Bot or human? In fraud, distinguishing between the two is essential.
Today, bots account for nearly half of all internet traffic. On top of this, technologies such as AI and machine learning are enabling bots to exhibit more sophisticated behaviors and capabilities — making them better at mimicking human behavior. Last year alone, nearly 30.2% of all internet traffic was attributed to bad bots like web scrapers, click fraud bots, and account hackers.
For companies fighting fraud, differentiating between bots and humans is a major challenge. The increase in bot activity and bot sophistication makes it difficult for existing fraud detection solutions to not only stay ahead of evolving threats but to actively spot the differences between human and bot behavior in real time.
Detecting and defeating evolving bots is possible, but it requires the right combination of tools and technology. Let’s dig into the behind-the-scenes components that work together to power an effective bot detection solution.
What's a bot?
A bot is simply a bit of software that’s programmed to perform a series of automated tasks online. Tasks can range from opening a webpage to clicking a link or filling in a form.
Fraudsters use bots and bot networks (botnets) in a variety of ways to carry out deceptive activities. Credential stuffing bots use stolen passwords to take over accounts, spambots gather email addresses to send unwanted mail, and DDoS botnets flood websites with unwanted traffic to disrupt normal operations and communications. Bots can even fill out entire forms and applications online.
What's bot detection in fraud?
Bot detection is the process of identifying and distinguishing between bot and human activity. In fraud, it’s a critical component to protecting organizations, consumers, and data from malicious attacks.
When it comes to fighting digital fraud, an effective bot detection solution leverages a combination of powerful tools, techniques, and technologies to differentiate between bots and real human behavior. The right solution captures and analyzes data to paint a clear picture of how typical consumers behave online.
How effective bot detection works
As mentioned, effective bot detection relies on distinguishing bot behavior from human behavior. This requires a thorough understanding of typical (human) consumer behavior, and to get that companies need better data to build more complete typical consumer profiles.
A consumer profile is a collection of information — preferences, behaviors, and interactions — that’s built on data from various sources like website activity, purchases, app usage, etc. To build complete “typical” consumer profiles, companies must capture lots of data — behavioral insight, contextual data, behavioral biometrics, historical data, etc. — across all touchpoints and channels. Once all the data is collected, it can be unified to create a detailed reference profile of how typical consumers behave online.
Consumer behavior is then used to compare digital activity in real time, making it easier to detect and flag unusual behavior, such as bot activity.
Components of an effective bot detection solution
To detect bots effectively, organizations need a robust fraud defense solution that combines several elements: identity verification, behavioral biometrics and analytics, and machine learning.
Digital identity verification
Digital identity verification is what it sounds like: the process of verifying a person’s identity digitally. In bot detection, it helps validate whether online activities (like opening an account or making a transaction) are made by real consumers.
Digital identity verification leverages:
- Behavioral biometrics to analyze consumer behavior patterns across devices and platforms. From typing speed and touch gestures to mouse movements and scrolling tendencies, behavioral biometrics help establish digital “body language” that's unique to individuals. Behavioral biometrics provide an additional layer of security by capturing and establishing baseline consumer behaviors in real time. This allows for continuous monitoring of an individual’s behavioral patterns to authenticate their identity and quickly detect suspicious activities. Real-time data capture also facilitates prompt response and mitigation decisions whenever anomalies, like bots, are detected.
- Behavioral analytics to reveal patterns in how consumers behave across devices, domains, and platforms. In fraud detection, deviations from typical consumer behavior are used to identify anomalies and questionable activities.
- Identity resolution to verify and connect consumer data points across all channels, devices, and interactions to create a holistic view of individual consumers. The connected data is used to compare legitimate and deceptive behaviors to spot bots and flag fraudulent activity.
Machine learning
Bots are continually evolving, so an effective bot detection solution must scale accordingly.
For bot detection to evolve, digital identity verification and biometrics should feed a machine learning model that learns and adapts as bot technology advances, enabling instant recognition of new bot behavior without having to build new model. Bot detection should leverage machine learning to continually enhance understanding of human vs. bot behavior, which reduces false positives and enriches fraud prevention.
Reveal the human to unmask the bot
Identifying bot behavior is critical to fighting evolving fraud. An effective bot detection solution uses better data and the right technologies — digital identity verification, behavioral biometrics, and machine learning — to paint a clearer picture of legitimate consumer behavior. When human behavior is understood, bot detection can work its magic.
