The frequency and sophistication of financial fraud are surging like never before. Unfortunately, traditional fraud detection and resolution approaches fail to live up to expectations. This is compelling fintech firms to turn to artificial intelligence trends like agentic AI for effective, timely, and intelligent fraud detection. 

Agentic AI systems can automate critical investigation and compliance processes, allowing risk teams to focus on complex cases while ensuring accuracy and regulatory compliance. 

Let’s take an example. It's a typical Monday morning. Amid back-to-back meetings, Jane receives an email from internal support informing her about a possible cybersecurity risk due to her compromised password and requesting she change it using the link provided. Without thinking twice, Jane clicks on the link and changes her password. 

Unbeknownst to her, the email carrying the company’s branding and typical professional tone was sent by a hacker. This successful phishing attempt has now opened doors to several opportunities for fraud, including financial losses, data theft, malware, and more. 

This blog will showcase where traditional fraud detection techniques fall short and why AI systems are the only way forward. 

Traditional Fraud Detection Limitations 

Account takeover fraud poses a significant threat to the fintech sector. Today’s hackers use various techniques to gain unauthorized access to user accounts, including phishing, social engineering, malware, and stolen credentials. 

Many firms rely on traditional fraud detection measures to combat emerging threat vectors despite the high stakes. For victims like Jane, these techniques bring several limitations:

• Rule-based and reactive: Traditional fraud detection employs rule-based systems. In the event of an account takeover, pre-defined patterns would include unusual login locations or deviation from typical user activity. If the hacker uses a different IP to log in, the system might be unable to detect it. 

• Limited scalability: Manual techniques of dealing with account takeovers leave fintech firms with massive scalability challenges. Resource-intensive and time-consuming, this approach requires substantial human intervention to analyze flagged activities. Any increase in accounts or data will delay detection and response. 

• Poor efficiency: Manual approaches to account takeover fraud are highly inefficient due to the high human involvement. Slow and inconsistent examinations of logs can lead to poor decision-making, especially for complex fraud patterns. 

• Delayed time-to-resolution: Traditional approaches to fraud detection are known to be slow. This tends to lead to higher operational costs while exposing the organization to a greater risk since the threat of any possible attack might take longer to identify and respond to. 

• High rate of false positives: Rules-based techniques can also be susceptible to false positives. Pre-defined patterns and subjective judgments can falsely flag legitimate user activities as fraudulent. For example, if Jane accesses her account from her new phone, the system might, unfortunately, flag her log in as suspicious. This can lead to unnecessary delays, frustration, and legitimate accounts getting locked out or subjected to further scrutiny.

How Agentic AI Fills the Gap 

For victims like Jane, agentic AI systems pave the way for smarter and more adaptive fraud detection and resolution. These AI systems can respond to suspicious behavior through real-time monitoring of systems and networks. They can uncover patterns, reduce false positives, and learn as they go – thereby enhancing safeguarding businesses and maintaining customer trust. 

Let’s look at how AI systems score over traditional fraud detection: 

• Proactively Detects Anomalies: Agentic AI systems work around the clock. They monitor online activity, identify normal behavior, and send alerts if they witness anything unusual. Instead of waiting for Jane’s account to be taken over to do their job, these AI agents can evaluate Jane’s transaction patterns and previous online behavior and alert her about the attempted fraud and prevent her from clicking on the link.  

• Automates Resolution: AI systems get to work immediately when Jane unknowingly changes her password through the link provided. They will lock her account and alert her (and the security team) of the fraud. To confirm the takeover, the agentic AI solution will also compare the hacker’s behavior patterns with Jane’s usual activity. As it analyzes the scam, it will prompt Jane for additional verification to regain access to the account securely.

• Conducts Root-cause Diagnosis: AI systems will process all the data leading to the incident to identify the reason for the account takeover. They will monitor online behavior over the past few weeks or months to analyze devices, locations, timestamps, etc.  In Jane’s case, the agentic AI solution will carefully study the genuine-looking phishing email she received, examine the language or phishing keywords, and verify the sender’s address, URLs, and attachments, if any. Once all the analysis is complete, the agentic AI system will refine the model to prevent similar attacks in the future. 

AI Agents for Fraud Detection – The Way Forward 

Fraud incidents, such as phishing, identity theft, and imposter scams, are on the rise, with account takeovers like the one Jane experienced surging by 24% year-over-year in 2024. Instead of relying on rule-based fraud detection systems, fintech firms must exploit the capabilities of AI systems.  

These agentic AI systems proactively safeguard financial and operational systems and prevent devastating losses. AI agents play a significant role in fraud detection and prevention by monitoring user behavior in real time and analyzing patterns. 

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