring issues to light before it’s too late.
- Understanding context. Since AI can understand the context of possible risks, it can help identify potential threats and rank them in order of severity, allowing compliance teams to accurately prioritize where their focus needs to be. If a keyword matching software uncovers a potential risk that’s actually a false positive, a lot of time and resources may be wasted on the investigation because a human (or a team of them) must review each of these flags manually. AI has the ability to help reduce false positives based on contextual information. Being able to more accurately interpret context helps ensure compliance teams are concentrating on the high-priority risks first instead of going on a wild goose chase.
- Prioritizing possible risks. Beyond just detecting and prioritizing risks based on their fraud probability, some banking organizations are using AI to automatically sort threats into distinct risk categories like money laundering, fraud or terrorist funding. Automating risk categorization allows compliance teams to quickly and easily assign a priority level to each investigation task to help align resource allocation toward the most urgent categories of risks. In short, AI can be trained to focus on the risks you care about most.
Though these three factors can help elevate compliance teams’ risk prevention strategies by uncovering more risks and saving time and resources, AI is not perfect, and there is no guarantee that it will totally erase fraudulent activity.
Despite the potential challenges, AI’s ability to analyze huge quantities of data far outpaces the capacity of humans with keyword matching software. Financial institutions that integrate AI technology to help enhance their current AML/KYC processes may ultimately have a competitive advantage when it comes to detecting and preventing fraud activity and regulatory violations, and less fraud equates to more customer trust, and proactive compliance will increasingly stand as a competitive differentiator in regulated spaces. AI isn’t a silver bullet, but it can level up AML/KYC procedures to be more accurate and efficient, driving long-term success and better results.
Vall Herard is the founder and CEO of Saifr.
