Automated Rule Engine for Fraud Detection
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Solution Overview
Problem
Conventional rules engines require manual intervention for adding, tuning, and removing rules, which is time-consuming and prone to inefficiencies, especially in detecting evolving fraudulent activities.
Innovation Solution
An automated system that generates and maintains rules using labeled data sets, employing supervised and unsupervised machine learning to produce and update rules, along with a user interface for monitoring rule health and effectiveness, allowing for the integration of manually generated rules for validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual rule creation and maintenance is used, then domain expertise can be applied to create effective rules, but the process is time-consuming and requires continuous manual intervention
Solution Approach 1:
The system enables automated rule generation where the rules engine automatically creates, tunes, and removes rules based on incoming data without requiring continuous manual human intervention. The system serves itself by automatically generating rules from data patterns, evaluating their performance, and maintaining them autonomously.
Solution Approach 2:
The patent replaces the manual mechanical process of rule creation with an automated computational system. Instead of humans manually creating and maintaining rules, machine learning algorithms and automated analysis processes generate and manage rules based on data patterns, substituting human manual work with automated computational mechanisms.
2Measurement precision
If manual rule tuning is performed, then rule accuracy can be improved, but the process becomes complex and time-consuming
Solution Approach 1:
The rules engine automatically evaluates rule performance using effectiveness metrics and false positive rates, then autonomously tunes and adjusts rules based on this feedback. The system monitors its own performance and self-corrects without requiring external manual tuning, reducing both complexity and time requirements.
Solution Approach 2:
The system implements a feedback loop where rule performance is continuously measured using effectiveness metrics and false positive rates. This feedback information is automatically used to tune and adjust rules, creating a closed-loop system that continuously improves accuracy without manual intervention.
3Productivity
If automated rule generation is implemented, then manual intervention is eliminated and rules are frequently updated, but the system requires sophisticated machine learning techniques and data processing
Solution Approach 1:
The patent replaces manual rule creation processes with automated machine learning-based rule generation. Computational algorithms analyze data patterns and automatically generate rules, substituting human expertise with automated computational processes that can operate continuously and scale efficiently.
Solution Approach 2:
The rules engine is designed as a universal system that can handle multiple types of rules, data formats, and evaluation metrics through a single automated framework. The system performs multiple functions including data analysis, rule generation, performance evaluation, and automatic tuning, consolidating what would otherwise require multiple separate processes.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for rule generation and interaction. A rules engine is provided that does not require manual effort to generate or maintain high-quality rules over time for detecting malicious accounts or events. Rules no longer need to be manually added, tuned, or removed from the system. The system is able to determine the health of each rule and automatically add, tune, and remove rules to maintain a consistent, effective rule set.