Why Device Intelligence is Critical for Fraud Fighters

When detectives work to solve a crime, they always need the same critical context to find the culprit—where the crime took place and what was used to commit it. Fraud investigators are no different. They need to know specifically what kind of device was linked to the fraud to have a complete picture of how the attack happened.

While detectives don’t have a single piece of software that can reveal that context for them, fraud fighters fortunately do—device intelligence. To understand why device intelligence is so critical for fraud fighters, we need to understand how it came about, what it does, and how it fits into a comprehensive fraud prevention system.

Fraud prevention before device intelligence

Since 1994 when Stanford Federal Credit Union became the first financial institution (FI) to offer online banking to all of its customers, online bank fraud has been a threat to all FIs. Before the advent of sophisticated device intelligence technologies, fraud fighters relied on traditional methods to identify and combat fraud attacks. These methods often involved analyzing transactional data and user behavior patterns at a large scale and then implementing rules-based systems to flag suspicious activities.

While these approaches were effective, they had significant limitations. Fraudsters became adept at testing to find the limits of the rules and exploiting gaps. The non-adaptive methods FIs relied on struggled to keep pace with fraudsters’ rapidly changing tactics and improving technology. Worse yet, these approaches often resulted in high false positive rates, leading to friction in legitimate transactions and customer dissatisfaction.

Another critical shift in customer behavior came with the rise of mobile devices. Online banking became app-based banking. Customers more frequently accessed their accounts from laptops and cell phones. Without insight into the specific devices used during online transactions, rules-based fraud prevention efforts left fraud teams navigating through murky waters blindly. Fraudsters became invisible in these gaps, appearing as good customers while running malicious apps and fraud tech in the background. The need for a more nuanced understanding of devices accessing digital banking platforms became obvious. Fraud teams had to develop a way to effectively distinguish between genuine users and fraudsters by seeing the whole picture.

Enter device intelligence – a modern tool ready to fight fraud in the modern age. Device intelligence empowers fraud fighters with a deeper understanding of the devices interacting with their systems, enabling them to make more informed decisions and accurately assess the risk associated with each transaction. By analyzing a myriad of device attributes such as device type, operating system, IP address, geolocation, and behavioral patterns, organizations can create comprehensive profiles of devices and their associated risk levels.

This nuanced understanding of device behavior allows organizations to implement more robust fraud prevention strategies and thwart fraudsters while minimizing false positives. From detecting account takeover attempts to identifying instances of synthetic identity fraud, device intelligence plays a pivotal role in fortifying defenses against an array of fraud schemes.

How does AI-powered device intelligence work?

Every online interaction leaves a trail of data. Through artificial intelligence (AI), FIs can see a complete picture of customer behavior in real time. AI-powered device intelligence leverages both advanced algorithms and machine learning to map meaningful patterns and correlations amongst vast datasets.

At the heart of AI-powered device intelligence is a tool called device fingerprinting. A device fingerprint is a unique identifier of key attributes about a device, from its hardware specifications to software configuration to browsing history and behavioral patterns. Many companies use device fingerprinting for identity validation and digital advertising. When it comes to fraud prevention, the fingerprinting process allows FIs to track individuals as they browse websites and interact with mobile applications.

Device intelligence can collect these data points via a web browser or mobile app. It captures things like IP address, OS version, screen resolution, system fonts, and HTTP cookies for browser-based users and device ID, GPS or location, and Wi-Fi networks in use for mobile app users. These points give a good outline of who the user is and also create a fingerprint so the FI can track that user across other accounts they may have and spot suspicious interactions or activity they may engage in, even if the account appears to be a user in good standing.

What fraud attacks does device intelligence prevent?

Device intelligence helps reveal fraudsters hiding behind sophisticated synthetic IDs and detects fraud tools like botnets, emulators, and hijacked devices. So, naturally, there are many types of fraud attacks that device intelligence plays a key role in preventing. Here are a few significant fraud attack types and how device intelligence thwarts each.

