Card-Not-Present Fraud Prediction Using Machine Learning Models
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Solution Overview
Problem
Conventional network-transaction-security systems are inaccurate and inefficient in detecting fraudulent network transactions using compromised credit card information, relying on heuristic computing models that process minimal information and often result in false approvals or denials.
Innovation Solution
A card-not-present machine learning model is used to generate fraud predictions for network transactions by identifying features such as merchant data, user account data, and historical transaction data, allowing for real-time processing and authorization or denial of transactions based on the prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If heuristic computing models are used to process transaction information, then the system can operate with minimal information input, but the accuracy in detecting fraudulent transactions deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and storing additional transaction features and data elements before the fraud detection analysis. This includes gathering device information, location data, transaction history, and merchant information in advance, so that when a transaction occurs, the machine learning model has comprehensive data ready for immediate analysis without requiring additional real-time input from users
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the minimal transaction input and the fraud detection decision. This intermediary processes the limited input data through complex algorithms that have been trained on extensive historical data, effectively bridging the gap between simple input requirements and high-accuracy detection needs
2Productivity
If heuristic computing models process only minimal transaction information, then the processing speed is maintained, but the reliability of fraud detection deteriorates
Solution Approach 1:
The system pre-collects and pre-processes additional transaction features including device information, geolocation data, transaction history, and merchant categorization before the actual fraud detection occurs. This preliminary data gathering enables the machine learning model to perform comprehensive analysis without adding real-time processing delays to the transaction flow
Solution Approach 2:
The patent transforms the fraud detection approach by changing the parameters used for analysis - moving from simple heuristic rules based on minimal data to complex machine learning models that analyze multiple transformed parameters including transaction patterns, device fingerprints, location consistency, and historical behavior metrics
3Device complexity
If conventional systems rely on serial disputes to identify fraud, then the current system architecture is maintained, but the loss of time in responding to fraudulent charges increases
Solution Approach 1:
The system performs preliminary fraud assessment at the point of transaction authorization using the machine learning model, before any dispute can occur. This preliminary action identifies potentially fraudulent transactions in real-time, preventing them from completing and thus eliminating the need for subsequent dispute resolution processes
Solution Approach 2:
The patent enables the system to skip the traditional serial dispute process by implementing real-time fraud detection that makes authorization decisions immediately. The machine learning model rushes through the analysis of multiple data features in seconds, providing instant fraud assessment that eliminates the lengthy back-and-forth dispute resolution timeline
4Device complexity
If heuristic models are used with minimal information, then the system simplicity is preserved, but the false positive and false negative rates increase
Solution Approach 1:
The patent segments the fraud detection system into distinct functional components: a simple interface that accepts minimal user input, a backend data collection layer that gathers additional features, a machine learning model layer that performs complex analysis, and a decision layer that outputs authorization results. This segmentation allows the system to maintain simple user interaction while incorporating sophisticated fraud detection internally
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a card-not-present machine learning model to generate a fraud prediction for a network transaction where a credit card used for the transaction is not present. In particular, in one or more embodiments, the disclosed systems identify features associated with the network transaction and generate, utilizing the card-not-present machine learning model, a fraud prediction based on the identified features. The disclosed systems can also apply transaction logic based on the fraud prediction to process the network transaction by performing an authorizing, declining, or other action with regard to the network transaction.