Key Takeaways:
- In just a few years, AI has evolved from a “nice-to-have” to an integral part of regulatory compliance.
- With increasing technical maturity, AI has also become more cost-efficient, trustworthy, and transparent, thanks to advancements in explainable AI and bias reduction.
- AI in RegTech now powers predictive analytics, risk profiling, link analysis, adverse media screening, and emerging risk detection—delivering real-time insights for stronger compliance and risk management.
- Regulatory bodies worldwide are encouraging AI adoption, with many countries implementing “regulatory sandboxes” to test new technologies in controlled environments.
- As financial regulations shift to continuous compliance processes, meeting obligations without advanced technologies like AI will soon be impossible.
- Despite its urgency, AI in compliance introduces risks and complexities that must be carefully managed to ensure success.
AI adoption in businesses has surged globally, rising from around 50% over the past six years to 72% in 2024, according to a McKinsey survey.1 This growth extends to RegTech, where the market is projected to grow at a CAGR of 36.7% between 2024 and 2033, reaching USD 29.6 billion.2 Finally, AI is also transforming regulatory compliance, where it has rapidly shifted from a “nice-to-have” to an essential tool in the fight against financial crime over the past 18 to 24 months.
Key developments, such as the launch of ChatGPT in November 2022, the mainstreaming of AI applications, and the growing sophistication of financial crimes, have driven this shift. Simultaneously, increasing regulatory requirements and the shift toward digital-first, near-real-time compliance have further pressured financial institutions to adopt advanced, data-driven technologies. This momentum was further reinforced in July 2024 with the European Union’s adoption of the EU AI Act, the first global framework for ensuring trustworthy, ethical AI with human oversight.
The Evolution of AI in Financial Crime Compliance
From simple logical operations to advanced reasoning and trend prediction, AI has come a long way. The early 2000s saw the rise of data science and machine learning applications, while the 2010s introduced deep neural networks capable of processing natural language, machine translation, and image recognition. As a result, AI’s applications in RegTech also became increasingly sophisticated.
Along with technical maturity, AI has become more cost-efficient, trustworthy, and transparent, with improvements in explainable AI and bias reduction. Yet, its mainstream adoption lagged behind until the early 2020s. The launch of ChatGPT changed the way AI was perceived, making it an accessible tool that can be used for everyday tasks, even without technical expertise.
The evolution of industry-specific AI in financial crime compliance has charted a similar path to general AI. In the early days, it focused on collecting and sorting data, but it quickly moved on to workflow automation in regulatory reporting, creating audit trails, and meeting deadlines. Next came continuous monitoring, advanced data analytics, and system integration, enabling seamless detection of inconsistencies and compliance threats.
Today, AI in financial crime compliance powers predictive analytics, risk profiling, advanced link analysis, adverse media screening, and understanding emerging risks – delivering real-time insights that make risk management more efficient and compliance much stronger.
Beyond predictive analytics and real-time anomaly detection, AI will be critical in delivering business intelligence and efficiency. By leveraging intelligent Robotic Process Automation (RPA), compliance officers can prioritize high-risk tasks, reduce false positives, and stay ahead of evolving regulations—all while improving operational efficiency.
AI and Compliance: How Global Regulators Are Taking Action
AI is not only unrivalled for compliance efficiency but is also proving invaluable for business intelligence and sustainability. AI has a role to play in improving customer experiences, client onboarding, and mitigating operational risks.
The Financial Stability Board (FSB) in its report “Artificial intelligence and machine learning in financial services: Market developments and financial stability implications” released as early as November 2017, states: “Our analysis reveals a number of potential benefits. Overall, AI and machine learning applications show substantial promise if their specific risks are properly managed. Financial institutions have incentives to use AI and machine learning for business needs. Opportunities for cost reduction, risk management gains, and productivity improvements have encouraged adoption, as they all can contribute to greater profitability.” 3
Regulatory bodies worldwide have reiterated the FSB’s views and encouraged the use of AI. Many countries have introduced “regulatory sandboxes”—controlled environments that allow regulators to assess AI’s impact while enabling businesses to refine their solutions before full-scale implementation.
