C2C Transaction Risk Analysis via Multi-Source Data Fusion
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Solution Overview
Problem
Current methods for determining the safety of consumer-to-consumer transactions on online platforms, such as eBay and Amazon, rely heavily on subjective star ratings, which are not reliable for assessing risk, as they do not provide a comprehensive view of a user's transaction history and behavior beyond the specific platform.
Innovation Solution
A system and method that determine risk levels for C2C transactions by combining payment data from a payment processing network, such as Visa, with external data like star ratings, geo-location, and fraud bureau information, allowing for a more comprehensive assessment of a consumer's creditworthiness and transaction history, and enabling the sharing of risk levels between parties before a transaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If star rating systems are used to assess transaction safety, then ease of operation is improved, but reliability of risk assessment deteriorates
Solution Approach 1:
The patent combines multiple data sources including payment network data, external fraud data, and behavioral data into a unified risk score. This merging of diverse data streams transforms the simple star rating system into a comprehensive risk assessment mechanism that maintains ease of use while significantly improving reliability through multi-faceted evaluation.
Solution Approach 2:
The risk assessment system uses a composite approach by integrating multiple types of data (payment history, fraud indicators, behavioral patterns) to create a composite risk score. This composite methodology ensures that no single data source dominates, providing a more reliable and nuanced assessment while keeping the interface simple for users.
2Reliability
If comprehensive payment data and external data are collected for risk assessment, then reliability of risk determination is improved, but device complexity and data processing requirements worsen
Solution Approach 1:
The patent introduces a centralized risk scoring system that acts as an intermediary between multiple data sources and the transaction platform. This intermediary consolidates complex data processing tasks, managing payments data, fraud data, and behavioral data through unified algorithms, thereby improving reliability while containing complexity within a dedicated system rather than distributing it across the entire platform.
Solution Approach 2:
The system replaces manual or simple automated risk assessment mechanisms with sophisticated algorithms that automatically process multiple data streams. These algorithms substitute complex mechanical or manual evaluation processes with computational models that can handle diverse data types efficiently, improving reliability without proportionally increasing operational complexity.
3Reliability
If risk levels are shared between consumers before transactions, then reliability of transaction safety is improved, but loss of information privacy worsens
Solution Approach 1:
The patent extracts only the essential risk assessment information needed for transaction safety and shares this distilled data between consumers, rather than exposing complete personal or financial records. By taking out only the necessary risk indicators while leaving sensitive personal information protected, the system improves transaction safety while minimizing privacy loss.
Solution Approach 2:
The system applies different levels of information disclosure to different users based on their needs and consent. Risk information is shared selectively and locally between trading parties rather than making all data globally visible, allowing each user to receive only the information relevant to their transaction safety concerns while preserving overall privacy.
Data Source
AI summary
Systems and methods are presented for determining risk levels for consumer-to-consumer (C2C) transactions on brokering websites. The risk levels can be based on payment processing network (e.g. Visa) account data as well as external data, such as geo-location using IP addresses, fraud bureau data, and star ratings. The buying and selling consumers can have multiple risk scores for each transaction, such as a fraud, credit, return/chargeback, dissatisfaction, product misuse, nonreturnable, and defect risk scores. The buying and selling consumers can trade their risk levels before proceeding with the transaction.