Confirmed User Flag for Network Transaction Fraud Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The prevalence of network-based platform interactions has led to an increase in first-party fraud, where users dispute transactions even though they have received the products, making it challenging for credit reporting to accurately identify specific instances of fraudulent activity.
Innovation Solution
The system and method involve appending a confirmed user (CU) flag to authorization requests based on verified network details associated with transactions, which includes delivery data elements and device identifiers. These network details are stored and verified against prior transactions, allowing for the determination of a CU flag that can be used by issuers and downstream systems to make informed decisions about transaction approval and chargeback handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If network-based platform interactions are used to enable remote transactions, then convenience and accessibility are improved, but susceptibility to first-party fraud increases
Solution Approach 1:
The system performs preliminary actions by collecting and storing network details (IP addresses, device identifiers, location data) during initial user interactions and prior transactions. This historical data is stored in advance and used for future fraud assessment, allowing the system to proactively identify fraudulent patterns before they result in chargebacks or losses.
Solution Approach 2:
The system implements feedback by analyzing current transaction network details against historical transaction data and using the results to generate a confirmed user flag. This feedback mechanism continuously improves fraud detection accuracy by learning from past transactions and applying those insights to assess current and future transaction risk.
2Reliability
If credit reporting is used to identify fraudulent activity, then fraud detection is attempted, but precision in identifying specific instances of fraud deteriorates
Solution Approach 1:
The system segments fraud detection into specific transaction-level analysis by comparing network details of individual transactions against historical data for that specific user. Rather than general credit reporting, the system creates customized fraud assessments for each transaction based on segmented user behavior patterns, device usage history, and network characteristics.
Solution Approach 2:
The system replaces traditional mechanical credit reporting mechanisms with a digital analysis system that processes network details, device identifiers, and location data through automated algorithms. This substitution enables precise, real-time fraud detection by replacing manual or batch credit checks with continuous automated analysis of digital footprints.
3Measurement precision
If network details are collected and verified for each transaction, then fraud detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system achieves universality by using a multi-functional platform that handles both legitimate transaction processing and fraud detection through the same infrastructure. The host system performs dual functions: normal transaction authorization and security analysis, eliminating the need for separate fraud detection systems and reducing overall complexity.
Solution Approach 2:
The system implements self-service by automatically collecting, storing, and analyzing network details without requiring additional manual intervention. The host system autonomously compares current transaction data against historical patterns and generates fraud assessments automatically, reducing the need for manual review processes and simplifying operations.
4Reliability
If a confirmed user flag is appended to authorization requests, then transaction security is improved, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-calculating and storing fraud risk assessments based on historical network details and transaction patterns. When a new transaction occurs, the system quickly retrieves pre-analyzed data and generates the confirmed user flag rapidly, rather than performing comprehensive analysis in real-time, thus minimizing processing time delays.
Data Source
AI summary
Systems and methods are provided for associating confirmed user (CU) indications to network-initiated transactions. An example method includes receiving network details for a transaction between a user and a first party and storing the network details in a data repository in association with an transaction identifier for the transaction. The method also includes accessing, from the data repository, network details for multiple prior transactions between the user and the first party and comparing the network details for said transaction and the network details for the multiple prior transactions. The method then includes, based on a match between the network details for said transaction and the network details for the multiple prior transactions, appending a confirmed user flag in an authorization message for the transaction, whereby an issuer relies on the confirmed user flag in deciding to approve or decline the transaction.