Account Creation Fraud

One of the biggest attack points for fraudsters is account opening. Fraudsters use methods like stolen identities or fabricating synthetic IDs by piecing together bits of real information to form a Frankenstein account. Once they’ve been approved, they can lie dormant for months or even years before busting out and committing a string of frauds before they disappear.

Device intelligence helps organizations mitigate account creation fraud by analyzing the device attributes and behavioral patterns associated with each registration attempt. Suspicious characteristics like high velocity of account creation, use of disposable email addresses, or inconsistencies in device attributes can serve as indicators of fraudulent activity, allowing organizations to block or flag suspicious registrations in real time. Other more detailed signals like the detection of emulators or bots working in the background help reveal that even a seemingly valuable customer on paper could be a fraudster lurking.

Account Takeover (ATO) Fraud

Account takeover frauds rely on fraudsters stealing credentials or exploiting security loopholes, which they can do by leveraging breached data to brute force takeover accounts. With device intelligence, organizations can detect suspicious login attempts by analyzing the device attributes associated with each login session. That includes gathering data in real time to reveal anomalies like login attempts from unrecognized devices or unusual geolocations, or the presence of malicious tools like malware, modified code, or other device manipulations. Real-time device intelligence also collects extensive profiling data like operating system details, network data, timestamps, languages, and user agents. Any deviations in these areas can trigger a fraud alert immediately upon detection. Comparing a device’s unique device ID to the one being used to access an account can also reveal a fraudster puppeteering a good user’s account.

Payment Fraud

Payment frauds like credit card fraud, ACH fraud, Zelle fraud, and first-party fraud can all be revealed through device intelligence that offers data security and monitoring at every touch point in the customer journey. For example, discrepancies between the location of the device and the billing address associated with the payment card can raise red flags, prompting additional scrutiny to prevent fraudulent transactions. Advanced ML algorithms sift through massive datasets to find even slight anomalies in customer behavior that signal possible payment fraud, and device intelligence solutions like DataVisor’s dEdge can flag or block those suspicious activities immediately before they settle.

Device manipulation detection and real-time event tracking also play a role in thwarting payment fraud, along with unique device ID detection and accurate fraud scoring that reveals when fraudsters have tried to reset a device or manipulate it to avoid detection.

Application Fraud

Fraudsters often manipulate or repackage applications to conduct fraudulent activities. Leading device intelligence solutions detect these manipulations by analyzing the integrity and environment of the application, ensuring the use of legitimate and untampered apps only. Because fraudsters rely on malware and other nefarious tech behind the scenes, device intelligence is critical for fraud fighters to see what they’re actually up against and for the FI to see who is actually applying for a new card, account, or loan with them.

Malware and Bot Attacks

Malware and bot attacks are a key part of fraudsters’ scams, but device intelligence solutions like dEdge identifiy and block devices infected with malware or controlled by bots. They can detect abnormal device behavior, and see signals from the device itself that alert the tool to the presence of malware. In addition, device intelligence spots high volumes of automated requests and other signs that indicate the presence of malicious software or botnet activity​.

Credential Stuffing

Because credential stuffing attacks involve using automated scripts to test stolen username and password combinations on multiple websites, device intelligence is an ideal foil for these attacks. It detects and blocks them by identifying unusual login patterns and high volumes of login attempts from a single device or IP address. Because device intelligence is part of a holistic solution, it works with the rest of the fraud prevention platform to capture a full picture of good customer behavior, then weeds out credential stuffing at account signup that deviates from that baseline behavior.

Phishing and Social Engineering Attacks

By monitoring real-time device and user behavior, device intelligence can identify signs of phishing and social engineering attacks. It detects when users are redirected to fraudulent websites or when unusual actions are taken that suggest manipulation by a third party​

Key benefits of device intelligence in fraud prevention

Through analyzing an array of device attributes and behavioral patterns, device intelligence provides organizations with a nuanced understanding of the risk associated with each transaction or user interaction. This enables more accurate risk assessment, allowing organizations to differentiate between legitimate users and potential fraudsters with greater precision. That greater precision helps reduce false positives that traditional fraud prevention methods often fall victim to, leading to unnecessary friction in legitimate transactions and eroding customer trust.

