There’s an invisible threat lurking in your systems

Engineering and product leaders face many challenges in keeping their software secure and accessible to trusted users. As they grapple with the complexities of managing in-house or legacy systems, they often overlook a silent adversary: fraud and account abuse. On top of that, fraudsters are becoming increasingly sophisticated, exploiting weaknesses and utilizing advanced techniques to mask their activities, leading to significant losses, damage to reputation, and a drastic impact on key performance indicators (KPIs).

While it’s relatively straightforward to measure the financial impact of fraud, it’s equally important to consider how abusive activities can affect other KPIs. For instance, a high incidence of fraud could lead to an increase in customer complaints, negatively impacting customer satisfaction scores. Conversely, effective fraud prevention measures can lead to positive KPIs such as increased trust and loyalty among users, ultimately driving higher user retention rates.

Common challenges with solving fraud and account abuse

The domino effect of inaccurate data

Two significant issues arise from inaccuracies in current fraud prevention processes and the data it collects: the failure to identify repeat fraudsters and the inability to differentiate automated threats from humans.

For instance, consider a scenario where a fraudster makes multiple purchases using different stolen credit card information. Without accurate fraud prevention, the system fails to recognize that these transactions originate from the same device. As a result, each fraudulent activity is treated as an isolated incident, allowing the fraudster to continue their fraudulent activities undetected.

Similarly, it becomes challenging to differentiate between automated bots and genuine human users without precise device identification, leading to false positives, where legitimate transactions are flagged as fraudulent, negatively impacting customer experience and potentially leading to loss of business.

The ever-changing faces of cyberthreats

Cybercriminals are simultaneously upping their game, crafting more complex and covert attacks as we develop more sophisticated defense mechanisms. A particularly alarming trend is the rise of sophisticated human threats — cyberattacks meticulously planned and executed by skilled individuals or groups.

Consider the SolarWinds hack, where attackers infiltrated the network management system used by numerous U.S. government agencies and Fortune 500 companies. The attack was stealthy, well-coordinated, and incredibly hard to detect, demonstrating the sophistication human threats can reach.

Navigating the botnet storm

Unlike human visitors, fraudsters program automated threats to mimic human-like device characteristics and behavior, making them incredibly difficult to detect. They masquerade as genuine users, often bypassing security measures and causing significant damage. It’s also becoming increasingly common, with 15% of Cloudflare customers reporting being a target of an HTTP DDoS attack accompanied by a threat or ransom note in 2022.

Automated threats can wreak havoc on websites, leading to distributed denial-of-service (DDoS) attacks, brute-force attacks, and credential stuffing. For instance, the infamous Mirai botnet attack in 2016 utilized a network of infected IoT devices to launch a massive DDoS attack, disrupting major websites worldwide.

Shedding light on the invisible threat with device intelligence

How should product and engineering teams detect these threats and ultimately prevent them from infiltrating technical operations? One highly effective way is to shed light on each visitor or user that visits a website or software application through an accurate and persistent visitor ID assigned through device identification. 

Most internet traffic is anonymous, or at least attempts to remain anonymous. That poses issues when it comes to secure authentication and visitor identification. You can use device intelligence to identify every user, even bad actors trying to shield their true identity on some level. 

Device intelligence is a method of identifying users by using device fingerprinting as the foundational method and adding additional techniques such as VPN detection, incognito mode detection, IP geolocation, and more. Device intelligence enables those looking to solve fraud prevention and detection challenges in their current technical environments. This method creates a unique visitor ID for every user regardless of browser, location, or device type, and the visitor ID remains stable over months to ensure a highly accurate visitor ID history. With an informed visitor ID database, you can challenge untrusted traffic while personalizing the experience for trusted visitors.

How does device intelligence work?

It starts with the collection of all available raw device information, signals, and attributes. From there, the information is processed to produce a unique device identifier. The device identifier should remain stable over months and account for all visits of the specific user regardless if some of the device signals or attributes changes such as browser type or IP address. 

Let’s dive into a few of the specific signals that are a part of a robust device intelligence solution like Fingerprint. We’ll organize these into three categories: user behavior signals, network signals, and bot detection signals.

User behavior signals

• Incognito mode detection: Detects if a user is trying to hide their identity or history.
• Browser tamper detection: Identifies browser spoofing incidents and suspicious browser signature anomalies.
• Android tamper detection: Ensures a safe Android mobile application environment by detecting rooted devices.

Network signals

• IP geolocation: Accurately determines the location of the actual IP address.
• VPN detection: Determines if a user is leveraging a VPN to conceal their identity by validating that their IP time zone matches their browser time zone.
• IP blocklist matching: Sees when an IP address matches a known database of malicious actors and spammers.

Bot detection signals

• Browser bot detection: Identifies automated tools, friendly search bots, and other sophisticated threats in real time.
• Android emulator detection: Prevents spam and protects against nefarious Android emulator farms by ensuring the request is coming from a physical device.

The case for accurate device intelligence

Fingerprint vs. Competitors

Common Use Cases

• Payment Fraud: Stop payment fraud in all its forms with accurate user identification. Use Fingerprint to keep chargebacks to a minimum, protecting your bottom line and seller reputation.
• Paywall: Fingerprint helps media companies, subscription services, and content creators monetize more effectively with accurate visitor identification to eliminate leaky paywall issues.
• Personalization: Accurately identify users and personalize websites by providing specific promotions or content based on user profile. Streamline user experience by reducing the need for 2FA/MFA.
• Account Takeover: Powerful account takeover protection for any web application. Keep your customers' accounts safe by accurately identifying threats before they cause damage with Fingerprint’s 99.5% accurate visitor identification.
• Sign-up Fraud: Reduce revenue loss by stopping users from creating multiple accounts or trial accounts which impact your revenue. Fingerprint also helps prevent users from creating multiple or false reviews.
• Attribution: Identify users and assign credit for conversions to different ads, clicks, and factors along a user's path to completing a conversion.

Revolutionizing fraud prevention: 
unleashing the power of device intelligence

Key benefits

• Improve fraud capabilities: Feed your fraud and risk engines with premium data. Leave the “junk in, junk out” scenario behind.
• Streamline trusted visitors:: A reduction in fraud means an easier experience for trusted visitors and recurring customers.
• Reduce fraud costs:: Detect and prevent instances of fraud costing your business and its customers money. 

Key features

• Identify visitors intent on carrying out fraud: Identify key characteristics about a visitor, like a mismatched time zone, making bad actors easier to identify and manage. 
• Increase strength of detection: Surface more data which indicates a bad actor might be trying to obfuscate their IP address or tampered with their device or browser settings.
• Incorporate more data into existing fraud and risk engines: Add to the signals you’re already collecting and fortify your decision engine to make detecting possible instances of fraud easier and more accurate.

Set up time to talk to our team and learn more about device intelligence can solve your fraud challenges.