Fraud Detection via Online Interaction and Transaction Data Correlation
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Solution Overview
Problem
Current methods for detecting online scams and frauds rely on single datapoints, such as transaction data or web information, limiting their effectiveness as financial fraud detectors lack access to historical financial data or consumer behavior baselines, and websites lack access to transaction data.
Innovation Solution
Correlating online interaction data with financial transaction data to detect suspicious activities by identifying matching monetary amounts and reputations, triggering security actions such as blacklisting or generating alerts, to improve fraud detection and prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If financial fraud detectors rely on single datapoint (transaction data or web information), then the system complexity is low, but the detection accuracy and reliability are insufficient
Solution Approach 1:
The patent combines web information data and transaction data into a unified fraud detection system. The correlation module merges these previously separate data sources, allowing the system to cross-validate information from both domains simultaneously, thereby improving detection reliability without requiring completely separate systems
Solution Approach 2:
The fraud detection system is designed to handle multiple types of data (web information, transaction data, consumer reports) and perform multiple functions (detecting scams, analyzing behavior patterns, generating alerts). This multi-functional approach allows a single system to address various fraud detection needs, improving reliability while managing complexity through unified architecture
2Loss of information
If banks use transaction data for fraud detection, then access to financial data is available, but access to website information and consumer behavior baselines is lost
Solution Approach 1:
The patent introduces a correlation module that acts as an intermediary between transaction data systems and web information systems. This mediator component receives data from both sources, correlates them based on matching criteria (URLs, timestamps, amounts), and produces integrated fraud detection results, thereby enabling information access while managing integration complexity
3Speed
If websites are monitored for suspicious interactions, then real-time detection capability is improved, but historical financial data and consumer behavior baselines become inaccessible
Solution Approach 1:
The system performs preliminary actions by pre-establishing correlation criteria and data matching rules before actual fraud detection occurs. Consumer behavior baselines and historical patterns are used to create pre-defined suspicious interaction patterns, enabling real-time detection speed while retaining the benefit of historical analysis through pre-computed reference data
Data Source
AI summary
The disclosed computer-implemented method for detecting websites that perpetrate at least one of scams or frauds may include correlating online interaction data with financial transaction data. The online interaction data may include information on suspicious websites obtained through an online interaction analysis, and the financial transaction data may include sources of suspicious financial activity obtained through a transaction trend analysis. The method may additionally include detecting at least one of online scams or frauds based on the correlation. The detection may include detecting that an online interaction is suspicious based on correlation thereof to a suspicious financial transaction, and/or detecting that a financial transaction is suspicious based on correlation thereof to a suspicious online interaction. The method may also include performing a security action in response to the detection. Various other methods, systems, and computer-readable media are also disclosed.