For example, the Canadian Securities Administrators (CSA) launched a regulatory sandbox that provided “time-limited relaxation from certain regulatory requirements.” Louis Morisset, then CSA chair and president, and current CEO of the Autorité des Marchés Financiers, said: “The objective of this initiative is to facilitate the ability of those businesses to use innovative products, services, and applications all across Canada, while ensuring appropriate investor protection.” 4
Similar sandboxes have been implemented globally, including by FINMA in Switzerland, De Nederlandsche Bank, Capital Markets Authorities across MENA and APAC, and the Financial Conduct Authority (FCA) in the UK.
As regulators establish frameworks to encourage innovation, financial and regulatory technologies have embraced AI in diverse ways. These approaches are reshaping the industry, from advanced analytics in compliance to predictive insights in risk management. Let’s explore how AI is transforming FinTech and RegTech.
- In Europe
The OECD encourages governments and private entities to invest in R&D to “spur innovation in trustworthy AI.” Most OECD countries offer tax relief for R&D investments in AI. Elizabeth McCaul, a Member of the Supervisory Board of the European Central Bank (ECB) stated: “The question is no longer about whether or not to use artificial intelligence, but rather about how it can be used most effectively and responsibly. [We can] draw on the power of AI to decipher data, understand risks and speed up processes, freeing up more time for human analysis and judgement in an increasingly complex world.” 5
- In APAC
Regulators across APAC recognize AI and machine learning as essential for innovation and competitiveness, and actively promote AI R&D and adoption. The Monetary Authority of Singapore (MAS) launched the Financial Sector Technology and Innovation (FSTI) scheme in 2015 to help financial institutions adopt AI for risk management. In 2017, it introduced the Artificial Intelligence and Data Analytics (AIDA) Grant program to further support AI and data analytics adoption.
In Taiwan, the Financial Supervisory Commission launched the FinTech Regulatory Sandbox in 2018, along with the Financial Information Service Platform, a centralized data repository for developing AI/ML solutions.
The Hong Kong Monetary Authority (HKMA) also took significant step in 2018 by launching the Banking Made Easy Task Force to promote AI/ML adoption in RegTech and introducing an open API standard for seamless adoption.
- In MENA
Regulators across MENA support AI in financial compliance, with regulatory sandboxes in place to facilitate the safe adoption of AI in regulation. These sandboxes exist in Saudi Arabia, Bahrain, Jordan, and the UAE.
- In the USA
In the USA, the Commodity Futures Trading Commission (CFTC) and the Securities and Exchange Commission (SEC) have acknowledged the unparalleled ways that firms are using machine learning technology to analyze information and streamline business operations. The SEC already leverages machine learning, deep learning, and data analytics for its SupTech initiatives.
- In the UK
In the UK, the Financial Conduct Authority (FCA) and the Prudential Regulation Authority (PRA) have adopted a pro-innovation approach to AI regulation and support the integration of AI into financial markets.
How Real-Time Compliance Hinges on AI
The FSB report outlines how regulations have evolved, making AI in compliance increasingly necessary:
“New regulations have increased the need for efficient regulatory compliance, which has pushed banks to automate and adopt new analytical tools that can include the use of AI and machine learning. Financial institutions are seeking cost-effective means of complying with regulatory requirements, such as prudential regulations, data reporting, best execution of trades, and rules on anti-money laundering and combating the financing of terrorism (AML/CFT).” 3
Since then, the increasing volume and speed of digital transactions, the rise in financial crimes, and stricter regulations have made managing compliance functions even more complex. As financial regulations shift from checklist-based processes to continuous compliance, keeping up with obligations without advanced AI-driven technology will become almost impossible.
Why AI is essential for combating financial crime and managing risk?
- Data Integrity & Insights
Data is the foundation of any compliance program—whether for monitoring customer profiles, keeping up with regulatory changes, or accessing real-time insights to prevent illicit transactions. Creating and updating an integrated, accurate data source or sifting through vast amounts of unstructured data to identify risks is no longer feasible without AI.
