Online Fraud Mitigation Engine Risk Scoring
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Solution Overview
Problem
Financial services organizations face challenges in detecting fraudulent online transactions due to the ease of hiding identities on the internet, with Internet-related fraud increasing significantly, necessitating effective methods to create a safe and secure online environment.
Innovation Solution
A system and method for determining fraudulent online transactions using an Online Fraud Mitigation Engine (OFME) that computes a risk score based on input parameters and rules, including travel velocity and transaction frequency, to flag potentially fraudulent transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fraud detection methods are used, then implementation is simple, but detection accuracy is insufficient due to ease of hiding identities online
Solution Approach 1:
The fraud detection system is segmented into multiple independent analysis modules: IP address analysis module, travel velocity calculation module, transaction frequency analysis module, and risk scoring module. Each module processes specific aspects of transaction data independently, then combines results to achieve comprehensive fraud detection with higher accuracy while maintaining manageable system complexity through modular design.
Solution Approach 2:
The system transitions from traditional single-dimension fraud detection to multi-dimensional analysis by incorporating geographic location data, temporal data (transaction frequency), and velocity data (travel velocity between locations). This dimensional expansion enables more accurate fraud detection by analyzing transactions from multiple perspectives simultaneously.
2Measurement precision
If comprehensive transaction analysis is performed, then fraud detection accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing baseline data such as user travel patterns, typical transaction frequencies, and geographic information before actual fraud detection occurs. During transaction processing, the system compares real-time data against these pre-established baselines, enabling fast fraud assessment without extensive real-time computation, thus reducing processing time while maintaining high detection accuracy.
Solution Approach 2:
The fraud detection system implements self-service mechanisms by automatically gathering and analyzing multiple data dimensions (IP address, location, velocity, frequency) without requiring manual intervention. The system autonomously computes risk scores and makes fraud determination, eliminating time-consuming manual review processes while comprehensive analysis is performed.
Data Source
AI summary
A fraudulent business transaction application (FBTA) is provided for monitoring application based fraud. When a consumer supplies account access information in order to carry out an Internet business transaction, the FBTA uses an online fraud mitigation engine to detect phishing intrusions and identity theft. Methods are also provided for calculating travel velocity and transaction frequency, which are useful for determining a fraudulent transaction.