Data Transfer Instruction Processing with Automated Link Indicators
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Solution Overview
Problem
Current link analysis methods for fraud prevention in data transfers, such as online account opening, rely on manual visual representation review, which is prone to errors and not scalable.
Innovation Solution
An automated process that creates link presence indicators between data records based on variables like email address, device ID, and IP address, without requiring a visual representation, using an algorithm to assign labels and calculate minimum values for linked records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual visual representation review is used for link analysis, then investigator can assess and flag potential issues, but the process is prone to errors and not scalable
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated computer-based system that generates visual representations and performs link analysis automatically. The system uses algorithms to identify connections between records based on shared variables (email addresses, device IDs, IP addresses) without requiring human investigators to manually examine visual representations, thereby eliminating human error while maintaining scalability.
Solution Approach 2:
The system enables self-service automated link analysis where the computer automatically generates visual representations, identifies linked records, and flags potential fraud without human intervention. The automated process independently performs the entire analysis workflow from data processing to fraud detection, making the system both reliable and scalable simultaneously.
2Productivity
If automated link creation is implemented, then scalability is enabled and errors are reduced, but complex algorithms are required to process data relationships
Solution Approach 1:
The patent segments the complex data processing task into distinct computational steps: generating visual representations of records, identifying links based on shared variables, calculating link strength metrics, and flagging suspicious patterns. By breaking down the analysis into modular segments, the system achieves scalability through automated processing while managing algorithmic complexity through structured problem decomposition.
3Loss of information
If visual representation is generated for link analysis, then relationships between records can be identified, but manual review process remains error-prone and inefficient
Solution Approach 1:
The patent replaces manual visual review with automated computer-based analysis that processes generated visual representations instantaneously. The system automatically interprets the visual data, identifies all relationships between records, and flags potential fraud without the time constraints or human errors associated with manual review, thereby preserving complete relationship detection while eliminating time loss.
Data Source
AI summary
Computing platforms, methods, and storage media for processing instructions associated with one or more data transfers are disclosed. Exemplary implementations may: obtain, by the apparatus, input data associated with a set of data transfer requests, the input data including a plurality of records; generate, by the apparatus and based on the input data and in the absence of a visual representation of the plurality of records, link presence indicators for the input data by automatically creating indications of presence of links between the plurality of records based on one or more of the plurality of variables; and create, by the apparatus and for storage in a memory, a set of linked data based on the input data and the generated linking relationships. Exemplary implementations focus on whether, rather than how, items are linked together, in an automated and scalable approach, and may perform analytics on the links and entities.