Introduction: Embracing AI and ML for AML Compliance in a Challenging Time
Financial institutions increasingly adopt AI and machine learning (ML) technologies for anti-money laundering (AML) compliance in response to the COVID-19 pandemic. A new study by KPMG, SAS, and the Association of Certified Anti-Money Laundering Specialists (ACAMS) found that a third of financial institutions are accelerating their AI and ML adoption for AML purposes.
Key Findings from the ACAMS Survey
The survey primarily asked each respondent how their employer is using or has used technology to detect money laundering. Here are some of the key findings of the survey:
Increasing AI/ML adoption
More than half (57%) of respondents said they have either deployed AI/ML into their AML compliance processes, are piloting AI solutions, or plan to implement them in the next 12-18 months. A quarter of respondents describe themselves as ‘industry leaders’ and ‘innovators’ and 24% as fast followers actively watching the progress of the industry pioneers. Meanwhile, 29% recognise themselves as ‘mainstream adopters’ who generally adopt technology once it has hit critical mass in their industry, and 22% as conservative ‘late adopters’ who resist change as long as they can.
The COVID-19 impact on adoption
39% of the compliance professionals surveyed said their AI/ML adoption plans will continue unchanged, despite the pandemic’s disruption. Meanwhile, 33% say their AI/ML plans have been accelerated and 28% say their timelines have been delayed due to the pandemic. “For institutions on the AI adoption path, they stayed the course with their AI implementation despite COVID impacts and did not derail or slow implementations,” said Tom Keegan, principal US solution leader for financial crimes at KPMG.
The AI/ML impact on AML compliance
There are three ways in which data-driven AI and ML help improve AML compliance: 1) It increases the quality of investigations and regulatory filings, 2) The reduction of false positives and resulting operational costs and 3) It detects complex risks by finding the patterns that traditional transaction monitoring rules cannot.
The AI/ML value proposition
When asked about the areas where AI/ML implementation offers the most value, 39% opted for reduction in false positives and negatives at source for the transaction monitoring process. 38% opted for assistance to investigators to get a better answer more quickly and 22% opted for classification of high and low-risk alerts before they are touched.
When it comes to the implementation of AI/ML solutions, over half (54%) considered advisory firms and/or technology vendors to be the best source for industry best practices on the adoption. Meanwhile, 22% said industry trade organisations are the most trusted source.
Regulatory stance on AI/ML
When asked about their AML regulator’s position on the implementation of AI/ML, 66% said their regulator promotes and encourages these technology innovations. Meanwhile, 28% said their regulator is apprehensive about AI/ML and 6% said their regulator is resistant to change and likely to stick with existing practices.
Small financial institutions are serious about AI/ML
The report revealed that 16% of smaller financial institutions (valued below US$1 billion) view themselves as industry leaders in AI adoption, alongside 28% of large financial institutions (with assets greater than $1 billion). This highlights that advanced technological solutions are also within reach for smaller financial organizations.
Why AI and ML Matter for AML Compliance Now More Than Ever
The potential for artificial intelligence (AI) in the AML compliance space is immense, with several factors driving its increased adoption among financial institutions. The COVID-19 pandemic has brought about a surge in complexity and sophistication of AML threats, as criminals exploit the disruptions caused by the crisis to launder money through innovative means. As a result, financial institutions are now faced with the challenge of detecting and preventing a greater range of money laundering schemes.
In addition to the growing complexity of AML threats, financial institutions must also grapple with vast volumes of data to analyze in their efforts to combat money laundering. This data comes from various sources, including customer transactions, account information, and external databases. The sheer volume of information can be overwhelming for traditional AML systems, which often struggle to process and analyze this data effectively.
Another challenge financial institutions face is the rise in false alerts, which occur when an AML system generates an alert for a transaction that is ultimately determined to be non-risky. False alerts significantly burden compliance teams, as they must investigate each alert thoroughly before determining its legitimacy. This consumes valuable time and resources and can lead to a backlog of alerts waiting to be reviewed.
Furthermore, many financial institutions continue to rely on manual processes for AML compliance, which can be both time-consuming and prone to human error. Manual processes also struggle to keep pace with the rapidly evolving nature of money laundering schemes and regulatory requirements, leaving institutions vulnerable to financial crime.
Compliance costs have ballooned in recent years, with financial institutions facing increasing regulatory scrutiny and hefty fines for non-compliance. This has prompted many institutions to invest in more efficient and effective AML solutions to achieve holistic risk coverage and reduce compliance costs.
Tookitaki’s AI-Powered AML Compliance Platform
Tookitaki offers the Anti-Money Laundering Suite (AMLS). This end-to-end AI-powered AML/CFT solution ensures operational efficiency, low risk, and better returns for the banking and financial services (BFS) industry. The solution is validated by leading global advisory firms and banks across Asia Pacific, Europe, and North America.
Tookitaki's AMLS platform covers three pillars of AML activity:
- Transaction Monitoring
- Name and Transaction Screening
- Customer Risk Assessment
Tookitaki has also developed the Typology Repository to power AMLS with comprehensive financial crime detection capabilities. The repository gathers intelligence from AML experts, regulators, financial institutions, and industry partners worldwide to identify and address new money laundering techniques.
Revamping AML Compliance Programs with Tookitaki
Of course, the pandemic has provided criminals with more opportunities to gain and clean their ill-gotten money. However, financial institutions also have options to reform and turbocharge their AML compliance measures by applying a risk-based approach and using modern technology.
As money laundering patterns continue to evolve, Tookitaki’s AML compliance solutions, powered by advanced machine learning, can help financial institutions revamp their compliance programs for lower cost of compliance, improved decision accuracy, and better automation of repetitive tasks.
Book a demo of our award-winning AMLS solution by contacting us.
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