What Is Bot Detection: Tools, Techniques & Ways to Prevent Against Bot Attacks

Bots have become an integral part of many customers’ everyday routine. Chatbots and virtual assistants, for example, facilitate interactions with businesses, financial institutions, and banks. But while some bots are designed to help, others are designed for harm. These malicious bots can create fake accounts, steal sensitive information, transfer money quickly, and more. 

Bot-generated traffic now constitutes nearly half of all web traffic. Of this, malicious “bad bots” are responsible for one-third of all bot-related activity, according to the 2025 Thales Bad Bot report.¹ This makes bot detection a critical component to protect customers.

Let’s explore how bot detection works.

Key Takeaways

• Bot detection refers to identifying legitimate or malicious automated programming activity on a website or mobile application.
• It’s estimated that bots account for half of all web traffic; one-third of this traffic is driven by malicious bots (Thales Bad Bot Report, 2025).²
• Businesses worldwide lose $186 billion annually to bot attacks (Thales Bad Bot Report, 2025).¹
• To effectively protect against bots, banks need a combination of behavioral biometrics, device intelligence, and real-time monitoring.

What is Bot Detection?

Bot detection is crucial to catching several fraud tactics, including account takeover (ATO), new account fraud, and social engineering attacks. The barrier for writing bots is also very low, prompting the rise of an entire “Bots-as-a-Service” market, meaning almost anyone can create and deploy a malicious bot. 

The question of whether the person on the other end of an interaction is a human will become increasingly crucial as bot activity surges online. Research from Thales reveals that traffic makes up more than half of all web traffic.¹

Put another way, the likelihood of knowing if you’re dealing with a person or a bot is as good as a coin flip. Here are some other notable numbers to consider about bot attacks:

• Global businesses lose $186 billion to bot-related attacks each year (Thales, 2025)¹
• An estimated 9 billion bot attacks were recently reported in an 18-month period (Akamai, 2023)³ 
• Bot-related security incidents rose by 28% in 2023 (Thales, 2025)¹
• Bad bots accounted for 37% of all internet traffic reported in 2024 (Thales, 2025)¹
• Account takeover fraud costs an average of $12,000 per victim (Javelin, 2020)⁴
• In Brazil (where Feedzai protects 60% of the payments market) bot-driven attacks targeting instant payment systems, such as Pix, are a top concern for financial institutions.

Bear in mind that it’s not just the clearly “bad” bots that deserve our focus. With the rise of agentic AI, bots that crawl websites for information, sometimes to feed generative AI models like ChatGPT, it’s essential to distinguish between helpful and harmful automated traffic.

What are ‘Harmless’ Bots? Why Are They Important?

Malicious bots pose a significant concern. But bear in mind that while not all bots are out to steal money, even the “good” ones can cause headaches.

• Analytics Pollution: Agentic AI crawlers can skew your website’s data, including engagement metrics, time-on-page, and conversion rates, creating an inaccurate picture of what’s really happening. 
• Resource Drain: Every single visit or request, even from a friendly AI crawler, consumes precious computing power. That means unnecessary API calls, database hits, and content rendering, which can rack up costs without a human ever even seeing the result. 
• Trust and Safety Implications: While some agentic AI bots are genuinely benign, others might be testing the waters for more nefarious activities. This could include IP theft, “prompt injection” testing, such as attempting to trick AI into performing an action it shouldn’t, or even automated social engineering. 

There’s also plenty of buzz about personal AI agents that could soon interact with websites on our behalf. When agentic AI becomes widespread, how will we be able to distinguish between “good” automation and malicious actors causing trouble? Solutions are emerging, such as a “zero-trust” approach, where only bots that can cryptographically prove their identity are allowed. 

However, a key question remains: Can AI agents be abused? Will agentic AI create more friction for legitimate users? Does it inadvertently stifle the growth of independent AI development? It’s a fascinating future and one that requires sophisticated bot detection to navigate.

Immediate Ways to Detect Bot Traffic

With bots now accounting for half of all web traffic, businesses must be vigilant for warning signs that a malicious bot is behind an interaction.

