Meet Agentic AI: Your New Ally In The Fight Against Financial Crime

Craig Costigan, CEO, NICE Actimize . As AI continues to deliver business impact, 2025 has brought a new twist with the emergence of agentic AI. The advanced capabilities of agentic AI are transforming the fields of anti-fraud and anti-money laundering, making significant strides in safeguarding financial systems.

This technology enhances detection, prevention and compliance measures while increasing efficiency. As a technology CEO, I've had many focused discussions with financial institutions about how they hope to embed agentic AI in their operations. After learning firsthand about the challenges they face, such as cost control, the strengths of agentic AI are being evaluated and tested carefully.



Recognized for its autonomous decision-making functions, agentic AI operates independently and can make informed decisions and detect suspicious activities in real time. This approach can noticeably improve response times to potential threats, instilling a sense of control and confidence without constant human oversight. What are the critical capabilities of agentic AI that contribute to its accelerating profile among businesses and financial institutions? Agentic AI orchestrates a complex set of capabilities—large language models (LLMs), machine learning (ML), natural language processing (NLP) and other technologies—to independently pursue goals.

It can formulate plans, carry out complex sequences of activities, make decisions and, most importantly, learn and adapt. As a result of these core capabilities, it succeeds at dynamically assessing risk and analyzing user behavior patterns over time. Dynamic risk assessment is a critical requirement in the fight against financial crime.

Agentic systems continuously adapt to the ever-changing risk landscape, which includes evolving fraud tactics and regulatory changes. They adjust risk profiles and identify fraudulent transactions more accurately while enhancing overall security by integrating real-time data feeds with historical transaction trends. Behavioral analysis is also vital.

Agentic AI systems analyze user behavior patterns over time, establishing what constitutes normal behavior for individuals. This understanding enables the identification of anomalies that may signal fraud or money laundering. Also, agentic AI's ability to reduce false positives can significantly improve the efficiency of fraud detection systems.

Working alongside human operators, agentic AI fosters collaborative intelligence, where AI and humans leverage their respective strengths. The AI provides valuable insights and recommendations while humans control critical decision-making processes, enhancing efficiency and effectiveness in identifying suspicious activities. Agentic AI’s core capability is making decisions, but this doesn’t mean you don’t have humans in the loop to review and approve critical decision-making.

Another advantage of agentic AI is its ability to integrate with existing systems. These agents can enhance current fraud detection and compliance systems without requiring a complete overhaul, easing the transition to advanced AI functionalities. This allows organizations to maximize their investments while benefiting from advanced AI capabilities.

Additionally, agentic AI can automate routine investigative processes, bringing a new level of efficiency. By gathering and analyzing data from various sources, these systems can build comprehensive profiles of suspicious activities, expedite investigations and allow human analysts to focus on more complex cases, making them feel more productive and effective in their roles. Predictive modeling is yet another capability of this technology.

Agentic AI can use ML techniques to forecast future fraudulent behaviors based on historical data. This predictive capability empowers institutions to implement preventative measures before potential fraud occurs. Because agentic AI systems can leverage ML technologies, we can supercharge predictive capabilities for better outcomes faster.

NLP also plays a crucial role in many agentic AI systems. By analyzing unstructured data from communications, such as emails and chat messages, these systems can identify potential misconduct or collusion that might not be apparent through traditional data analysis. Agentic AI also enhances compliance monitoring.

These agents can continuously oversee transactions and customer activities to ensure adherence to regulatory requirements. Their ability to quickly adapt to new regulations helps organizations maintain compliance without extensive manual intervention. Finally, agentic AI facilitates enhanced reporting.

It can generate detailed reports on suspicious activities, equipping compliance officers with actionable insights and streamlining regulatory reporting processes. We're still in the early innings of agentic AI’s debut, so there are challenges to overcome and implement at scale. The journey will be smoother as the tech matures and agentic AI systems and frameworks become more widely available.

And as with all new AI technologies, there will likely be concerns about data privacy and governance. However, momentum is flowing in a positive direction. Managing an agentic AI system requires a shift in mindset and approach.

For example, if using a predictive model to forecast trends, ensuring the model is tuned and optimized is essential for maintaining accuracy and relevance. Regular monitoring should be implemented to assess performance, allowing for timely adjustments in response to changing data patterns or external factors. Also, the efficacy of agentic AI tools relies heavily on the quality of the input data.

Conducting rigorous data health checks and implementing strong DataOps practices are vital to ensure that datasets are accurate, complete and representative. Poor data quality can lead to misleading outputs, undermining trust in the system. Data monitoring and observability are more critical than ever in this environment.

Agentic tools can query datasets, but the results are only as good as the data put into the system. Clearly, agentic AI is making remarkable advancements in the fight against fraud and money laundering. By leveraging its capabilities, organizations can improve their detection, prevention and compliance efforts, ultimately contributing to a more secure financial landscape.

Overall, agentic AI and its agents enable autonomy, adaptability and collaboration with human users to address complex issues effectively. Assess what AI and agentic AI can do for your business and proceed accordingly. Don't be left behind.

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