ATM Skimmer Detection via Mobile Crowdsourcing and Incentives
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Solution Overview
Problem
Current systems lack effective methods for detecting skimmers and fraudulent activities at automated teller machines (ATMs) and other transacting devices, leading to potential financial losses and security breaches during card-based transactions.
Innovation Solution
A computer-implemented method that generates a risk database for ATMs, sends push notifications to nearby mobile devices, and executes a software application to gather feedback on the ATMs' condition, providing incentives for users to inspect and report on the devices, thereby enhancing fraud detection and prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fraud detection systems are used, then system complexity is reduced, but detection precision and reliability of fraudulent activities are insufficient
Solution Approach 1:
The patent introduces mobile devices as intermediary tools that enable users to capture images and provide feedback about ATMs. These mobile devices act as mediators between the detection system and the ATMs, allowing remote inspection without requiring complex physical inspection equipment at each ATM location. The mobile device captures images and transmits them to the server, which then analyzes them for fraudulent devices.
Solution Approach 2:
The system enables self-service fraud detection by allowing ATM users to independently inspect and report suspicious activities at ATMs using their own mobile devices. Users are empowered to take images, provide feedback, and contribute to the detection process without requiring specialized equipment or expert intervention. This distributes the detection capability across many users rather than requiring a centralized complex inspection system.
2Productivity
If manual inspection methods are used, then device complexity is reduced, but productivity and coverage of fraud detection are insufficient
Solution Approach 1:
The patent makes the mobile device universal by utilizing its multiple existing functions (camera, GPS, communication capabilities) for fraud detection purposes. The same mobile device that users employ for general communication and information access is also used for capturing ATM images, providing feedback, and participating in the detection network. This eliminates the need for dedicated specialized inspection equipment while achieving high detection productivity.
Solution Approach 2:
The system implements continuous feedback loops where users provide real-time feedback about ATM conditions, which is processed by the server to update risk levels and alert other users. The feedback mechanism enables rapid propagation of detection information across the network, allowing the system to respond quickly to newly detected fraudulent activities and improve overall detection productivity through collective learning.
3Measurement precision
If comprehensive ATM monitoring is implemented, then detection precision is improved, but loss of time for user transactions is increased
Solution Approach 1:
The system performs preliminary fraud detection actions by analyzing images and feedback before users complete transactions at ATMs. The server processes images and determines the presence of fraudulent devices in advance, allowing users to be alerted before approaching or using suspicious ATMs. This preliminary detection prevents users from wasting time at compromised locations while maintaining high detection precision through thorough image analysis.
Solution Approach 2:
The system applies partial monitoring by focusing detection efforts on high-risk ATMs identified through risk level assessments rather than uniformly monitoring all ATMs. The server selectively requests images and analyzes feedback for ATMs with elevated risk levels, achieving effective detection precision for critical targets while minimizing the time and resources required for comprehensive monitoring of the entire ATM network.
Data Source
AI summary
Systems and methods of detecting fraudulent activity including skimmers adapted to compromise transacting devices such as automated teller machines (ATMs) are disclosed. In one embodiment, an exemplary computer-implemented method may comprise determining that a subject device has a risk level higher than a risk threshold, providing a push notification to a mobile device proximal to the subject device, executing a software application executed by the mobile device for gathering information and transmitting feedback regarding the subject device, and providing an incentive, upon receipt of the feedback, to, for example, an account or device associated with an individual involved with the feedback or interaction with the device.