The ability to analyze incoming data in real time enables organizations to leverage device intelligence in detecting and immediately responding to fraudulent activities as they occur. This proactive approach to fraud detection allows organizations to thwart fraud attempts before they escalate, minimizing financial losses and reputational damage.

Device intelligence is also highly adaptive, and through its advanced AI capabilities can keep pace with fraudsters’ constantly evolving tactics. Market-leading solutions like DataVisor’s dEdge stand out even further in this regard, thanks to its scalability and flexibility to handle high volumes of data and the growing needs of organizations. It supports both on-premise and cloud environments, offering flexibility in deployment and integration with existing systems. Another key benefit dEdge provides is a reduced load on central servers, lowering cloud computing costs and infrastructure investments. This efficiency makes it a cost-effective solution for organizations looking to enhance their fraud prevention capabilities.

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Incorporating device intelligence into fraud prevention strategies, especially tools like dEdge that integrate seamlessly with other fraud prevention tools and systems, yields tangible benefits across various dimensions. Choosing the right solution comes down to your FI’s unique needs and the benefits you prioritize most.

Choosing a device intelligence solution

Here are some key factors to consider when choosing a device intelligence solution:

1. Accuracy and precision: Look for a device intelligence solution that offers high accuracy and precision in identifying fraudulent activities while minimizing false positives. Solutions that leverage advanced AI algorithms and machine learning techniques are better equipped to analyze complex patterns and discern subtle fraud indicators with greater accuracy.
2. Real-time capabilities: Opt for a device intelligence solution that can analyze incoming data in real time to detect and respond to fraudulent activities as they occur. Real-time fraud detection enables organizations to mitigate fraud attempts swiftly, reducing financial losses and minimizing reputational damage.
3. Scalability and flexibility: Choose a device intelligence solution that is scalable and flexible enough to accommodate the evolving needs of your organization. Whether you’re a small fintech or a large FI, ensure that the solution can scale seamlessly to handle growing volumes of data and adapt to changing fraud patterns and attack vectors.
4. Integration capabilities: Assess the integration capabilities of the device intelligence solution with your existing fraud prevention infrastructure and third-party systems. Look for solutions that offer seamless integration with popular fraud management platforms, payment gateways, and identity verification services to streamline implementation and maximize efficiency.
5. Compliance and security: Verify that the device intelligence solution adheres to industry standards and regulatory requirements for data privacy and security. Ensure that the solution employs robust encryption protocols and follows best practices for data protection to safeguard sensitive information and maintain compliance with applicable regulations.
6. Customer support and Service Level Agreements (SLAs): Evaluate the level of customer support and service offered by the device intelligence solution provider. Choose a vendor that provides responsive customer support, comprehensive documentation, and service level agreements (SLAs) to ensure timely resolution of issues and ongoing support for your fraud prevention initiatives.
7. Cost-effectiveness: Consider the cost-effectiveness of the device intelligence solution in relation to the value it delivers to your organization. Compare pricing models, licensing fees, and total cost of ownership (TCO) across different solutions to determine the most cost-effective option that aligns with your budget and business objectives.

When it comes to choosing a best-in-class device intelligence solution, DataVisor’s dEdge stands out as a leading choice for organizations seeking comprehensive end-to-end fraud prevention capabilities. dEdge leverages advanced AI algorithms and machine learning techniques to analyze device attributes and behavioral patterns in real time, enabling organizations to detect and mitigate fraudulent activities with unparalleled accuracy and efficiency. With its scalable and flexible architecture, dEdge seamlessly integrates with existing fraud prevention infrastructure, empowering organizations to stay ahead of evolving fraud schemes while maintaining compliance with industry regulations. Backed by DataVisor’s expertise in fraud detection and prevention, dEdge offers robust customer support and service, ensuring that organizations receive the assistance they need to maximize the value of their fraud prevention investments. To see how DataVisor’s dEdge can elevate your fraud prevention capabilities and safeguard your digital assets against emerging threats, book a customized demo with one of our experts.