• Unusual User Behavior: Consider how a user interacts with their device, including their mouse movements, keystrokes, and navigation of webpages. These subtle actions can often reveal whether a human or a bot is in control.
• Non-human Interactions: When it comes to speed, bots leave us humans in the dust! If a “user” is zooming through forms or hitting every mark with lightning speed, that’s a sign it’s not a person filling out forms. Also, genuine human behavior isn’t always perfect (how many times have you forgotten a password today alone?). We often fumble with our mouse, backtrack to re-read something, or even make typos. Bots, however, are too perfect. They don’t make these little “human” errors, which can be a giveaway!
• Unusual Traffic Spikes: Sudden, inexplicable surges in traffic, especially those coming from unfamiliar or unexpected geographical locations, are a troubling sign. This uncharacteristic activity often points to a coordinated bot attack.
• Device and Network Anomalies: Watch for telltale signs, such as repeated device fingerprints across different sessions or connections originating from known high-risk networks. 
• Repeated Failed Logins: It’s only human to forget your password sometimes. But a surge in attempted logins is a sign that it’s not a human trying to access an account. Bots can conduct brute-force attacks, resulting in a rise in failed login attempts, particularly with credential-stuffing bots or automated tools like OpenBullet.
• Fast, Repetitive Actions: Bots can automate repetitive tasks at high speeds, resulting in inflated traffic, skewed analytics, and malicious activities such as spam submissions, credential stuffing, and click fraud. Their volume and speed pose a significant challenge for maintaining data integrity and system security.
• Suspicious IP Addresses: Bots often operate from a limited set of IP addresses or cycle through a larger, yet still identifiable, range of “bad” IP addresses known for hosting malicious traffic. For example, some anonymity service platforms may offer rotating residential IP addresses.

“Most bot defense systems miss subtle inconsistencies that researchers and R&D teams, like ours at Feedzai, uncover through reverse engineering and deep analysis. This is why a layered approach to bot detection is critical.”

— Dan Holmes, Director of Fraud and Identity SME, Feedzai.

Techniques and Software to Detect Bots

Bots are getting smarter every day. Fortunately, the bot detection tools are also keeping pace and can quickly flag and detect suspicious behavior. Here’s a quick look at the top techniques and technologies that help spot bots before they can do any real damage.

• Behavioral Biometrics: Behavioral biometrics solutions study and learn a digital user’s unique partners, including how they type, swipe, touch their screens, or move their mouse. These behaviors are activities that bots can’t imitate. Just as importantly, this activity occurs silently in the background without interrupting the customer experience. 
• Device Fingerprinting: It’s not just about recognizing a device; it’s about understanding how that device is used over time, flagging anything that feels out of character for the user handling it. Note that traditional device and tracking solutions have seen a decline in their efficiency in detecting bots due to privacy updates and OS lockdowns. Along with a modern adaptive approach to device trust, the best way to detect bots and other malicious activity is to understand the human behavior and activity behind a device. It’s also vital to identify evidence of device tampering, such as someone trying to disguise a device’s identity.
• Individual Profiling: By learning each user’s typical habits and patterns, banks can quickly identify when something is off, such as a login from a new location or device.
• IP Anomalies: Monitor IP addresses for suspicious characteristics, such as known bots. This may include cross-referencing against blocklists or reviewing databases for problematic IP addresses.  
• CAPTCHA Challenges: Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCA) methods have become widespread when authenticating human behavior. They often involve solving puzzles or testing a user’s problem-solving skills to stump automated bots from performing actions. However, as criminals innovate their tactics, CAPTCHA tests have become less effective.
• Machine Learning Risk Engines: These innovative platforms combine data from devices, behaviors, and threat intelligence to identify risks in real time and adapt to new fraud tactics.
• Continuous Risk Assessment: Instead of checking for fraud at just one point, this approach monitors the entire customer journey, identifying subtle signs of trouble, such as social engineering or remote access.
• Group Profiling: This method compares a user’s behavior to that of similar customers, so even if someone is new, unusual activity stands out against what is normal for their peer group.
• Real-time Monitoring and Alert Systems: Implement continuous real-time traffic pattern monitoring to quickly catch irregularities or sudden spikes that indicate a bot attack in progress. 
• Observing Automation Footprints: Savvy detection tools can spot the digital “breadcrumbs” left behind by automation frameworks and environments. This includes looking for suspicious signals, such as a browser acting in a way that no human would, or specific indicators of automated scripts at work.  

“Feedzai provides behavioral biometrics capabilities as a part of its Digital Trust solution, providing a holistic approach for an end-to-end risk management lifecycle and, enabling the identification of fraudulent activities such as impersonation and manipulation fraud.”

— Quadrant Knowledge Solutions

The Threat & Business Costs of Bots

Bots are no longer a niche threat—they’re a mainstream, multi-billion-dollar problem for financial services and beyond. Here are some of the top threats they pose to businesses.

