Device Trust Scoring Using Attributes for Cold-Start Fraud Prevention
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Solution Overview
Problem
Conventional fraud detection methods fail to prevent identity theft due to reliance on personally identifiable information and reactive analysis, and reputation scoring systems do not provide predictive intelligence for unknown devices, exacerbating the 'cold start problem.
Innovation Solution
A trust scoring service using unsupervised machine learning to analyze device attributes and generate trust scores, addressing the 'cold start problem by predicting trust levels based on device behavior patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional detection methods use personally identifiable information (PII) or traffic properties for fraud detection, then detection capability is provided, but the methods fail to prevent identity theft because PII can be stolen and traffic properties can be obscured or faked
Solution Approach 1:
The patent introduces device attributes as an intermediary between the device and the fraud detection system. Instead of directly using PII or traffic properties that can be compromised, the system uses device attributes (such as device identifiers, hardware characteristics, and behavioral patterns) as a mediator that is harder to fake or steal, thereby improving both detection capability and identity theft prevention
Solution Approach 2:
The patent replaces conventional detection methods that rely on PII and traffic properties with a machine learning-based trust scoring system. This substitution transforms the mechanical approach of analyzing raw data into an intelligent system that evaluates device attributes and behavioral patterns, enabling proactive fraud prevention rather than reactive detection
2Reliability
If conventional detection methods analyze data after-the-fact, then fraud detection is provided, but loss prevention is not achieved because the methods are reactive and do not prevent fraud losses
Solution Approach 1:
The patent implements preliminary action by calculating trust scores for devices before fraudulent activities occur. The system proactively evaluates device attributes and establishes trust scores in advance, enabling the fraud prevention system to block or flag suspicious activities before they result in losses, rather than reacting after fraud has occurred
Solution Approach 2:
The patent incorporates feedback mechanisms where the trust scoring system continuously monitors device behavior and updates trust scores based on observed patterns. This feedback loop enables the system to adapt to changing fraud patterns and maintain effective prevention strategies, closing the gap between detection and prevention
3Loss of information
If reputation scoring systems use only global anchors such as IP address and email domain name, then reputation scores can be determined for known malicious incidents, but predictive intelligence is not provided for IPs and email domains that have not been reported
Solution Approach 1:
The patent adds another dimension to reputation scoring by incorporating device attributes beyond traditional global anchors like IP address and email domain. By evaluating multiple dimensions of device characteristics and behavioral patterns, the system can generate trust scores for previously unseen devices, providing predictive intelligence without relying solely on historical blacklist data
4Measurement precision
If the focus is on correctly identifying devices to ensure history captures for reputation scoring, then reputation scores based on device behavior history can be determined, but the cold start problem occurs where devices seen for the first time cannot be scored
Solution Approach 1:
The patent applies preliminary action by establishing initial trust scores for new devices based on their device attributes before any behavioral history is available. The system proactively evaluates hardware characteristics, device identifiers, and initial interaction patterns to generate baseline trust scores, enabling the system to handle cold start scenarios and maintain scoring capability for all devices regardless of history
Data Source
AI summary
A fraud prevention system that includes a client server and a fraud prevention server. The fraud prevention server includes an electronic processor and a memory. The memory including a trust scoring service. When executing the trust scoring service, the electronic processor is configured to receive a trust score request of a device from the client server, generate, with a trust model, a trust score of the device, and responsive to generating the trust score, output the trust score to the client server in satisfaction of the trust score request, wherein the trust score is distinct from a risk factor, the trust score representing a predicted trust level of the device, and the risk factor representing a fraud risk level associated with the device based on one or more device behaviors.