9 essential benefits of agentic AI in financial services

Understanding why and how agentic AI is the future of crime prevention in financial services 

The rise of financial crime poses a significant threat to organizations and individuals alike, prompting a demand for more sophisticated prevention technologies. As a pioneer in AI and fintech, SymphonyAI is committed to leading the charge with innovative Software-as-a-Service (SaaS) solutions and capabilities. One such example is groundbreaking agentic AI technology, designed to empower organizations to stay ahead of financial criminals by using autonomous AI agents to uncover their actions more quickly than ever before using a combination of predictive AI and machine learning. 

In this blog, you will find 9 essential areas where agentic AI can benefit financial crime prevention teams. Learn how it can revolutionize the way firms approach this critical issue and how best to implement it using SymphonyAI software. 

What is agentic AI and what are AI agents? Is there a difference? 

Agentic AI is an important next phase of AI evolution, comprising two types— copilots and autonomous AI agents.  

• Copilots are AI models that respond to prompts or execute predefined tasks, assisting human investigators.  

• Autonomous AI agents, also referred to as agentic process automation (APA), work dynamically within predefined work scope and requirements. These AI agents can make decisions, plan, and learn from previous experiences. They can even collaborate with one another to seamlessly complete complex workflows.  

Agentic AI technology is already in use with the Sensa Investigation Hub. SymphonyAI plans to roll further agentic technology across the full suite of financial crime prevention products.  

 Unlike AI copilots that support human investigators, autonomous AI agents take the lead in investigations, working together to analyze vast amounts of data, uncover hidden risks, and enhance decision-making. As with all AI used in financial crime prevention, human oversight is always present as a safeguard. 

9 essential benefits of AI agents in financial services 

#1. Enhanced capability across organizations

Traditional systems often struggle with the sheer volume and complexity of financial transactions, especially as financial regulations become more complex. Agentic AI excels at processing vast amounts of data with speed and precision, identifying potentially fraudulent activities that may go unnoticed by investigators. It isn’t a case of human error; AI is privy to machine learning and advanced algorithms that can spot patterns more quickly than their human counterparts. 

Moreover, AI agents can automate interactions with multiple third-party tools to perform dedicated tasks that standalone language models were not designed for, including quantitative calculations (which can then be formulated into a table) or browsing websites for adverse media mentions. 

Using a blend of predictive AI and generative AI, this increased capability means organizations can use AI agents in financial services to more effectively safeguard against unauthorized transactions, money laundering, and other forms of financial crime. 

#2. Improved productivity in banks and other financial institutions

AI agents can complete complex workflows by working together effectively from a single human-entered prompt. By automating many of the labor-intensive processes involved in anti-money laundering (AML) transaction monitoring and fraud detection, using agentic AI in financial services significantly boosts productivity. Routine tasks that once required hours of manual oversight and explanation can now be executed in seconds.  

This automation not only frees up valuable human resources to focus on more complex or strategic initiatives but also ensures that threat detection is both continuous and timely. As a result, companies experience a more efficient operational workflow. 

#3. Self-learning systems that adapt autonomously

Unlike static rule-based models, agentic AI continuously evolves by learning from new behaviors and emerging risks. 

This self-learning capability means that the system becomes increasingly adept at spotting anomalies over time, which improves the AI agents being used for specific tasks. The AI adapts to the ever-changing landscape of financial crime, providing a defense mechanism that is always up to the minute. 

#4. Transparency and trust within banks and financial institutions

Transparency is critical in establishing trust with stakeholders. AI agents are designed with clear, explainable processes in mind, and each has set jobs and parameters that provide stakeholders with insights into decision-making and threat detection.  

This transparency over what occurs ‘behind the scenes’ helps build confidence both internally and externally, reassuring financial crime prevention teams and regulators. Alongside this, it shows an increased commitment to regulators that an organization is implementing the most recent and up-to-date AML software and is taking financial crime prevention seriously. Finally, by analyzing how the AI agent operates on the assigned processes, organizations can more effectively assess how the agentic AI is performing and understand the agents’ approach, tweaking outcomes as necessary. 

#5. Agentic AI offers unmatched adaptability

Financial criminals are constantly developing new tactics and using new methods to bypass security measures and regulations. Because the finance industry is global, newly emerging threats can quickly cross the world, meaning that banks and other institutions need to constantly be aware of the latest developments and how best to combat them.  

Thanks to its adaptability, agentic AI in financial services swiftly adjusts to these evolving concerns. AI agents can recalibrate their parameters and develop new strategies in real-time to identify suspicious activities, by analyzing previous data sets and identifying how best to mitigate them. This adaptability ensures that financial institutions remain one step ahead of bad actors, maintaining the integrity of financial systems. 

#6. Superior accuracy within financial crime prevention software

Accuracy is paramount in anti-financial crime software. Huge numbers of false positives can lead to unnecessary manual reviews, disrupting workflows and increasing costs. Reducing false positives accurately — by preventing criminal transactions but allowing all genuine payments to proceed — is a top priority for all banks and financial institutions. 

 AI agents can cross-validate findings, ensuring investigations remain highly accurate while reducing compliance burdens by:  

• Reducing unnecessary manual reviews 

• Increasing efficiency in AML transaction monitoring 

• Supporting targeted interventions

#7. AI agents in financial services provide increased intelligence

Human investigators are vital to the smooth running of financial institutions, using their experience and industry knowledge to ensure that financial crime is sought out and stopped before it goes ahead. Unfortunately, investigators are limited by the number of members within their team and can only do so much. Working in tandem with AI agents can provide that extra expertise to go the extra mile. 

As such, agentic AI provides organizations with a heightened level of intelligence about their financial operations. Through sophisticated analytics and reporting, organizations gain insights into transactional patterns and potential vulnerabilities. This intelligence is invaluable in forming proactive strategies and continuously improving security measures against financial crime. 

#8. Cost-effectiveness for financial institutions

Implementing strong anti-financial crime measures is often associated with high costs. However, by shifting away from a reliance on manual labor, autonomous AI agents offer: 

• Lower operational costs for banks and other financial institutions 

• Faster compliance processes 

• Improved return on investment (ROI) 

Additionally, SaaS-based deployment enables scalable adoption without costly infrastructure investments. 

#9. Agentic AI provides proactive risk management

Agentic AI empowers organizations to engage in proactive risk management rather than reactive damage control. By identifying potential threats before they materialize, companies can take pre-emptive measures to avert financial loss and reputational damage. This proactive approach is crucial for maintaining strong security in an ever-evolving threat landscape. 

SymphonyAI offers AI agents in banking and financial services 

As financial crime continues to evolve, organizations must implement the best tools available. SymphonyAI leads this innovation, offering AI agents as part of the full financial crime prevention suite and developing agentic AI solutions that redefine what’s possible in financial crime prevention. Sensa Investigation Hub allows investigators to interrogate the conversational agents within Sensa Copilot today to more effectively uncover financial crime.  

Improve your fight against financial crime with SymphonyAI’s anti-financial crime suite, with AML software, payment fraud solutions, and KYC/CDD tools.