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Autonomous AI Agents in Fraud and Risk Management

Saurabh Bajaj

Nov 8, 2023

Leveraging the capabilities of autonomous AI agents in fraud and risk management can revolutionize how organizations approach their risk operations, offering a more nuanced, efficient, and proactive strategy.

Generative agents or virtual assistants that utilize large language models are the next frontier in machine learning algorithms. While the promise of general artificial intelligence may still be far away, smart agents can still provide tangible business benefits to organizations that deploy them.

In fact, Markets and Markets reports that the market for autonomous agents will grow from USD 4.8 billion in 2023 to USD 28.5 billion by 2028.

An autonomous agent (or rather a network of virtual assistants) can help a human user achieve their day-to-day goals as they can execute tasks on their own, cutting the time spent on menial tasks by human operators. What’s more, these smart robots can work in tandem with other agents that are suited for different tasks, in order to autonomously achieve the goals of your department or organization with human operators overseeing them.

In the world of fraud and risk management, we are used to dealing with different kinds of automated systems to deal with the near-infinite and ever-changing set of challenges that are peculiar to our industry. 

But with autonomous agents, we move beyond mere machine learning models to almost human intelligence territory, in which the role of the fraud manager changes from being a mere operator of a fraud management system into that of a commander of a fleet of smart robots.

Oscilar’s first-of-a-kind AI risk decisioning platform will make use of such agents to help our clients utilize generative AI technology at the forefront of fighting fraud.

As such, this article will walk you through, with examples, on how autonomous AI agents are transforming fraud and risk management, and what are their advantages over traditional rule-based or machine-learning systems.

The topics we will discuss are:

  • The evolution of risk management: from rules-based systems to autonomous agents

  • Understanding autonomous AI agents

  • The renaissance in fraud and risk management

  • Integrating agents for a cohesive strategy

  • The importance of an agent network in task planning

  • The road ahead: integrating autonomous agents in risk management

Let’s get started.

The evolution of risk management: from rules-based systems to autonomous agents

Traditionally, risk and fraud management relied on rules-based systems to counter bad actors and their various fraud schemes. Over time, these systems were bolstered by innovations in machine learning, scaling these rule-based systems to meet the demands of our ever-connected lives.

However, such fraud detection technologies, which we can call Risk 1.0 and 2.0 respectively, find themselves shackled when faced with new, unforeseen scenarios, unable to learn or adapt beyond their original programming, thus limiting their evolutionary trajectory.

Now we are at a pivotal juncture in the evolution of risk management technologies with the introduction of generative AI in fraud detection: Risk 3.0.

In fraud and risk management, Generative AI emerges as a dynamic force, capable of analyzing complex data patterns and adapting to emerging threats with agility. It can foresee potential fraud avenues by analyzing broader data sets, including market trends and customer behaviors, offering a proactive stance in mitigating risks and protecting organizational assets.

What’s more, generative AI can be reliably deployed in the form of autonomous agents, a type of virtual assistant that is developed with a specific use case in mind and is capable of acting both as a co-pilot and as a smart program that’s able to automate repetitive tasks.

To understand the technological aspects of this shift, read our article on generative AI use cases in financial services.

Now let's delve deeper into the world of autonomous AI agents and their potential applications in fraud and risk management.

Understanding autonomous AI agents

Autonomous agents in AI are intelligent systems capable of performing tasks with minimal human intervention. These agents can analyze complex data sets, identify patterns, and make decisions based on their analysis. 

In the context of fraud and risk management, these agents can be instrumental in identifying and mitigating potential risks before they escalate, offering a proactive approach to risk management.

Autonomous agents in fraud management

Root cause analysis agent

Root cause analysis (RCA) is a fraud prevention technique in risk management that aims to uncover the primary source or cause of an issue. 

The commonly known shorthand version is looking at a symptom, like a fraudulent transaction or false positive, and asking the question ‘Why?’ five times to find out what really led to that outcome.

RCA is typically deployed either in post-event analysis to uncover systemic vulnerabilities, or as a preventive measure to prepare against hypothetical threats, allowing organizations to deploy mitigation strategies in advance. 

