Payment Buffering Service for Fraud Detection
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Solution Overview
Problem
The increasing use of credit cards online has led to a rise in credit card fraud, with existing security measures being inadequate in detecting and preventing fraudulent activities, resulting in significant financial losses for suppliers.
Innovation Solution
A system and method that utilizes a database to store customer information and employs machine learning algorithms to identify fraudulent activities by analyzing spending patterns, coupled with a buffering service that replaces customer payments with secured payments to shield suppliers from fraudulent risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional security measures such as magnetic stripes or embedded chips are added to credit cards, then fraud detection capability is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary buffering service that sits between the customer and supplier in the payment process. This service receives customer payments, verifies them through multiple authentication factors, and then issues secured payments to suppliers. The intermediary handles the complex fraud detection logic centrally rather than embedding it in individual cards or requiring direct customer-supplier verification.
Solution Approach 2:
The patent replaces physical security measures (magnetic stripes, embedded chips) with a digital authentication system. Instead of relying on physical card features, the system uses multiple authentication factors including biometric data, device identifiers, and behavioral analysis performed by machine learning algorithms to verify payment authenticity.
2Reliability
If spending limits and pre-approval processes are implemented, then fraud prevention is improved, but transaction speed decreases
Solution Approach 1:
The buffering service performs preliminary authentication and verification actions before the actual transaction is completed. Customer payments are verified through multiple authentication factors in advance, and the buffering service pre-issues secured payments to suppliers based on this verification, eliminating the need for slow pre-approval processes for each transaction.
Solution Approach 2:
The system implements continuous feedback loops where machine learning algorithms analyze transaction patterns in real-time, adjusting authentication requirements dynamically. Low-risk transactions proceed quickly with minimal verification, while suspicious transactions trigger additional authentication steps, optimizing both speed and security.
3Measurement precision
If machine learning algorithms are used to analyze spending patterns, then fraud detection accuracy is improved, but computational resources required increase
Solution Approach 1:
The system applies machine learning algorithms selectively rather than to every transaction equally. Low-risk transactions based on established spending patterns require minimal computational analysis, while transactions that deviate from patterns or involve higher amounts trigger more intensive machine learning analysis, optimizing resource usage while maintaining detection accuracy.
Data Source
AI summary
Methods for reducing fraudulent activities associated with credit card and debit card usage can include a better authentication process, such as getting to know the customers, thus can detect the fraudulent activities when the credit payments do not fit the usage patterns of the customers. The method can include presenting the flight search result in a matrix format and a listing format, with the actions of the customers assessed to generate behavior profiles. The behavior profile can be used authenticate the customers. In addition, the method can include replacing a payment from a customer to a supplier with a different payment from a platform to the supplier.