Trust Knowledge Broker for Real-Time Network Fraud Scoring
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Solution Overview
Problem
Existing transaction networks face challenges in effectively detecting fraud due to limited access to comprehensive transaction data, leading to inefficiencies in fraud detection and prevention.
Innovation Solution
A system and method that utilizes a trust knowledge broker to facilitate additional data sharing between merchants and banks through a cloud data pipe and anti-fraud consortium, enabling enhanced fraud detection by integrating transaction contextual data with a distributed trust-base and machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fraud detection methods are used with limited transaction data, then system complexity is reduced, but fraud detection accuracy deteriorates
Solution Approach 1:
The patent introduces a trust knowledge broker as an intermediary component that mediates between merchants and banks. This broker aggregates transaction contextual data from multiple sources including cloud data pipes and anti-fraud consortiums, then provides comprehensive fraud detection capabilities to banks without requiring them to build complex data collection systems themselves. The intermediary handles the complexity of data aggregation and analysis while delivering improved fraud detection accuracy to the end users.
2Reliability
If comprehensive transaction data is shared across the network, then fraud detection capability is improved, but information security risks increase
Solution Approach 1:
The trust knowledge broker serves as a secure intermediary that enables comprehensive data sharing while mitigating security risks. It implements controlled data access mechanisms where merchants, banks, and anti-fraud consortiums can share transaction contextual data through standardized interfaces. The broker aggregates and processes this data centrally, providing fraud detection insights to participating entities without requiring them to directly share sensitive information with each other, thus reducing information security risks while improving fraud detection capability.
3Speed
If real-time fraud evaluation is performed with multiple data sources, then fraud detection speed is improved, but data processing complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-aggregating transaction contextual data from cloud data pipes and anti-fraud consortiums before fraud evaluation is needed. The trust knowledge broker continuously collects and processes transaction data, building a ready-to-use knowledge base of fraud patterns and risk indicators. When a transaction requires fraud evaluation, the system can quickly query this pre-prepared data rather than gathering information in real-time, thus improving fraud detection speed while managing data processing complexity through advance preparation.
Data Source
AI summary
This disclosure relates to systems and methods of risk detection in an electronic network. The method may include receiving a first portion of session context information for an interaction of an individual over a limited bandwidth network, the first portion including a session identifier for the interaction. The method may include retrieving a second portion of session context information for the interaction over a cloud data pipe using the session identifier, in response to receiving the first portion. The method may include accessing, a data structure representing connected knowledge of the individual. The method may include receiving, via a distributed system, security reputation data including aggregated information from a plurality of members. The method may include analyzing the session context information using the security reputation. The method may include generating a security risk score for the interaction based on the session context information, data structure, and the security reputation data.