Multi-Channel Transaction Segmentation for Entity-Level Analysis
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Solution Overview
Problem
Existing transaction systems struggle to efficiently process and analyze data from multiple interaction channels due to the inability to differentiate between unique identifiers, leading to inefficiencies in data transmission and analysis.
Innovation Solution
A multi-channel adapter system that selectively retrieves data from various transaction channels based on client-defined parameters, detects unique identifiers, and generates separate files for each identifier, enabling seamless data transmission and analysis at the client device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If transaction systems process data from multiple interaction channels without differentiating unique identifiers, then data retrieval can be performed, but data processing efficiency deteriorates and analysis accuracy deteriorates
Solution Approach 1:
The patent segments transaction data by unique identifiers (such as account numbers, customer IDs, or transaction reference numbers) to create distinct data groups. This segmentation allows the system to process and analyze transactions belonging to different entities separately, preventing data confusion and improving processing efficiency. The multi-channel adapter divides the incoming data stream into manageable segments that can be handled independently.
Solution Approach 2:
The patent introduces a multi-channel adapter as an intermediary component between various interaction channels and the transaction processing system. This adapter detects unique identifiers in incoming data and routes or tags transactions accordingly, serving as a mediator that ensures proper differentiation and handling of transactions from multiple channels before they reach the core processing system.
2Quantity of substance
If transaction systems retrieve data from multiple interaction channels, then comprehensive transaction data can be obtained, but data transmission efficiency deteriorates due to lack of selective retrieval
Solution Approach 1:
The patent extracts only the necessary transaction data that meets client-defined parameters from multiple interaction channels, rather than retrieving all available data. The multi-channel adapter filters and extracts relevant transactions based on criteria such as time period, transaction type, amount ranges, or specific account identifiers, reducing the volume of data that needs to be transmitted and processed while still providing comprehensive information for analysis.
Solution Approach 2:
The patent applies local quality by customizing data retrieval parameters for different clients or different data sources. Each client can define specific parameters tailored to their analysis needs, and the system adjusts the data extraction process accordingly. This allows different parts of the system to retrieve different subsets of data based on local requirements, optimizing transmission efficiency while maintaining data comprehensiveness for each specific use case.
3Loss of information
If transaction systems analyze aggregated data from multiple entities, then overall transaction patterns can be observed, but account activity analysis accuracy deteriorates
Solution Approach 1:
The patent segments transaction data by unique identifiers to maintain distinct records for different entities while still enabling aggregate analysis. By organizing data into separate groups based on unique identifiers (such as customer IDs or account numbers), the system preserves the ability to analyze overall patterns across all entities while simultaneously allowing precise analysis of individual account activities when needed.
Solution Approach 2:
The patent creates a multi-functional data structure that serves both aggregate and individual analysis needs. The same segmented data organization enables both high-level overview of transaction patterns across multiple entities and detailed examination of specific account activities, making the system universal in its analytical capabilities without requiring separate processing systems.
Data Source
AI summary
A computer-implemented method includes storing parameters that define data requested by a client device. The method also includes receiving the data that satisfies the parameters from interaction channels in response to a request for the data. The method further includes detecting unique identifiers in the data. Each unique identifier of the unique identifiers can be associated with an entity. Additionally, the method includes generating subsets of data for each of the unique identifiers. The method also includes causing a display of the subsets of data for each unique identifier at a user interface of the client device. The subsets of data for each unique identifier can be sent to the client device as separate files.