Revolutionizing financial crime investigation – the power of agentic AI technology

Agentic AI-powered autonomous agents are set to transform financial crime detection and prevention 

In today’s rapidly evolving financial landscape, the fight against financial crime has never been more critical. As criminals become increasingly sophisticated, financial institutions are under immense pressure to detect, investigate, and prevent illicit activities such as money laundering and fraud. SymphonyAI’s groundbreaking autonomous agent technology will bring game-changing capabilities to financial crime investigation. 

The challenge of financial crime investigation 

Financial crime investigation has long been a complex, time-consuming, and resource-intensive process. Traditional methods often involve manual review of vast amounts of data, relying heavily on human expertise and judgment. This approach, while necessary, comes with several challenges: 

• Overwhelming volume of alerts: Anti-money laundering (AML) transaction monitoring and screening systems generate numerous alerts, many of which turn out to be false positives. 

• Time constraints: Investigators are under pressure to review cases quickly, potentially missing crucial details. 

• Inconsistency: Human investigators may interpret data from AML software differently, leading to inconsistent outcomes. 

• Limited scalability: As transaction volumes grow, it becomes increasingly difficult to scale human investigation teams. 

These challenges underscore the need for a more efficient, accurate, and scalable approach to financial crime investigation. SymphonyAI is developing agentic AI technology – autonomous agent software. These agents will be available within the financial crime compliance suite. 

What is agentic AI? 

Agentic AI technology is an important next phase of AI evolution, comprising two types— copilots and autonomous agents. Copilots are AI models that respond to prompts or execute predefined tasks. Autonomous agents, also referred to as agentic process automation (APA), work dynamically within predefined work scope and requirements. As such, the AI can make decisions, plan, and learn from previous experiences. These autonomous AI agents can even interact with one another, helping each to achieve the specific task for which it was intended. 

AI agents in the form of copilots are already available to Sensa Investigation Hub customers. SymphonyAI plans to roll further agentic technology across the full suite of financial crime prevention products. 

SymphonyAI’s powerful and innovative agentic AI technology can work as a team of autonomous agents that can rapidly conduct thorough, consistent financial crime investigations. This might mean a research agent looking at adverse media interacting with an agent dedicated to monitoring transactions. Unlike AI copilot assistants that support human investigators, autonomous agents take the lead on the investigation process, with human oversight as a safeguard. 

How do autonomous AI agents differ from generative AI? 

Autonomous AI agents are designed for decision-making and autonomy. Their primary focus is on executing tasks, making decisions, and adapting to different situations based on set goals. Operating with a certain level of independence, autonomous AI learns from its interactions and feedback loops. This type of AI is found in applications like autonomous vehicles and virtual personal assistants, where it can take actions in real-world environments. 

Generative AI, meanwhile, is all about content creation and (contrary to its name) synthesizing information, which it achieves based on patterns and relationships learned from actual data. It excels at generating new text, images or other forms of media by using models like neural networks to mimic the patterns of its training data. Generative AI is commonly used in content creation, art generation, and language models such as ChatGPT or Perplexity AI. Essentially, while agentic AI focuses on autonomy and action, generative AI specializes in producing creative outputs based on learned data patterns. 

What can autonomous agents do? 

Agentic AI is useful across many different industries, helping to improve efficiency and productivity. Within financial services, SymphonyAI’s autonomous agents can perform a wide range of tasks crucial to financial crime investigation. These agents work through several steps when investigating, with the example below highlighting how they work in AML transaction monitoring. 

Example: Using autonomous agents powered by agentic AI software in AML 

Financial institutions can tweak the agentic AI approach with customizable workflows, tailoring the investigation process to their specific needs and regulatory requirements.

It is important to note that the entire process takes minutes rather than hours or even days as happens with an investigation currently.

1. Multi-agent workflow: The software employs a variety of specialized agents, each with a unique role and expertise. Autonomous AI agents all use natural

language processing and can understand and generate readable text that is easily interpreted by reviewers.

Agent types include:

• Investigation Manager: Oversees the investigation process and makes key decisions.
• Transaction Analyst: Analyzes financial data and patterns.
• Research Agent: Conducts online searches for relevant information.
• Case Reviewer: Evaluates and provides feedback on investigation reports.

2. Modular investigation process: The investigation is broken down into three main modules:

• Transaction and account analysis: The agents analyze the primary account being investigated, its transactions, and associated accounts (those that have received or sent money from the investigated account). By analyzing multiple factors, agents can evaluate the level of risk associated with these transactions or entities.
• Web research: The research agent scours the internet for reports that feature the name of the account holder. It’s important to note that the research agent will look beyond the first and last name, making sure that other identifiers also match, exactly as a human investigator would do.
• Case report drafting: The information is collated into a report that is then passed on to a human investigator.

3. Adaptive learning: Agentic AI learns from human feedback meaning that autonomous agents continuously improve their performance for future investigations. While capable of working on their own, the agents also interact with human investigators, providing insights and responding to queries. As new data becomes available, agents can also adjust their analysis and conclusions accordingly.

Benefits of autonomous AI agent software in financial crime investigation 

The implementation of autonomous AI agent software offers numerous benefits to financial institutions. SymphonyAI’s autonomous agents offer: 

• Enhanced efficiency: Agentic AI can process vast amounts of data and conduct investigations much faster than human investigators, significantly reducing the time required to review cases. 

• Improved accuracy: By using AI machine learning (ML), the software can identify subtle patterns and connections that might be missed by human investigators, reducing false positives and negatives. 

• Consistency: AI agents apply the same rigorous approach to every investigation, ensuring consistency across cases. 

• Scalability: The software can easily handle increasing volumes of transactions and alerts without the need for proportional increases in human resources. 

• Cost-effectiveness: By automating much of the investigation process, financial institutions can reduce the costs associated with manual reviews. 

• Continuous learning: The system’s ability to learn from human feedback means it can continuously improve its performance over time. 

• Audit trail: The software maintains a detailed record of the investigation process, making it easier to demonstrate compliance to regulators. 

• Risk mitigation: By enabling more thorough and consistent investigations, the software helps financial institutions better manage their risk exposure. 

How can a financial institution prepare itself for agentic AI? 

To prepare for agentic AI, a financial institution should begin by strengthening its infrastructure and technology. This involves building a robust IT framework that can support AI capabilities, ensuring proper data management, and fostering a culture of innovation. Having standardized data formats across the company is crucial for agentic AI to operate most effectively. 

Maintaining strong cybersecurity measures and privacy safeguards to protect customer data is a must, while educating staff and investing in AI-related training programs is also needed. Behavioral standards and risk management frameworks should also be developed to oversee AI deployment, ensuring responsible use and compliance with regulations.  

To benefit from best practice in using agentic AI and autonomous agents, engaging in strategic partnerships with other firms and industry groups can provide access to key ideas and information that helps to benefit the industry. Alongside this, keeping customers informed about AI initiatives and actively seeking their feedback ensures transparency and helps refine AI applications to improve services. 

The future of financial crime investigation 

As financial crimes become increasingly complex and widespread, the need for advanced investigation tools has never been greater. SymphonyAI’s autonomous agents, powered by agentic AI software, represents a significant leap forward in the field of financial crime detection, investigation, and prevention. 

By harnessing the power of AI and machine learning, autonomous agents enable financial institutions to conduct faster, more accurate, and more consistent investigations. As the system learns and improves over time, it has the potential to revolutionize financial crime prevention, setting a new standard for the industry.