The RegTech Summits in London and New York delivered a deep dive into AI-driven compliance, accelerated settlement, and evolving regulatory frameworks among other key RegTech topics, with industry leaders and regulators weighing in on the biggest challenges ahead.
From the implementation of accelerated settlement cycles across global markets to the rise of generative AI (GenAI) in surveillance and anti-money laundering (AML), this year’s sessions revealed what’s next for compliance practitioners navigating regulatory change management challenges.
We’ve selected nineteen key insights from the 2024 summits that offer a glimpse of what’s coming in 2025 – check out this year’s upcoming events—here for London, and here for New York. These events are brought to you by A-Team Group, publishers of RegTech Insight.
The Future of Regulatory Data Collection (London)
Transforming regulatory data collection is not just about efficiency—it’s about creating a national asset that supports financial stability and global competitiveness. Aligning firms’ internal data management with reporting requirements not only improves data quality but also significantly reduce compliance costs. The shift toward standardized, machine-readable reporting frameworks could revolutionize compliance operations across the industry.“Aligning how firms manage, exchange, and use data internally to the way they report to us will improve data quality and cut the costs of reporting.”
A key development in regulatory compliance is the push toward machine-readable regulation, allowing firms to ingest rules directly into AI-driven compliance systems. Regulators including the FCA and Bank of England are actively working on tagging regulatory documents to make them easier to process using AI. This could significantly reduce the burden of manual interpretation for compliance teams.
“The FCA Handbook will soon be machine-readable, making AI-powered compliance much more viable.”
Best Practices in Regulatory Data Management (New York)
Data management remains a bottleneck for compliance, with firms still relying on manual process steps and fragmented data sources. A shift toward API-driven, machine-readable frameworks is essential to cope with the increased volume, improve accuracy and reduce costs. Panellists emphasized that poor data governance is no longer just a compliance issue—it’s become a fundamental business risk.“Data quality isn’t just a compliance problem—it’s a business problem.”
EMIR Refit: Lessons from Implementation (London)
Firms implementing EMIR Refit are encountering significant data consistency challenges, particularly in aligning trade repositories with internal reporting systems. Without a standardized approach to data reconciliation, firms risk reporting discrepancies that could trigger regulatory scrutiny. The panellists emphasized the need for a robust, automated data validation framework to ensure compliance across jurisdictions.
“If your data isn’t harmonized across your reporting stack, your compliance risks skyrocket.”
Cybersecurity & Digital Operational Resilience Act (DORA) (London)
The discussion highlighted the importance of resilience testing and third-party risk management, particularly for firms relying on SaaS/cloud-based services and outsourced technology. DORA compliance requires data sources that firms might not currently have clear access to such as critical vendor supply chain status.
“DORA isn’t just another compliance exercise—it’s a long-term resilience shift. Firms must act now to embed these controls into their operations.”
The Evolution of Generative AI in Surveillance (New York)
Generative AI is increasingly being used to detect compliance violations across email, chat, and voice communications. However, one of the biggest concerns remains hallucinations—where AI generates false conclusions. While AI is helping to reduce false positives, panellists warned that it must be carefully managed to ensure decisions are evidence-based and explainable.
“Recently published papers showed that hallucinations are a fundamental artifact of LLMs that are not removable.”
Regulatory Expectations for AI-Powered Financial Products (New York)
With AI-powered investment platforms on the rise, regulators are paying close attention to how AI makes investment recommendations. The concern is that some fintech firms are automating advice in ways that could mislead investors, particularly when AI-generated content is presented without clear disclaimers. Regulators made it clear that AI-based investment tools will be held to the same fiduciary standards as human advisors.
“One of the fraud risks that we talked about was the use of deep fakes, and how individual investors and organizations themselves should be wary about trusting things that come from one source.”
Collaboration Between RegTech Firms & Regulators (New York)
Regulators emphasized the need for early engagement between RegTech firms and financial regulators to ensure new technologies are compliant before they reach production. Firms were urged to work with regulators before launching AI-driven tools to avoid unnecessary compliance risks. The panel also highlighted ongoing regulatory initiatives to gather industry feedback and refine AI policies.
“If you find areas where you’re trying to do certain things with technology, including Gen AI, where you’re running into grey areas, come and talk to us.”
AI & Large Language Models in Compliance (London)
Firms are experimenting with Generative AI in compliance workflows, such as regulatory reporting and contract analysis. However, concerns over hallucinations, data bias, and regulatory scepticism remain significant barriers to adoption. Panellists emphasized the need for robust AI governance frameworks to ensure that outputs are reliable and explainable.
“AI’s inability to detect causality is a fundamental flaw. It will confidently give you a wrong answer, and that’s a big problem in compliance.”