As autonomous AI agents have access to data from various sources, they are designed to pinpoint the underlying causes of issues, helping organizations to prevent future occurrences.

For instance, RCA agents can answer questions related to recent surges in fraudulent activity, or uncover misconfigured security measures that are declining transactions that are legitimate.

The big advantage of relying on AI autonomous agents for RCA is that while the technique is the bread and butter of risk management, machines can conduct such analysis much faster than human operators. 

RCA requires pattern recognition, temporal and multi-factor analysis, and AI agents excel at such tasks, while humans have to query different databases, sift through data, and conduct the analysis manually.

Labeling agent

Data labeling, at its core, refers to the process of tagging or annotating data (often raw) to make it usable for machine learning models. In fraud detection, transactions can be labeled as "fraudulent" or "non-fraudulent" based on historical records. 

Feature identification, which is also done via labeling, is necessary to accurately identify which parts of the data are relevant for the given use case to weigh the model accurately.

Autonomous AI agents can help with this time-consuming process in several ways. 

By using active learning, agents can use a small set of labeled data to judge the rest of the set, leaving only ambiguous cases for human review. With the ability to do transfer learning, an AI model trained in fraud detection can quickly label data in an unrelated but similar data set for the same use case.

Furthermore, AI agents excel at creating synthetic data to enhance training sets for machine learning models, reducing the need for labeled real data altogether.

As data labeling is intensively time-consuming, labeling agents can make the process faster, getting more accurate over time and requiring less and less human oversight.

Customer support agent

While chatbot technology has been with us for years now, the new generation of generative AI technology significantly improves upon their abilities. As automating customer support tasks and especially customer conversations was one of their main use cases, autonomous AI agents are a natural choice for such functions.

The ability to understand context and deal with ambiguity allows AI agents to have more natural conversations than the traditional, scripted chatbots of the previous era. They can also be fed customer data, allowing them to create more personalized experiences for clients, which is increasingly important in areas such as finance.

Autonomous AI agents are also multifunctional, meaning that they can be configured to handle a multitude of specialized customer service tasks, such as providing support for documentation, helping customers with troubleshooting, or fulfilling FAQ functions.

Visualization agent

Data visualization in fraud and risk management is a cornerstone technique, both for getting the ‘big picture’ of recent trends, as well as in deep dives that are related to RCA. Currently, data scientists work with fraud managers to query the relevant databases and create visualizations for data analysis to understand what’s happening under the hood.

Autonomous agents that specialize in visualization will transform this process. 

An analyst can talk to the agent and request visualizations, highlights, and even analysis. The agent then transforms complex data into visual formats, helping the analysts in identifying patterns and trends that might indicate fraudulent activities.

Network agent

By functioning within network environments, these AI agents can actively monitor, learn from, and respond to potential threats in real-time. Network agents can monitor vastly more information than human agents, spotting irregularities that can be indicative of fraud attacks or security breaches (such as picking up a vast number of ATO attempts automatically).

Autonomous agents can also monitor network-wide behavior, establishing a baseline of expected actions and transactions and quickly flag derivations from the norm. 

Furthermore, by leveraging graph analysis, network fraud detection ai agents can spot otherwise hidden or hard-to-detect connections between different entities in the system (a common modus operandi of sophisticated fraud rings), and recommend actions automatically. 

As the agent is integrated into the fraud detection stack, these actions can range from blocking certain attributes (like credit cards or IP addresses) to setting up new rules or flagging different cases for manual review, as in automated transaction monitoring.

Knowledge agent

Experienced fraud and risk managers are known for their extensive knowledge of historical cases, best procedures, regulatory and compliance information, as well as worldly knowledge.

A knowledge agent is a repository of information, having access to all the relevant data in the system and insights, and can serve as a co-pilot when it comes to decision-making on all levels. Experienced analysts can rely on them to collect all the relevant info regarding a case as well as advice on what decisions to make, while newer colleagues get the benefit of coaching.

