Consortium ML Fraud Detection for Self-Service Kiosks
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Solution Overview
Problem
Existing systems for maintaining and updating self-service kiosks, such as ATMs, are inefficient and costly, particularly when it comes to implementing real-time fraud detection, which is often slow and requires human intervention.
Innovation Solution
A continuous learning model using machine learning that leverages data from a consortium of financial institutions to provide real-time fraud detection parameters, analyzing transaction details to identify potential fraud and updating in real-time based on fraud determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rules-based fraud detection unique to each financial institution is used, then fraud detection can be implemented, but the process is slow and requires human intervention for rule approval, testing, and deployment
Solution Approach 1:
The system enables self-service fraud detection through automated machine learning models that continuously learn from transaction data without requiring manual rule creation or deployment. The model automatically processes transactions, identifies fraud patterns, and updates its detection parameters in real-time, eliminating the need for human administrators to approve and deploy rules.
Solution Approach 2:
The patent replaces the mechanical manual process of rule creation, testing, and deployment with an automated machine learning system. The ML model automatically analyzes transaction data, identifies fraud patterns, and updates detection parameters without human intervention, substituting the manual mechanical process with an automated intelligent system.
2Reliability
If conventional rules-based fraud detection is used, then fraud can be detected, but the system is slow to update and cannot respond in real-time to newly identified fraud
Solution Approach 1:
The machine learning model operates continuously, processing transactions in real-time and continuously learning from new data. The model continuously updates its fraud detection parameters based on emerging patterns, ensuring it responds immediately to newly identified fraud without the delays associated with manual rule updates.
Solution Approach 2:
The system incorporates feedback mechanisms where transaction outcomes and fraud determinations are fed back into the machine learning model. This feedback loop enables the model to learn from actual fraud patterns and continuously refine its detection parameters, allowing real-time adaptation to new fraud techniques.
3Reliability
If human intervention is required for rule approval and deployment, then rules can be carefully controlled, but the process becomes costly and inefficient
Solution Approach 1:
The machine learning model performs self-service by automatically learning from transaction data and updating its own detection parameters without requiring human approval or deployment. This self-service capability maintains reliable fraud detection while eliminating the costly and inefficient manual processes of rule creation and deployment.
4Adaptability or versatility
If individual financial institution-specific fraud detection is used, then each institution can tailor detection to its needs, but scalability and shared learning across institutions is limited
Solution Approach 1:
The machine learning model is designed with universality, serving multiple financial institutions within the consortium through a shared platform. The model can be customized to each institution's specific needs while maintaining a unified architecture that enables scalability and shared learning across all institutions, eliminating the need for separate detection systems for each institution.
Data Source
AI summary
Arrangements for universal self-service kiosk fraud detection are provided. A request for a transaction may be received via a self-service kiosk. The financial institution associated with the transaction may be identified as part of a consortium of financial institutions. Accordingly, a machine learning model particular to the consortium may be used to analyze the transaction. The model may output a determination of whether fraud or potential fraud exists in the transaction. In some examples, the machine learning model may be trained using historical transaction data from one or more financial institutions that are part of the consortium. If fraud is detected, one or more security actions associated with the transaction may be identified and executed. Further, one or more additional self-service kiosks that may be impacted by the fraud may be identified and security actions may be identified and transmitted to the one or more additional self-service kiosks for execution.