Entity Graph Edge Adjustment for Deal Relevance
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Solution Overview
Problem
The 'deals marketplace' faces challenges in providing consumers with relevant deals, leading to consumer disengagement due to poorly tailored offers, and lacks effective systems for fraud detection and risk management.
Innovation Solution
A system and method for generating an entity graph based on relationships between entities using internal, external, and online data, allowing for tailored marketing, fraud detection, and risk management by updating the graph with new data and adjusting relationships, and combining entity graphs to create a universal graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional deal distribution systems are used, then deals can be delivered to consumers, but the deals are poorly tailored and irrelevant to consumers
Solution Approach 1:
The system segments consumers into distinct groups based on their preferences, behaviors, and characteristics stored in the consumer database. This segmentation enables tailored deal distribution by matching deals to specific consumer segments rather than using blanket distribution, directly resolving the contradiction between deal delivery and deal relevance.
Solution Approach 2:
The system dynamically updates consumer preferences, behaviors, and characteristics in the consumer database based on ongoing transactions and interactions. This dynamic adaptation allows the system to continuously improve deal tailoring accuracy over time, resolving the contradiction by making the system responsive to changing consumer needs.
2Adaptability or versatility
If comprehensive data collection is implemented to improve deal tailoring, then consumer preferences can be better understood, but system complexity increases
Solution Approach 1:
The consumer database serves multiple functions: storing consumer preferences, tracking behaviors, recording characteristics, and enabling both deal tailoring and fraud detection. This multi-functionality reduces overall system complexity by consolidating data collection and processing into a single universal database rather than requiring separate systems for each function.
Solution Approach 2:
The system automatically collects and processes consumer data through routine transactions and interactions without requiring manual intervention. Consumers effectively 'self-service' the data collection process by providing information naturally during their normal activities, reducing the complexity of active data gathering mechanisms.
3Reliability
If real-time fraud detection is implemented, then fraud protection is enhanced, but processing time and system complexity increase
Solution Approach 1:
The system performs preliminary fraud detection checks by analyzing consumer patterns and characteristics before transactions are completed. By pre-establishing consumer profiles and detection rules in the database, the system can quickly evaluate transactions against these pre-prepared criteria, enhancing fraud protection without significantly increasing processing time.
Solution Approach 2:
The system uses feedback from transaction outcomes and consumer behaviors to continuously refine fraud detection algorithms and consumer profiles. This feedback loop improves fraud detection accuracy over time while optimizing processing efficiency, as the system learns to distinguish between legitimate and fraudulent patterns more effectively.
Data Source
AI summary
The present disclosure includes a system, method, and article of manufacture for generating an entity graph. The method may comprise determining a relationship between a first entity and a second entity based upon internal data, external data, and/or online data associated with the first entity, and generating the entity graph comprising at least two nodes and an edge connecting the at least two nodes. The method may further comprise, in various embodiments, tailoring marketing to the first entity based upon the entity graph, detecting fraud against the first entity based upon the entity graph, periodically updating the entity graph based upon new internal data and new online data, and/or adjusting the edge based upon a change in the relationship between the first entity and the second entity.