Automated Multi-Party Event Detection for Shared Expense Settlement
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Solution Overview
Problem
Existing systems lack the ability to proactively identify, detect, and predict multi-party transactions or events that result in shared expenses, leading to time-consuming and error-prone manual processes for settling up group-related transactions.
Innovation Solution
A system that automatically identifies potential multi-party events and transactions by analyzing user data, determines associated parties, and manages payment delegation based on an optimal scheme, linking received payments to the original transaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used to identify and settle multi-party transactions, then system complexity is low, but time consumption and error rate increase
Solution Approach 1:
The system performs preliminary identification and classification of multi-party transactions before settlement. Event detection modules proactively identify potential multi-party events by analyzing transaction patterns, user relationships, and event characteristics in advance, preparing the groundwork for automated settlement processes.
Solution Approach 2:
The patent introduces an automated decisioning system as an intermediary between transaction detection and settlement. This system includes event detection modules, party identification modules, and payment scheme determination modules that mediate the complex process of identifying, verifying, and settling multi-party transactions automatically.
2Measurement precision
If automated detection of multi-party events is implemented, then transaction identification accuracy improves, but data processing requirements increase
Solution Approach 1:
The automated detection system is segmented into multiple specialized modules: event detection modules that identify potential multi-party events, party identification modules that determine involved parties, and payment scheme determination modules that calculate settlement amounts. This segmentation allows each module to focus on specific aspects of detection, improving accuracy while distributing processing load efficiently.
Solution Approach 2:
The system applies partial action by selectively analyzing only transactions that exhibit characteristics of multi-party events, rather than processing all transactions uniformly. The event detection modules use heuristics and patterns to identify candidate events, then apply more rigorous analysis only to these candidates, reducing overall data processing requirements while maintaining high detection accuracy.
Data Source
AI summary
Methods, systems, devices, and computer-readable media for detecting multi-party events and transactions are provided. User data may be monitored to detect data associated with an event involving multiple individuals, such as by identifying transaction data associated with certain types of merchants and/or scheduling, calendar, or correspondence data indicative of an event. The data may be further analyzed to identify a date, location, and/or parties associated with the event. A multi-party event may be generated. The user data may continue to be monitored to identify transactions associated with multiple parties and occurring during a time and/or at a location of the event. At a conclusion of the event, the transactions may be aggregated and an optimal payment scheme may be determined for settlement of the transactions between the parties. In accordance with the determined payment scheme, delegation of portions of the aggregated transactions may be initiated for settlement amongst the parties.