How AI Agents in Finance Are Driving Reduced Risk and Increased Scalability

Improved Fraud Detection

Fraud detection is the act of identifying suspicious behavior on financial accounts. Such examples include the criminal theft of individual or company funds. Unfortunately, traditional AI and GenAI can only flag suspicious behavior based on predefined rules; it is up to human operators to take an appropriate response.

AI agents, on the other hand, can go further by classifying the transaction’s threat level based on its time, location, and frequency. It can then determine whether to allow, flag, or stop the transaction. This real-time decision-making allows AI agents to help eliminate fraudulent activity faster and more accurately than ever.

Research shows that 99% of financial organizations are already using some form of ML or AI to combat fraud. And 93% of those respondents believe that AI will have a positive impact on combating fraudulent activity.

Minimized Investment Risk

The finance sector is rife with risk and high-stakes opportunities. Even the slightest judgment error could cost customers a fortune. A well-calculated investment could yield generous returns, increasing customer satisfaction and brand credibility.

AI agents are an effective risk management tool in finance. Equipped with guardrails, memory, and instructions (functions that we’ll explain later), they ensure the AI agent operates within safe boundaries to avoid mistakes and risks.

AI agents can also filter out and block malicious or incorrect inputs, preventing policy or scope violations. And, if the AI agent exceeds a specific threshold, then a human operator can intervene to determine if they should allow, modify, or stop the output.

Increased Scalability

As finance leaders continue to service more customers and gather more data, they must also increase their headcount and resource allocation. This can be difficult to do during the rapid growth phase, where demand exceeds business capability.

AI agents allow financial services institutions to scale upward without investing in increased headcount and resources. AI agents can be programmed to interact with more APIs, expanding their capabilities beyond their initial inception. They can also adapt to dynamic market conditions, processing new information and responding accordingly in real-time with predictable outcomes.

Continuous Learning and Adaptation

The finance sector is constantly changing. This is especially true for finance teams monitoring market trends and responding to sudden social, political, or institutional events that influence investor performance. Hence, there is a need for rapid, low-latency decision-making.

The great thing about AI agents is that they continue to learn and grow the more you use them. One day, an agent may require human intervention to make a crucial decision. But next, they may be able to perform that same task autonomously.

Luxury company LVMH, whose brands include Dior and Tiffany, recently deployed autonomous AI agents to adjust prices in real-time based on currency fluctuations, protecting its profit margins in the face of unpredictable market changes. Its sales advisers also use agents to summarize previous customer interactions, using that data to deliver personalized sales messages.

AI Workflows

AI workflows perform specific tasks based on predefined, step-by-step sequences. And they do so in a linear fashion with measurable, predictable outcomes. Due to the high-risk and highly-regulated nature of the finance sector, AI workflows are the preferred choice. They offer the highest level of control and predictability.

AI Agents

AI agents have a higher level of autonomy than AI workflows. They utilize CoT prompting to break down complex tasks into smaller logical steps. This aims to produce more accurate and reliable outcomes of complex predictions.

The downside to AI agents is that they are more expensive and less predictable than AI workflows. This makes them riskier to use in the finance sector. Partnering with an experienced data and AI services company like Orient Software can help you determine which AI solution is best for your business.

Instructions

Instructions outline the steps that the AI agent must follow to operate correctly. Prompt engineers prepare well-crafted instructions that define what the agent does, how it does it, and why it does it. Prompt engineers also put in place constraints, ensuring the agent operates within safe boundaries.

Even in the best of circumstances, AI agents are still unpredictable. Therefore, prompt engineers should be ready to continuously refine the instructions, constraints, and assumptions that they provide their agents.

LLM Tools

LLM tools are the functions that you make available to an AI agent. The AI agent accesses these external tools through APIs, allowing the agent to utilize the functionality of a separate program.

The most common LLM tools for AI agents include data retrieval, computation, and action execution. For example, an AI agent may use a Python tool to calculate the potential return on an investment based on market conditions.

Information Retrieval

AI agents in finance rely on high-quality data to produce reliable, trustworthy outputs. The two most common information retrieval layers include internal and external layers.

Internal information retrieval helps keep your AI agent grounded in reality. It uses the retrieval-augmented generation (RAG) method to surface internal data from vector databases, as well as SQL agents to gather data from proprietary tables.

