How best to use generative, predictive, and agentic AI in financial crime prevention

The impact of AI on financial services has been revolutionary, paving the way for more efficient and effective systems to combat financial crime.
In a recent webinar, AI in Action: Practical Applications for Financial Crime Prevention in 2025 , experts Nick Vitchev, Research Director at Chartis Research , and Charmian Simmons, Financial Crime & Compliance Expert at SymphonyAI, shared valuable insights on how AI is shaping the financial industry. Below is a short preview.

This blog delves into key topics discussed in the webinar including:

• AI adoption in financial services
• Spending trends in financial crime prevention
• Practical tips for using AI
• Predictions for the future

Spending on AI in financial crime prevention is increasing

Investments in financial crime prevention technologies continue to increase as banks and other financial institutions aim to bolster their defenses against sophisticated threats. Nick Vitchev highlighted the significant spending trends and the immense potential for AI in financial services to optimize these efforts.
Vitchev explained, "More than $70 billion annually is being spent on manual processes within financial crime prevention." This expenditure includes tasks such as managing Suspicious Activity Reports (SARs) and conducting investigations, where repetitive and often mundane tasks consume substantial resources.
Vitchev pointed out that this spending vastly surpasses technology investments, with a ratio of four dollars spent on manual processes for every dollar spent on technology.
The challenge lies in balancing this spending while ensuring that AI in financial services can effectively support automation without massive job losses . Vitchev reassured, "Automation doesn't replace jobs; it enhances efficiency, allowing institutions to manage their rising workload better." This perspective resonates with the industry's focus on adapting and implementing AI-driven solutions to improve operational efficiency and reduce costs.

Current AI adoption in financial services

AI in financial services is not just a future promise but an ongoing reality that is reshaping the industry. Financial institutions across the globe are recognizing the transformative power of AI and, as a result, are making strides in its adoption to tackle financial crime more effectively.
Charmian Simmons emphasized that AI adoption has rapidly gained momentum, with financial institutions engaged in practical applications and testing initiatives. Pointing to research, she noted that "80% of banks surveyed by Chartis are currently engaged in AI initiatives, ranging from Proofs of Concept (POCs) to full-fledged investments."
This underscores the industry's recognition of AI's potential to improve efficiency, accuracy, and decision-making capabilities in financial crime prevention.

A growing confidence for using AI in financial services

The growing confidence in AI is driving a surge in initiatives that explore its utility in various aspects of financial services operations.
A significant factor contributing to this surge is the evolving landscape of AI technologies, particularly agentic AI and large language models. These innovations are seeing interest because of their ability to perform complex tasks, provide insights, and assist with decision-making processes.
With the aim of improving productivity, there is enthusiasm to invest in solutions that incorporate AI capabilities into their existing frameworks rather than completely reworking the current approach to preventing financial crime. This investment is not just limited to top-tier banks with mid-sized and smaller institutions also exploring using AI in financial services, recognizing it as a strategic tool that can offer competitive advantages.

Solving the early challenges of AI adoption

Financial institutions must navigate various challenges, including data integration, compliance , and change management, to fully embrace AI in financial services:

• “Internal readiness, data access, and quality governance are essential for successful AI integration,” said Simmons. These elements are critical in ensuring that using AI in financial crime prevention is grounded in a solid foundation that supports scalability and sustainability.

• Moving beyond initial experimentation and pilot projects, institutions are now focusing on building strong data infrastructure. Clean, high-quality and standardized data across an organization is essential for AI to function effectively, enabling accurate analysis and insights. Institutions are also investing in data transformation projects to enhance data accessibility and interoperability across various systems and departments. This effort ensures that AI applications can seamlessly integrate into existing workflows and provide immediate value.

• Comprehensive responsible AI policies and protocols that oversee AI usage are being developed with the appointment of AI officers and specialized teams overseeing AI strategy and implementation, ensuring initiatives align with broader organizational goals. AI adoption in financial services is driving innovation in governance and oversight and banks are keen to ensure transparency, accountability, and compliance with regulatory standards.

• A cultural shift is occurring. A critical component of AI adoption. Simmons noted that the industry's willingness to embrace AI is reflective of a broader recognition that technological innovation is necessary to remain competitive and effective in the fight against financial crime. This cultural transformation is fostering an environment where AI is viewed not as a threat but as an enabler, unlocking new possibilities for efficiency and effectiveness.

Vitchev and Simmons agreed that AI adoption in financial services is advancing rapidly, driven by a combination of technological innovation, strategic investment, and cultural change. Institutions are increasingly recognizing AI in financial services as a vital component of their financial crime prevention strategies, leading to a strong wave of initiatives aimed at harnessing its full potential. This includes improving AML software to enhancing KYC/CDD processes and combating payment fraud .

Practical tips for using AI in financial crime prevention

Implementing AI in financial crime prevention requires careful planning and a strategic approach to maximize benefits. Nick Vitchev and Charmian Simmons provided insightful tips to help institutions effectively use AI for financial crime prevention.

1. Focus on specific use cases: "AI is not for everything", said Simmons. Identifying areas where AI can add the most value, such as AML transaction monitoring , sanctions screening , and money mule detection, is crucial. By targeting specific processes, institutions can achieve tangible outcomes, like reducing false positives and enhancing efficiency.

1. Embrace Agentic AI: Agentic AI represents a significant advancement in automation by enabling AI systems to make decisions and execute tasks autonomously. Simmons noted that "Agentic AI is where AI not only processes information but also makes decisions, offering transformative potential for tasks like sanctions evasion detection." Using agentic AI in financial services can streamline processes and hugely improve productivity.

1. Invest in platform modernization: Integrating AI technologies requires modernized platforms and infrastructure. This involves cloud adoption, data integration, and containerization to ensure scalability and flexibility.

1. Upskill and train staff: "Equipping staff with the right tools and training enhances job satisfaction and retention," said Vitchev, underscoring the importance of upskilling employees to harness AI effectively. Furthermore, training programs enable employees to shift focus from manual tasks to strategic risk management, maximizing AI's impact.

1. Ensure explainability and governance: As mentioned previously, AI systems must be transparent and explainable to satisfy regulatory requirements. Institutions should implement robust governance frameworks to oversee AI processes and outputs, ensuring compliance and accountability.

The future of AI in financial crime prevention

The future of AI in financial services is filled with exciting possibilities, especially in the areas of detecting and investigating financial crime. The webinar highlighted several trends and predictions that underscore the transformative potential of AI to redefine financial crime prevention.
“Real-time data and analytics sharing is a key goal for 2025 ,” said Vitchev. “Banks, regulators, and consumers all demand it." This vision emphasizes the importance of collaborative efforts to build integrated systems that facilitate seamless information sharing, enhancing industry-wide efforts against financial crime.
As part of the move to real-time processes, Charmian Simmons expressed her enthusiasm for agentic AI in financial services - "Agentic AI is set to revolutionize financial crime prevention, enabling real-time, independent decision-making." As AI technologies mature, their applications will expand, offering unprecedented levels of automation and intelligence.
By embracing generative, predictive, and agentic AI and following best practices, financial institutions can enhance their systems and secure a competitive edge over criminals.

Discover AI in financial crime prevention with SymphonyAI

SymphonyAI is at the forefront of revolutionizing AI in financial crime prevention. With a comprehensive range of financial crime prevention solutions, we empower financial institutions globally to navigate complex regulatory landscapes and optimize their compliance frameworks.  
Don't miss the opportunity to learn from industry experts and stay ahead of the curve in this rapidly evolving area.