• Financial Losses: Automated bot attacks cost businesses up to $186 billion annually worldwide, according to the Thales Economic Impact of API and Bot Attacks report.⁵ Bot-related security incidents rose 88% in 2022 and another 28% in 2023, according to Thales’ Economic Impact of API and Bot Attacks report.⁶ These losses include financial theft, fraud, and the costs incurred by banks to invest in infrastructure, as well as the expenses associated with downtime and legal actions.
• Account Takeover Fraud Rises: ATO fraud, driven heavily by bots, resulted in approximately $15.6 billion in losses in 2024 alone, according to Javelin Research.⁷
• Internet Traffic: Bots now generate more than half of all web traffic, and about one-third of that is from malicious “bad bots,” according to the Thales 2025 Bad Bot Report.⁸ This means that a significant portion of visits to financial platforms aren’t from real customers but from automated scripts attempting to commit fraud, scrape data, or test stolen credentials.
• API in the Bot Crosshairs: Insecure APIs and automated bot abuse are responsible for 11.8% of all cyber events and losses globally, according to the Thales Economic Impact of API and Bot Attacks report.⁹ Financial services, healthcare, and eCommerce are the most targeted industries, primarily because they involve vast amounts of sensitive data.
• Inflated Operational Costs: The impact goes beyond direct theft: Bots inflate infrastructure and bandwidth costs, degrade site performance, and can even trigger expensive downtime. For example, every one-second delay in page load can drop conversion rates by 7%, according to web development firm Tenacity.¹⁰
• Regulatory Fines: In the EU, failing to prevent bot-driven ATOs can be considered GDPR violations. This means companies found to be at fault could face fines of up to 4% of their global annual turnover or €20 million, whichever amount is higher, per GDPR guidelines.¹¹

The bottom line: Whether it’s direct fraud, infrastructure waste, lost revenue from content theft, or regulatory penalties, bots are a significant and growing business risk for financial institutions and any organization operating online. Banks need advanced bot detection to protect their customers, revenues, and reputations. 

Leveraging Digital Trust for Advanced Bot Detection

Bots pose a threat on multiple levels. If you only solve for part of the problem, your organization will be vulnerable in other areas. Banks need a unified approach to bot detection to stay ahead of different threats. 

Feedzai’s Digital Trust platform combines advanced AI, behavioral analytics, and real-time monitoring to enable financial institutions to deliver sophisticated bot detection and fraud prevention. This combination of technologies and solutions provides a 360-degree view of user risk, enabling financial institutions to make informed, real-time decisions.

Here’s how each core capability works to keep your organization and your customers safe from bot attacks:

Behavioral Biometrics

Feedzai uses behavioral biometrics to analyze how users interact with digital platforms, tracking typing speed, mouse movements, gestures, and navigation patterns. This data builds a unique, dynamic profile for each individual.

By comparing ongoing user behavior to this baseline, Feedzai can spot anomalies that indicate bot activity or account takeover attempts, all while operating silently in the background to minimize friction for legitimate users. The solution can also block or escalate authentication for sessions exhibiting signs of manipulation, such as remote access trojans or phishing attempts.

Key benefits

• Continuous, passive authentication that doesn’t disrupt the customer experience.
• Early detection of suspicious activities before real-time payments are processed.
• Adaptive security as user profiles are constantly updated with new behavioral data.¹²

Device and Network Profiling

Feedzai’s platform collects and analyzes data from the user’s device and network environment, including browser settings, device characteristics, IP addresses, and geolocation. This device and network profiling helps identify non-human patterns. This includes the use of automation tools, scripts, or bots to prevent the creation of fake accounts or the launch of attacks. It also links multiple suspicious activities back to the same device or network, uncovering fraud rings and synthetic identity schemes.

Key benefits

• Detection of bots and automation tools at the point of account creation or login.
• Identification of repeated devices and networks used for fraudulent activity.
• Prevention of downstream identity verification costs by stopping bots early.

Real-Time Monitoring 

Every interaction is monitored in real time, with profiles analyzed and updated using hybrid AI to ensure that evolving threats are detected instantly. 

Key benefits

• Authenticate users at every touchpoint without introducing friction.
• Trigger step-up authentication or terminate the session if a threat is detected.
• Integrate seamlessly with Feedzai’s Transaction Monitoring or other SIEM/fraud management systems for a holistic view of risk.¹²

Hunter Analysis Tool

Feedzai’s AI-powered visual analysis tool, Hunter, helps risk analysts uncover larger fraud patterns and bot networks. By visualizing connections between accounts, devices, and transactions, analysts can quickly identify coordinated attacks, money mule networks, and emerging bot threats.¹³ All findings feed back into the machine learning models, improving detection rates and reducing false positives over time.

Key benefits

• Rapid investigation of suspicious activity with intuitive visual tools.
• Detection of fraud rings and bot-driven campaigns.

Feedzai’s tools can be used standalone or integrated with existing fraud management systems, enabling banks to tailor their security to their specific needs. By leveraging these advanced capabilities, Feedzai empowers financial institutions to build digital trust, keeping out bots and fraudsters while ensuring genuine customers enjoy a seamless, secure experience.

At the end of the day, fighting bots is about more than just blocking bad traffic. It’s about building real digital trust with your customers. Bot threats evolve fast, but so too do the tools at our disposal. With the right mix of behavioral intelligence, real-time monitoring, and a relentless focus on the customer experience, banks can stay one step ahead. 

Additional Resources

• Blog: How FIs Can Outsmart Bot Attacks
• Video: New Account Fraud: The Money Mule Challenge
• Solution: Prevent Account Takeover with Behavioral Biometrics for Silent and Continuous Protection