How Netacea Develops Cutting Edge Bot Detection Techniques

Fraud
Attack Types
Bots
Netacea
May 05, 2021
Whitepapers
On every website there are bots making automated requests. Bot traffic is now so prevalent that the internet as we know it today could not function without bots, for better or worse.

Although some bots are beneficial, malicious bots are using a whole host of methods to make a profit at the expense of websites and users. From scraping to scalping, account takeover and creation to carding and credential stuffing, the threats posed by malicious bot traffic are growing more dangerous and harder to defend against.

These attacks are distinct from traditional hacks that exploit technical weaknesses in systems. Bots automate legitimate activity on websites to exploit business logic weaknesses, making conventional security measures ineffective against these threats. To compound the problem, modern bots are sophisticated enough to bypass client-side detection techniques used by many bot defense systems, such as device fingerprinting and user interaction tracking

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Related Resources

Mar 08, 2023
First-Party Fraud: What It Is, and What It Isn’t

The fraud prevention industry is peppered with hundreds of vendors who mainly solve for third-party identity theft fraud. Some vendors branch out into synthetic fraud, including manipulated or fabricated identities, yet very few vendors tackle first-party fraud. First-party fraud is defined as the use of one’s own identity to open an account and use it to commit a dishonest act for personal or financial gain. It remains an elusive problem because there are no consumer victims in chargebacks, disputes, or overdraft fraud. Moreover, when it comes to the granular semantics of first-party fraud, different opinions start to clout the agreed-upon definition, making it difficult to classify, pinpoint, and ultimately combat these dishonest acts. 

Join this session to hear from industry experts about: Where do manipulated identity or rewards gaming abuse fall on the spectrum between first-party and synthetic fraud?  How do these categorizations differ by industry? In what ways do our assumptions around these semantics turn into ineffective proxies for first-party fraud?  How can we differentiate between a consumer’s intent to commit a dishonest act, versus a consumer who was manipulated into a dishonest act, versus a consumer making an honest mistake? 

The key is context. We need to understand a consumer’s act in context of other financial decisions they’ve made across various life stages, across different financial institutions, and across various economic environments. Behavioral anomalies across time and space will serve as better proxies in determining whether a consumer is a true first-party fraudster or whether new socio-economic conditions or happenstance interactions with malicious actors have resulted in a first-party-like occurrence. 

In order to achieve this level of context, a multi-industry data consortium is required. Consumer transactional behavior can then be analyzed across the financial ecosystem, over time, to correlate actions with true first-party fraud and to promote an ecosystem of trust.

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