Device Intelligence Enhances Fraud Detection in Digital Banking
/ 4 min read
Quick take - Device intelligence is increasingly vital in digital banking for real-time fraud detection, utilizing advanced techniques like device fingerprinting and call intelligence to analyze risks associated with evolving fraud tactics, including mobile malware and remote access tools.
Fast Facts
- Device intelligence is crucial for digital banking, addressing rising fraud risks like scams and device takeover fraud through real-time analysis of device attributes.
- Device fingerprinting creates unique identifiers for devices, combining static and dynamic data points (e.g., IP intelligence, installed applications) for improved identification accuracy.
- Location intelligence and call intelligence are key components in fraud detection, with advanced tools analyzing location familiarity and call characteristics to identify potential fraud.
- The increase in mobile malware targeting financial applications complicates fraud detection, necessitating next-gen tools that utilize proactive threat intelligence and heuristic rules.
- Continuous updates to device intelligence tools are essential for adapting to evolving fraud tactics, with risk signals integrated into transaction monitoring for real-time risk assessment.
The Importance of Device Intelligence in Digital Banking
Device intelligence is becoming increasingly important in the realm of digital banking, especially as fraud risks such as scams, social engineering, and device takeover fraud continue to rise. This technology involves the real-time analysis of device attributes to assess the likelihood of fraudulent activities during banking sessions.
Evolving Fraud Techniques and Device Fingerprinting
As fraud techniques evolve, there is a growing need to move beyond merely identifying devices to thoroughly analyzing their associated risks. At the heart of device intelligence is device fingerprinting, which creates unique identifiers for devices based on various attributes and configurations. Traditional device fingerprinting tools often rely on static identifiers. However, the most effective tools integrate both static and dynamic data points to enhance identification accuracy. Dynamic data points can include a range of information, such as IP intelligence, installed applications, and paired devices.
Location intelligence assesses risk by analyzing factors like location familiarity, plausibility, and reputation. Advanced tools offer feature toggles that allow for tailored location data collection and privacy strategies.
The Role of Call Intelligence and Mobile Malware
In addition to device attributes, call intelligence has emerged as a significant factor in fraud detection. Genuine digital banking transactions rarely occur during live calls, making the detection of such calls a strong indicator of potential fraud. Next-generation fraud detection tools can now analyze additional data points, including call type and duration, to improve their fraud detection capabilities.
The threat landscape is further complicated by a notable increase in mobile malware targeting financial applications, with a reported 94% rise in mobile malware families over the past three years. Modern mobile malware can execute device takeovers, complicating fraud detection efforts. Traditional malware detection tools that rely on hash-based methods are often ineffective, as fraudsters can easily circumvent these measures. In contrast, next-gen tools employ proactive threat intelligence and heuristic rules, leading to more effective malware detection.
Continuous Updates and Risk Assessment
Remote Access Tools (RATs) have become a common weapon for fraudsters, used for bank impersonation and tech support scams. The most advanced device intelligence tools can detect the active use of RATs during banking sessions. However, high false positive rates can arise if these tools fail to distinguish between active and dormant RATs.
Device security posture assessments can identify various risk signals, such as jailbroken devices, rooted devices, and emulators. To combat the ever-evolving fraud tactics, continuous updates to device intelligence tools are essential. Companies like ThreatFabric have established dedicated Mobile Threat Intelligence teams focused on researching and developing effective detections for emerging fraud tools.
Risk signals generated from device intelligence tools are integrated into transaction monitoring solutions, enabling real-time risk assessment. These tools facilitate continuous risk profiling throughout the customer journey rather than relying on static profiling. Financial institutions leverage risk signals in machine learning models and rulesets for payment risk scoring.
Device intelligence is applied across various fraud detection use cases, including Account Takeover (ATO), vishing scams, Device Takeover (DTO), and mobile malware fraud. As digitalization increases, so do opportunities for fraud. Device intelligence can provide critical signals necessary for detecting and preventing fraudulent activities, enhancing the security of digital banking experiences.
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