AI’s Expanding Role in Market Oversight (New York)
FINRA explored AI’s growing role in market surveillance, trading risk management, and compliance while highlighting the emerging problem of “AI-washing”—firms overstating their AI capabilities to attract investment. While AI presents enormous potential for improving regulatory efficiency, the challenge lies in ensuring AI-generated decisions remain explainable and auditable. Regulators are urging financial institutions to engage with them early to establish best practices for AI governance.
“AI is designed to predict, not provide factual information. You need a system in place, a governance system, to make sure you don’t end up with a bloodied-up company.”
Monitoring GenAI Outputs in Compliance (New York)
AI’s unpredictability makes continuous monitoring essential, particularly in applications such as AML, fraud detection, and e-communications surveillance. Unlike traditional IT systems, AI models must be regularly tested and retrained to ensure their outputs remain accurate and compliant with evolving regulations. Firms that fail to monitor model drift and data drift risk making decisions based on flawed, outdated, or biased models.
“The set-it-and-forget-it model cannot be what you’re using. You need ongoing oversight, governance, and human validation.”
The Global AI Regulatory Landscape (New York)
Regulators, including the SEC, FINRA, and European authorities, are closely monitoring AI adoption in financial services. A major challenge is the lack of harmonized global standards, making it difficult for firms operating in multiple jurisdictions to deploy AI based solutions. Panellists advised financial institutions to engage with regulators early to ensure AI solutions align with evolving compliance expectations.
“We’ve been speaking to a number of different institutions to figure out how they’re using AI, including Gen AI, within the past couple of years.”
AI’s biggest challenge is not its technological limitations, but governance and ethics. Without proper oversight, AI-driven decisions could introduce biases, compliance failures, or even legal liability. Firms adopting AI must establish robust accountability frameworks, ensuring AI models are tested rigorously before deployment.
“AI cannot replace human judgment, especially when compliance decisions impact customers and markets.”
T+1 Settlement Transition Success in the U.S.
The T+1 settlement transition in the U.S. has been a success, with industry-wide coordination led by DTCC, SIFMA, and the Investment Company Institute (ICI). The shift has already unlocked $3 billion in clearing fund reserves, improved same-day affirmation rates, and maintained low fail rates comparable to T+2 levels. Globally, the move has accelerated momentum toward shorter settlement cycles, with the UK, EU, and several Latin American markets planning T+1 transitions by 2027. However, operational adjustments continue, particularly in areas like securities lending, ADRs, and ETF processing, where firms have introduced T+0 create-and-redeem options to mitigate liquidity challenges.
“Since May, the average institutional affirmation rates have reassuringly risen to the mid-90 percent.”
Post-Trade & T+1 Settlement: The Automation Imperative (London)
With T+1 live in the US and accelerated settlement now on the horizon for the UK, panellists stressed that firms must eliminate manual post-trade processes to meet new speed and accuracy requirements. The discussion highlighted the operational risks of failing to automate, particularly around trade reconciliation and securities lending. Firms that delay automation risk being caught off guard when the industry shifts to an accelerated settlement cycle.
“Automation is not optional. If your post-trade process isn’t streamlined, T+1 will expose every weak point in your operations.”
E-Communications Surveillance: The Persistent Blind Spot (New York)
Despite regulatory crackdowns, many firms still struggle to capture and monitor off-channel communications. AI-powered surveillance tools are helping firms detect behavioural patterns that indicate potential compliance risks. Privacy concerns and capture/archiving challenges are slowing adoption.
“Lexicon-based surveillance is outdated. AI can now detect behavioural patterns that indicate misconduct, but firms must learn how to use these insights effectively.”
The Future of Trade Surveillance & AML (New York)
Trade surveillance and anti-money laundering (AML) compliance remain heavily reliant on traditional rule-based detection methods, but AI-driven advancements are improving efficiency. Panellists noted that false positives continue to be a major issue, and AI solutions must be carefully managed to balance risk mitigation with operational efficiency. Regulators are particularly focused on ensuring that AI-driven AML decisions remain explainable and transparent.
“Explainability in AI-driven AML is not optional—it’s a regulatory requirement.”
FinCEN’s Final Rule & Buy-Side Compliance (New York)
FinCEN’s new AML requirements for buy-side firms will require asset managers and hedge funds to enhance data governance and surveillance capabilities. Unlike banks, many buy-side firms have not previously been subject to stringent AML obligations, making compliance a significant challenge.
“Buy-side firms can’t rely on outdated AML frameworks anymore—this rule changes everything.”
Building an Agile RegTech Infrastructure (New York)
Legacy compliance systems are slowing down firms’ ability to adapt to regulatory changes and AI-driven solutions. Panellists highlighted the importance of cloud-based, API-first architectures that allow firms to quickly integrate regulatory updates and new technologies. Without this agility, firms risk being left behind as markets and regulations rapidly evolve.
“If your compliance tech can’t evolve, you’re already behind.”
So, there you have it, nineteen insights for 2024. This is just a taste of what’s to come this year, so mark your calendars for RegTech Summit London and RegTech Summit New York.
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