Fraud Scoring Engine Normalization for Dynamic Model Adaptation
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Solution Overview
Problem
Existing fraud detection systems rely on static rules-based models that require frequent manual updates, are prone to errors, and struggle with limited data availability for millennials and other demographics, leading to inefficient fraud identification and potential false positives/negatives, impacting user experience and revenue.
Innovation Solution
A system and method for iteratively measuring transaction scoring data using a fraud scoring engine that normalizes transaction scoring data by applying multiple rules bases sequentially, allowing for automatic updates and optimization based on feedback, thereby improving fraud detection accuracy and reducing human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static rules-based systems are used for fraud detection, then implementation is simple and straightforward, but the systems require frequent manual updates and cannot immediately recognize or correct flaws in fraud decisions
Solution Approach 1:
The patent transforms the static rules-based system into a dynamic machine learning model that automatically adapts to new fraud patterns. The system continuously learns from transaction data and updates its detection capabilities without manual intervention, resolving the contradiction between implementation simplicity and adaptability.
Solution Approach 2:
The machine learning model performs self-updating and self-optimization by automatically learning from new data and adjusting its parameters. This eliminates the need for manual rule updates while maintaining simplicity in deployment, as the system serves itself by continuously improving its fraud detection capabilities.
2Reliability
If manual rule updates are performed frequently, then fraud detection accuracy can be maintained, but human effort and time consumption increase significantly
Solution Approach 1:
The system implements automated feedback loops where transaction outcomes are fed back into the machine learning model, which then automatically adjusts its parameters and rules. This continuous feedback mechanism maintains high detection accuracy without requiring manual rule updates, eliminating time loss while preserving reliability.
Solution Approach 2:
The patent replaces the mechanical process of manual rule creation and updating with an automated machine learning system. The ML model automatically processes transaction data, identifies fraud patterns, and updates detection rules without human intervention, maintaining accuracy while eliminating the time-consuming manual update process.
3Measurement precision
If more data is collected for fraud analysis, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and feature-engineering transaction data before analysis. It extracts only the most relevant features and prepares data in advance, allowing rapid processing of large datasets without sacrificing detection precision. This preliminary preparation enables the system to handle more data efficiently.
Solution Approach 2:
The machine learning model extracts only the most critical features and patterns from large volumes of transaction data, focusing computational resources on the most relevant information. This selective extraction maintains high detection precision while reducing processing time by ignoring redundant or less important data elements.
Data Source
AI summary
Provided are a system and methodology for iteratively measuring data, as between multiple sets thereof, that accounts for underlying data generation sources and bases. Doing so, via normalization of the data, enables uniformity of interpretation and presentation of the data no matter the machine learning model that produced the data.