- Continuous Monitoring
Regulations now require ongoing transaction and sanctions screening to mitigate AML and fraud risks. This is already an enormous challenge, but detecting minor anomalies and assessing them in real-time against evolving risk profiles is nearly impossible without AI. Additionally, while financial regulations demand 24/7 monitoring, labor laws in certain jurisdictions—such as Germany’s Working Hours Act—prohibit work on weekends, creating compliance challenges that AI can help bridge.
- Intelligent Automation
From repetitive form-filling to frequent regulatory reporting, compliance officers are often bogged down by low-value tasks, leaving less time for serious investigations. Robotic Process Automation (RPA) can handle these tasks efficiently, freeing compliance teams to focus on high-risk cases and strategic initiatives.
- Link Analysis
Detecting beneficial ownership (UBO) manually—while criminals leverage advanced technologies to obscure financial trails—is a losing battle. Without AI, compliance teams waste valuable resources trying to gather, analyze, and contextualize complex ownership structures. AI-powered link analysis enables faster and more accurate UBO detection.
- Risk Profiles
Real-time compliance demands dynamic risk scoring and the ability to trigger perpetual KYC updates when risk profiles change. These are data-intensive, high-volume processes that AI can manage instantly, whereas manual processing would consume excessive human resources.
- Cost of Compliance
Rising compliance costs are driven by wasted time, inefficiencies, and human error. However, the greater risk is that manual, number-heavy compliance processes can result in critical oversights, regulatory fines, and reputational damage. AI optimizes compliance costs while enhancing accuracy.
- Business Intelligence
It’s no longer enough to react to existing risks—compliance teams must now predict emerging threats and take proactive measures. Without machine learning, conducting predictive or regression analysis is inefficient and less effective. AI acts as a business intelligence tool, offering insights for strategic decision-making and competitiveness.
The Risks of AI in Regulatory Compliance and How to Overcome Them
As AI adoption in compliance accelerates, it also introduces significant risks and complexities that must be carefully managed. Financial institutions using AI must address these challenges and work with partners who design solutions to mitigate them effectively.
AI’s reliance on vast amounts of data makes data privacy and security critical. Regulations like the GDPR and AI Act impose stringent requirements to protect sensitive information. Machine learning algorithms also require large datasets, but biased data can result in discriminatory outcomes. Despite its advancements, AI should not operate autonomously, especially when inaccuracies in algorithms or predictions could result in serious consequences. Many AI systems still lack the robustness, transparency, and explainability needed to minimize user effort and build trust.
Although AI adoption in regulatory compliance is growing, not all solutions are created equal. A well-known problem is how AI-based compliance software creates more false positives and leads to more work for users instead of improving efficiency.
The best AI-based solutions address these challenges by offering robust, transparent, and explainable solutions, favoring a hybrid approach that keeps human users in the loop, and prioritizes usability through no-code interfaces.
Siron®One: A Unique Hybrid Approach, Combining the Best of AI and Rules
IMTF’s Siron®One platform brings together the best of both worlds—a powerful rule-based engine and cutting-edge AI technologies—to generate the most accurate and reliable alerts available on the market. By leveraging this hybrid approach, Siron®One ensures high alert precision, significantly reducing false positives by up to 90%.
Unlike AI solutions that function as mere add-ons, AI in Siron®One is fully embedded into daily compliance operations, seamlessly supporting teams in managing alerts, detecting risks, and making well-informed decisions. More than just automation, our “human-in-the-loop” model keeps compliance teams in control, enabling them to work more efficiently and effectively.
Beyond technology, IMTF supports businesses throughout their compliance journey, providing end-to-end services, including consulting, solution delivery, user training, and 24/7 customer support. This ensures that customers fully leverage the platform’s capabilities and maximize their compliance investment.
Learn more about the Siron®One Platform, and if you’d like to see it in action, contact us!
Sources:
- https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
- https://market.us/report/ai-in-regtech-market/
- https://www.fsb.org/uploads/P14112024.pdf
- https://www2.deloitte.com/us/en/insights/industry/public-sector/future-of-regulation/regulating-emerging-technology.html/#figure-5
- https://www.bankingsupervision.europa.eu/press/interviews/date/2024/html/ssm.in240226~c6f7fc9251.en.html
