Address Risk ML Model for Fraud Detection
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Solution Overview
Problem
Historical fraud detection methods are inadequate due to reliance on incomplete information, and notifications associated with interactions are limited, failing to provide essential information about fraud, safety, or completion status.
Innovation Solution
A method using a machine learning model to assess fraud risk by analyzing address information, including labeled data on flagged and non-fraudulent behaviors, and transmitting notifications based on calculated risk scores, with the ability to standardize and weight different types of behaviors for accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive address information is used for fraud detection, then fraud detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments fraud detection into multiple independent scoring components: address risk score, entity risk score, interaction risk score, and device risk score. Each component evaluates specific aspects of the transaction independently, allowing comprehensive analysis while maintaining modular system architecture that manages complexity.
Solution Approach 2:
The patent introduces a fraud score generator as an intermediary component that aggregates data from multiple sources (address information, entity information, interaction information, device information) and transforms it into a comprehensive fraud risk assessment. This intermediary layer simplifies the overall system by centralizing the complex analysis logic.
2Loss of information
If multiple types of notifications are transmitted to entities, then information completeness is improved, but communication overhead increases
Solution Approach 1:
The notification system dynamically adapts its behavior based on the calculated fraud risk score. When risk is low, minimal notifications are sent. When risk exceeds thresholds, the system selectively transmits specific notification types (fraud warnings, safety alerts, completion confirmations) tailored to the risk level and interaction stage, optimizing communication efficiency.
Solution Approach 2:
The system implements feedback mechanisms where notifications are sent based on risk assessments, and subsequent entity responses are monitored to refine future notifications. The fraud detection model is continuously updated based on notification outcomes and entity behaviors, creating a closed-loop system that reduces unnecessary communications over time.
Data Source
AI summary
A method of fraud risk assessment, comprising: receiving labeled data that includes address information for one or more addresses and labels corresponding to the one or more addresses; training an address risk machine learning model capable of predicting a risk of fraud for an address by determining relationships among the labeled data; and determining, using the address risk machine learning model, an address risk score of a first address of the one or more addresses based on the labeled data of the first address.