External information retrieval involves using web and news search APIs to gather data from outside sources like the internet. These tools autonomously perform multiple search queries to gain a deep understanding of a particular topic. Then, they enter a loop – repeating the process several times until they gather enough information to produce the desired output.

Memory

Memory allows an AI agent to retain context and data that it can recall later. The two main types of AI agent memory are short- and long-term memory. Most AI finance agents have both memory types to serve different purposes.

Short-term memory retains the context of immediate information, such as conversation history. Think of it like a temporary notepad – holding on to certain information for just long enough to finish an existing task.

Long-term memory retains persistent knowledge accumulated over a longer period of time. It stores data in a vector database. The AI agent can then retrieve this long-term data through a RAG system at any time.

Guardrails

Guardrails ensure that an AI agent operates within predefined rules, limitations, and restrictions. Such restrictions may be industry, as is to be expected in the highly-regulated financial services industry, or they may be institutional, so as to comply with company policy and guidelines.

Common AI agent guardrails include input filters, tool-call gatekeepers, and output checks.

Input filters block or sanitize malicious or off-topic inputs that violate policy. Tool-call gatekeepers determine the extent to which APIs and other third-party sources can interact with the AI agent. Output checks can scan outputs for compliance purposes. For example, an agent may block an output from generating data labelled as “confidential.”

Another type of guardrail is having a human-in-the-loop. This is where an AI agent cannot perform a specific action until a human has reviewed the output. Since the stakes are so high in the finance sector, human-in-the-loop guardrails are essential in risk management and protecting investor capital.

Fraud Detection and Prevention

AI agents can identify suspicious behavior and intervene before disaster strikes. And they can do so with minimal or no human intervention. The result is faster decision-making and reduced risk for finance customers.

Let’s say a customer regularly uses their debit card in New York City. Then one day, the card processes several high-value transactions originating from Australia. In response, the AI agent flags the transactions as suspicious, notifying the cardholder via email or SMS. The cardholder can then approve or deny the transactions.

Customer Service and Engagement

Good customer service is paramount for a financial institution. AI agents are helping customer support teams offer more personalized guidance and support.

Instead of waiting for a human operator via phone or email, AI agents can autonomously answer questions, address queries, and direct customers to relevant resources. This helps reduce waiting times and increase customer satisfaction rates.

Research shows that AI agents can help cut response times by up to 74%, down from 8.2 minutes to 2.1 minutes.

Credit Scoring and Risk Assessment

Traditional credit scoring and risk assessment have their downsides. It often leaves under-banked individuals out in the cold. Without an existing banking history, under-banked individuals cannot prove they are eligible for a loan or new credit.

AI agents can review a wider range of data sources than traditional credit scoring and risk assessment tools. They can review employment records, transaction history, and even behavioral signals (e.g., loan application attempts, bill payment patterns).

This helps make it easier for under-banked people to more easily access loans and other financial products.

Financial Forecasting and Planning

AI agents can help give wealth managers greater visibility into current and potential future market conditions. By analyzing internal and external data, AI agents can project realistic and viable financial forecasts.

Agents can even modify their forecasts in real-time in the wake of unexpected changes. For example, a sudden increase in the cost of procuring raw materials, which, in turn, will influence the projected profit margins.

Regulatory Compliance

AI agents can help financial institutions enforce compliance. And with minimal, if any, human intervention.

Instead of waiting for periodic reviews to ensure compliance, AI agents can continuously monitor documentation, transactions, and reports against predefined standards. For example, an agent can ensure that customer-facing communications comply with company policy and fair lending standards.

When a change is introduced, the agent can flag the change and recommend adjustments, reducing the risk of non-compliance.

Auditing

With the support of agentic AI, experienced auditors can save time on repetitive, predictable tasks. This gives them more time to focus on more complex tasks, such as strategy and consulting.

AI agents can be deployed to execute predictable workflows with less supervision, maintain memory across interactions, and request human-in-the-loop action for specific events. In doing so, auditors can more efficiently adapt to changes and make sound judgment calls.

For example, an auditor can deploy an AI agent to compare a list of terminated employees to payment records, identifying patterns and flagging discrepancies for review. This level of adaptability would make it easier for auditors to validate all aspects of a business with greater ease.