Keeping up with anti-money laundering regulations? Rules and requirements keep getting updated very fast, and financial institutions need innovative solutions that will help them strengthen their compliance programs and remain one step ahead of regulators.
Artificial intelligence can arm banks with a vital edge for regulatory compliance by way of better transaction monitoring, customer due diligence, and suspicious activity reporting.
The machine learning algorithms can be trained to recognize legitimate customer transactions, simultaneously raising a red flag in case of abnormal activity that may indicate potential money laundering or financial crime. This helps banks adhere more comprehensively to regulations while monitoring accounts continuously.
This article will discuss how to leverage AI in BSA/AML Compliance to Gain a Regulatory Edge.
Anti-money laundering regulation is evolving
The AML and CFT regimes continue to change with new regulations. It is upon the banks to update themselves with new sets of rules provided by the regulators.
AI in BSA/AML Compliance and AI in AML can be applied in financial institutions to adapt faster to evolving regulations.
Algorithms review changes to laws and alert staff to any updates needed to their monitoring processes.
By 2023, about 70 percent of financial institutions globally will be implementing AI across their AML programs.
Understand the compliance obligations
It becomes necessary for financial institutions to understand all their compliance obligations to follow anti-money laundering laws effectively. Many rules surround know-your-customer practices, transaction monitoring, and suspicious activity reporting.
AI can help banks understand the full scope of their responsibilities. This would give the banks an understanding of the regulations through analysis, provide training to all employees using AI all, and make them comprehend expected protocols.
It’s projected that AI applications in regulatory compliance will save the banking industry more than $1 billion annually in regulatory fines and associated costs by 2024.
Bonus: To learn more about how AI money laundering solutions can reinforce your institution’s AML compliance program and its regulatory relationships, book a demo with one of our compliance experts.
AI monitoring to beef up programs
Vital risk-mitigating AML programs are central to compliance. Artificial intelligence holds great promise to push transaction surveillance and faster reactions. Algorithms can apply big data to drive out unusual patterns and suspicious transactions that could be invisible to the human eye.
AI, which is weaved into AML compliance programs, not only strengthens the monitoring of accounts and activities but also works toward the perfection of continuous improvement in the detection of financial crimes like money laundering.
By 2023, AI-enhanced systems will have increased the identification of complex money laundering schemes by 30% over traditional methods.
Enhancing Customer Diligence AI
Knowing your customers lies at the heart of any strong AML program. However, gathering and verifying customers’ details is time-consuming when done manually.
With AI and ML in anti-money laundering, banks will be able to apply AML AI to support customer due diligence processes.
Artificial intelligence can crawl through databases and public records for information during onboarding. At the same time, machine learning becomes more astute over time in detecting inconsistencies or pinpointing incomplete due diligence. It helps bankers and further gives a depth of knowledge about clientele to financial institutions.
By 2024, AI will help banks cut down by up to 50% time spent on customer due diligence.
Complex schemes of artificial detection
Modern money launderers no longer deposit vast amounts of cash in a single location. They have adopted highly complex methodologies to remain below the radar. However, artificial intelligence anti-money laundering is adept at unraveling even sophisticated layering and integration schemes.
AI finds patterns that humans would miss by tracing transactions across accounts, entities, and geographies. When anomalous financial activities are recognized, AML AI notifies compliance teams.
Early warning helps respond rapidly, preventing the completion of laundering cycles. This shuts off the movement of illicit funds via the banking system.
In 2023, AI-driven systems gave warnings to halt about $2 billion in potential money laundering activities by shutting off illicit funds movement early in the laundering cycle.
Fewer false, more true positives machine learning
With vast volumes of financial transactions to process for review, even the finest artificial intelligence anti-money laundering solutions can yield a few erroneous flags. Machine learning at AI money laundering screening helps to change models over time.
The algorithms self-correct as experts provide feedback on which alerts were false positives and which represented actual suspicious activity.
That means that with each new batch of data, the number of false positives reduces, and the rate of true positives being correctly identified increases in this progressive learning process. Resulting in more robust AML screening services.
Partnering regulators through artificial intelligence
The regulators aim to ensure that FIs are correctly enforcing the AML/CFT legislation, but most also realize the burden of review quotas. Artificial intelligence offers a way to reconcile these two objectives.
AI for all screening services generates transparent case-by-case explanations for every single alert. This enables productive discussions whenever supervisors audit banks.
Moreover, AI-processed de-identified suspicious transaction data related to money laundering can be shared with government agencies safely. This kind of collaboration tunes technologies and further fortifies the anti-financial crime partnership.
