ML Trust Score for New Device Recognition and Fraud Prevention
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Solution Overview
Problem
The challenge lies in efficiently recognizing new devices and transferring user settings while preventing fraud, as existing methods are cumbersome and prone to technical issues during device updates, particularly in organizational settings where unauthorized use can occur.
Innovation Solution
A system utilizing machine learning models to generate a trust score for user-device pairs, determining whether to conduct fraud prevention actions or automatically enable settings based on the trust score, thereby streamlining the transition to new devices and enhancing security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual device recognition and settings transfer methods are used, then user control and security verification are maintained, but the setup process becomes cumbersome and time-consuming
Solution Approach 1:
The system performs preliminary actions by automatically detecting new devices and pre-evaluating them against fraud criteria before the user completes setup. The machine learning model generates trust scores in advance, and approved settings are prepared for transfer, reducing the actual setup time and effort required by the user.
Solution Approach 2:
The system enables self-service by allowing automatic device recognition and settings transfer without requiring manual user intervention for each step. The fraud prevention system operates autonomously by evaluating device trustworthiness and automatically enabling or blocking settings based on generated trust scores, freeing users from cumbersome manual processes.
2Productivity
If automatic settings transfer is enabled for all devices, then setup efficiency is improved, but fraud risks increase due to potential unauthorized use
Solution Approach 1:
The system applies local quality by treating each device individually with customized trust score evaluations rather than applying a uniform approach to all devices. The machine learning model assesses specific device characteristics, user behavior patterns, and contextual factors to generate differentiated trust scores, allowing automatic settings transfer only for devices that meet specific trust thresholds while maintaining fraud prevention for others.
Solution Approach 2:
The system utilizes parameter changes by dynamically adjusting the trust score threshold and evaluation criteria based on device risk profiles. The machine learning model continuously refines trust score parameters by analyzing new data patterns, allowing the system to adaptively balance productivity and reliability by changing the stringency of fraud prevention measures according to assessed device trustworthiness.
3Reliability
If traditional fraud prevention methods are used, then security is maintained, but the user experience deteriorates due to additional verification steps
Solution Approach 1:
The system applies partial action by implementing fraud prevention measures selectively rather than universally. The machine learning model generates trust scores to determine the appropriate level of fraud prevention action for each device, applying only the necessary verification steps based on assessed risk levels. This allows the system to maintain security for high-risk devices while providing a streamlined experience for trusted devices, avoiding excessive verification steps for low-risk cases.
4Reliability
If device recognition requires manual verification, then fraud prevention is enhanced, but setup complexity and time requirements increase
Solution Approach 1:
The system replaces mechanical verification methods with automated machine learning-based trust score generation. Instead of requiring manual user verification steps, the system uses algorithms to automatically evaluate device trustworthiness, analyze behavior patterns, and make fraud prevention decisions. This substitution maintains high fraud detection accuracy while eliminating the complexity and time requirements of manual verification processes.
Data Source
AI summary
Disclosed embodiments may include a system for recognizing new devices. The system may receive data indicative of a user being associated with a new device, and may receive, via the new device, a request to perform an action. The system may generate, via a machine learning model (MLM), a trust score associated with the user and the new device. The system may determine whether the trust score exceeds a predetermined threshold. Responsive to determining the trust score does not exceed the predetermined threshold, the system may conduct fraud prevention action(s) with respect to the user and the new device. Responsive to determining the trust score exceeds the predetermined threshold, the system may cause the new device to display a notification. Responsive to receiving a response to the notification, the system may identify user setting(s) associated with a previous device, and enable the user setting(s) via the new device.