Artificial intelligence (AI) is reshaping the landscape of financial crime management at a pace of change that is staggering. Criminals are evolving their tactics using various levels of AI, but so are those tasked with defending against the escalation of financial crime. In a recent panel webinar, I joined my colleagues from Nasdaq Verafin to examine how AI is impacting financial crime management. From our discussion, here are five key insights that emerged that are critical for fraud and AML analysts to understand right now.
1. Vigilance is Vital to Stop Evolving AI Schemes
Criminals are leveraging AI in ways that were unimaginable just a few years ago. The barrier to entry for sophisticated scams has dropped dramatically. Today, it takes only a few seconds of audio for a fraudster to clone someone’s voice and perpetrate convincing social engineering attacks. With deepfake technology easily creating realistic video calls, romance scams and Business Email Compromise have become far more believable.
What’s more, AI-powered tools can scrape the internet for personal information, craft flawless phishing emails, and build legitimate-looking websites in minutes. The quality and velocity of these attacks are increasing, and even non-technical criminals can now execute complex schemes. For those on the front lines, this means that vigilance and an understanding of these new threats must be sharper than ever.
2. Using the Best AI Tool to Fight Financial Crime
Fortunately, the same technologies that empower criminals are also revolutionizing the defenses against them. The AI and machine learning landscape is progressing at an exponential rate built on a hierarchy of technologies.
Artificial intelligence aims to approximate human intelligence to solve complex problems like fraud detection. This can include machine learning where models learn from historical data, or deep learning which aims to mimic the human brain to uncover complex patterns.
In recent years, more advanced AI tools have emerged. Generative AI is a specific deep learning architecture specializing in natural language processing and understanding long term context. Large language models (LLMs) are unlocking new capabilities for analyzing unstructured data and understanding. Nasdaq Verafin utilizes generative AI in its Entity Research Copilot that automates research tasks like negative news searches and counterparty analysis – boosting productivity for financial crime management teams.
As new tools like generative AI are developed, they lead to a natural cascading evolution. Agentic AI leverages generative AI to autonomously plan a workflow, reason over context, and take actions toward achieving specific goals. It can reduce the human intervention needed in repetitive processes so they can focus attention on critical decisions. Nasdaq Verafin’s Agentic AI Workforce utilizes a Digital Sanctions Analyst and Digital EDD Analyst that automate high-volume, low-complexity compliance tasks, reducing manual workloads and accelerating investigations.
While generative AI is grabbing headlines, not every problem requires the same level of technology. Sometimes, classic machine learning approaches are more effective for specific tasks such as check and wire fraud detection. The key is to select the right tool for the job and to keep refining models as new threats emerge.
3. Consortium Data and the Effectiveness of AI-Powered Solutions
One of the most exciting developments is the integration of AI across fraud detection, AML, and financial crime management platforms. The effectiveness of these solutions is rooted in their ability to process vast amounts of consortium data — billions of transactions, hundreds of millions of counterparties, across thousands of institutions — while maintaining strict privacy standards.
The depth and breadth of consortium data is the game-changer. By analyzing transactional histories and profiling counterparties, models can be trained that are far more effective at identifying suspicious activity. The result is a more efficient allocation of resources and a stronger defense against financial crime – meaning fraud and AML teams can focus on genuine threats rather than wasting time on unnecessary investigations.
4. The Efficiency of Agentic AI Workforces
Beyond detection and prevention, AI is also transforming how teams work. Agentic AI workers—autonomous agents that replicate human actions in compliance workflows — are now a reality. These agents can automate time-consuming tasks for case automation and regulatory reporting, freeing up analysts to focus on strategic decision-making.
The vision for Agentic AI is to automate end-to-end processes, reduce compliance costs, and allow institutions to invest in growth areas rather than just keeping up with regulatory demands and repetitive tasks and process work.
By removing the time component from manual tasks, it enables teams to put their expertise to work where it matters most. The result is a more strategic, agile, and effective approach to financial crime management.
5. Responsible AI Development is Essential
When leveraging AI, it’s important to establish a policy for responsible AI usage, to serve as guiding principles when developing AI tools, or as a guidebook to help evaluate and implement third-party tools within your own processes. At Nasdaq Verafin, there are defined principles used to guide responsible AI development:
- Fairness: Minimizing unintended bias in AI outcomes across different demographics.
- Transparency & Explainability: Creating understandable outputs from AI systems, avoiding “black box” analysis.
- Accountability & Auditability: Clear indication of when decisions are made by AI, with comprehensive logs for input and output audit purposes.
- Security & Privacy: High standards of data privacy for model inputs and outputs. Securing AI systems against misuse.
- Reliability & Safety: Measure AI systems for robustness, precision, recall and reliability to reduce operational risk.
- Human Oversight & Controllability: Identify critical decisions that require human-in-the-loop, maintain clear oversight over performance, and maintain ultimate control over the AI system.
Partnering for Success in the Changing AI Landscape
AI is a transformational technology that’s changing the fight against financial crime. The tactics of criminals will continue to evolve, but so will the defenses responding to these pressures. Success in this new era requires not just technology, but transparency, governance, and a willingness to adapt.
With over 20 years of experience in AI-driven financial crime management, Nasdaq Verafin stands ready to help institutions navigate this complex landscape. Whether you are just beginning your AI journey or looking to advance your institution’s capabilities, now is the time to engage with experts who understand both the risks and the opportunities.
Watch the full in-depth discussion – How AI is Impacting Financial Crime Management
About the Author
Tim Light
Principal Data Scientist, Nasdaq Verafin
As Principal Data Scientist at Nasdaq Verafin, Tim Light leads innovation across strategic domains including consortium data and artificial intelligence, working closely with subject matter experts to consult on software and infrastructure. Tim’s specific focus is ensuring that analytics, including those powered by artificial intelligence and machine learning, are designed and implemented following architectural and data science best practices, while also spearheading broader data science initiatives across the organization.
With nearly a decade of experience at the company, Tim has held several senior leadership roles, notably as Head of Data Science and Lead of Artificial Intelligence, driving transformative initiatives in financial crime detection and data-driven decision-making. Tim’s background in software design engineering underpins a strong technical foundation, enabling the development of scalable artificial intelligence solutions and advanced analytic platforms that support Nasdaq Verafin’s mission to combat financial crime through collaborative intelligence.