Maps agent

Geospatial data is invaluable when it comes to fraud and risk management, as it can be used to spot anomalies based on space (distance) and time. 

Maps agents utilize geographical data to analyze patterns and trends in fraud cases, helping in the identification of fraud rings operating in specific regions, or detecting anomalous behavior on a geographical level. 

AI for fraud prevention can uncover new high-risk areas that are favored by fraud rings targeting your company, or spot threat actors acting in a timed manner across different regions.

Identity agent

Customer identity verification relies on a multitude of tools from KYC verification processes to advanced technology ranging from behavioral analysis all the way to biometric and facial recognition systems.

AI identity agents tie the data from these systems together, allowing them to conduct analysis, and make the appropriate decisions, making them invaluable tools in fighting identity-related fraud.

The renaissance in fraud and risk management

The metamorphosis from rules-based systems and machine learning to Generative AI and autonomous agents has catalyzed a renaissance in the field of fraud and risk management. 

We are entering a new era of proactive fraud detection and enhanced decision-making characterized by intellectual depth and agility. 

Let's delve deeper into the nuances of this transformation:

  • Proactive fraud detection: a new frontier
    While traditional fraud prevention systems operate based on predefined rules, identifying fraudulent activities based on historical data, generative AI heralds a new frontier in proactive fraud detection. 

    AI-powered fraud detection ML can predict potential fraud patterns before they manifest, analyzing and learning from a broader and more complex set of data, including emerging trends and patterns. 

    A proactive stance is pivotal in mitigating risks at an embryonic stage, potentially shielding organizations from substantial financial and reputational losses.

  • Enhanced decision-making: the intellectual approach

    Generative AI facilitates a more intellectual approach to decision-making in risk management. It can dissect vast datasets, uncover hidden patterns, and provide profound insights that assist in making well-informed, strategic decisions. 

    Intellectual prowess is a significant leap from traditional AI, which is confined to a fixed set of rules and cannot adapt to the fluidity of changing patterns or trends.

  • Dynamic response to emerging threats: the agile defender

    Agility is a prized asset in the ever-changing landscape of cyber threats and fraud. AI agent risk management with its learning and adaptive capabilities, embodies this agility, evolving its strategies and solutions in real-time to offer a robust defense against sophisticated and continuously evolving fraud schemes.

  • Contextual understanding and analysis: the deep dive

    Generative AI introduces a deeper level of understanding to fraud and risk management by analyzing the context behind data and transactions. It can identify subtle, complex patterns in data that would be impossible or extremely time-consuming for a human or traditional AI to spot. 

    Contextual understanding allows for a more nuanced approach to risk management, where decisions are based on a deeper understanding of the underlying factors and potential implications.

  • Automating complex processes: the efficient maestro

    AI agent fraud management stands as a maestro in automating complex processes, which were previously reliant on human intervention. It can handle multi-faceted tasks, analyze complex data structures, and generate insights, making the process more efficient and less prone to human error.

Integrating agents for a cohesive strategy

In the grand scheme of things, these agents are not just individual entities operating in isolation. They are cogs in a larger machine, each contributing to a cohesive strategy aimed at revolutionizing fraud and risk operations. 

For instance:

  • The network agent could identify a potential security breach

  • Work in tandem with the identity agent to verify the identities involved

  • While the knowledge agent provides insights to aid in decision-making. 

A collaborative approach ensures a more robust and comprehensive strategy, where complex tasks are handled efficiently, paving the way for autonomous risk management.

The name of this strategy is called an autonomous agent network, which orchestrates the different kinds of agents to work in unison towards achieving your goals.

The importance of an agent network in task planning

While an individual AI agent may be a powerful tool, these smart assistants really shine in a well-organized agent network. 

The agent network serves as the backbone of a cohesive strategy, where various agents work in harmony to accomplish complex tasks that require multi-faceted approaches and collaborative efforts. Let's explore the critical aspects that underline the importance of an agent network in task planning:

Delegation and autonomy

An efficient AI agent network thrives on the principles of delegation and autonomy. Agents are assigned specific roles based on their strengths and capabilities, allowing them to focus on particular aspects of a task. 

