As fraudulent attacks become more nuanced and disruptive, bot detection has become an ongoing challenge for global business.
Simply put, bot detection refers to identifying and distinguishing real people from non-human users. Not all bots are bad, but when used with the wrong intention, they can cause irrevocable damage. They are often the weapon of choice by fraudsters looking to engage in harmful activities such as account takeover. Malicious bots can also be used to cost businesses big, such as in the case of pay-per-click (PPC) fraud, which can skew marketing and advertising analytics and consume budget spend.
A business's goal for bot detection should be to prevent these malicious bots from engaging in activities like spamming, hacking, and scraping private data, which can ruin their day-to-day operations and eventually end up costing the business (and their customers) a ton of money.
Common methods for bot detection include IP analysis, CAPTCHA, device fingerprinting, artificial intelligence (AI) and machine learning (ML), behavioral biometrics, and more.
The key is to be able to detect and analyze customer behavioral patterns and data to identify actions that are unique to bots.
As an example, bots may be programmed to click on links or fill out forms in a specific way or at a certain speed. These bots may also attempt to access the same page several times in a short period from various IP addresses. If you use the data patterns as indicators, your business can quickly detect suspicious activity before it's too late.
Over the years, it has become increasingly difficult for businesses to detect hacker attacks in real time.
Advancements in technology, driven by AI, natural language processing, and other methods, have added fuel to the fire. These advanced systems evade detection and mimic humans with increasing ease. Scary, right?
The takedown of the Genesis Marketplace earlier this year showed how bots and the criminals operating them have become organized at scale, affecting millions of people worldwide.
The truth is: fraudulent activity is big business. Fraud cost consumers $8.8 billion last year alone and is a growing problem.
A sophisticated bot detection strategy should incorporate bot detection models that are agile and not held back by prescriptive, limited data capture approaches, like tagging. For example, data capture via tagging can potentially only be reduced to minutes with a good system in place. An alternative to tagging is a solution that "captures everything" – one that contextualizes and activates data in milliseconds, leading to increased conversions and more complete data sets.
Additionally, when developing your own bot detection strategy, you should consider how machine learning and AI models can help identify bad bots before they strike. These types of models aid in monitoring behavior and sketchy patterns.
Companies that can detect malicious bots before they have a chance to wreak havoc can be better equipped to offer a seamless customer experience. Data-driven strategies, predicated on complete data sets, add an extra layer of protection to afford organizations greater assurances for their customers from the damaging effects of fraudulent activity. Organizations must continue to overcome increasingly smarter and more sophisticated bots, with strategies and partners that combine continuous monitoring with AI and ML measures and updates.
*This article originally appeared in Newsweek