Task delegation ensures that each agent can operate given a task list autonomously within its domain, contributing to the overall goal without constant supervision, thereby speeding up the process and enhancing efficiency.

Inter-agent communication

For a network to function seamlessly, communication between agents is vital. Agents need to exchange information, share insights, and collaborate to make informed decisions. 

Inter-agent dialogue facilitates a cohesive approach to problem-solving, where insights from one agent can aid others in fulfilling their roles more effectively.

Avoiding recursive behavior and hallucinations

A well-designed agent network is equipped with mechanisms to avoid pitfalls like recursive behavior, where an agent might get stuck in a loop, repeating the same actions without making progress. 

Similarly, agents are programmed to avoid hallucinations, where they might perceive non-existent patterns or trends. These safeguards ensure that agents remain focused on their tasks, contributing positively to the overall strategy.

Maintaining long conversations and collaborative efforts

In the context of fraud and risk management, tasks often require sustained efforts and long conversations between agents. Agents need to maintain a dialogue, discuss developments, and share insights over extended periods. 

Continuous communication ensures that agents can adapt to changing circumstances, modifying their strategies based on the latest information, and working together to achieve the common goal.

Multi-team collaboration

Complex tasks often necessitate the involvement of multiple teams, each bringing a unique set of skills and perspectives to the table. 

In an agent network, this collaboration is facilitated through a structured approach, where teams work together, complementing each other's efforts and contributing to a comprehensive strategy. 

Multi-team collaboration is vital in tackling complex issues, offering a more rounded approach to fraud and risk management.

By fostering delegation, autonomy, communication, and collaboration, this network ensures a cohesive and efficient approach to problem-solving. 

As we venture further into this domain, the role of agent networks will undoubtedly become more pronounced, offering a promising avenue for innovation and progress in the fight against fraud and financial crime.

Balancing control and autonomy: will autonomous agents ever be reliable?

As we venture deeper into the realm of autonomous agents, a pertinent question arises: Can we rely on these agents to make critical decisions autonomously? 

The answer lies in the balance between control and autonomy. While these agents are designed to operate independently, a certain level of human oversight is essential to ensure reliability and prevent potential misuse.

For instance, in the financial sector, an autonomous agent in fraud management could be employed to monitor transactions and identify potential fraud cases. 

However, the final decision on whether to flag a transaction as fraudulent might still require human intervention, ensuring a balance between automation and human judgment.

What does it mean for an AI agent to be believable and human-like?

As AI technology advances, the line between human and machine is becoming increasingly blurred. 

In the context of customer service, for instance, AI agents are now capable of understanding and responding to complex queries in a manner that is almost indistinguishable from a human agent. 

Such human-like capability not only enhances the customer experience but also adds a layer of security, as these agents can be trained to identify and respond to potential fraud attempts in a more nuanced manner.

Innovation in fraud and risk management usually mirrors innovations in fraud techniques. Autonomous AI agents aiding the risk managers of the future come at a time when the same intelligent agents will be widely available for nefarious actors as well.

Fraudsters have utilized bots and automation technology since time eternal to scale their attacks, and generative AI will allow them to launch new bot attacks with human-like behavior, increasing the effectiveness of phishing and social engineering attacks.

Down the line this will mean an increased number of threats on the bottom line of businesses, meaning that augmenting risk analysts via the same agent technology will quickly become a necessity in grappling with the increasing volume of sophisticated attacks.

The road ahead: integrating autonomous agents in risk management

As we navigate the complex landscape of fraud and risk management, the integration of autonomous AI agents presents a promising avenue for innovation and efficiency. 

By leveraging the capabilities of these agents, organizations can develop more robust and proactive strategies, helping to protect their assets and customers from the ever-evolving threats of fraud and financial crime.

The road ahead is promising, and with the right approach, the integration of autonomous AI agents can revolutionize the field of fraud and risk management, paving the way for a safer and more secure financial ecosystem.

Next Steps: How to get started with generative AI risk decisioning for your business

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