Adaptive Digital Fraud Detection Workflow
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Solution Overview
Problem
Current digital fraud and abuse detection technologies lack accuracy and real-time response capabilities, failing to effectively detect new threats and automatically evolve to neutralize them.
Innovation Solution
A method for adapting automated decisioning workflows by reconfiguring existing routes based on anomaly detection, tuning decisioning criteria, and simulating new workflows to improve detection and mitigation of digital fraud and abuse, using machine learning models to generate and implement new threat mitigation workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing digital fraud detection technologies are used, then basic detection capability is provided, but detection accuracy and real-time response capability are insufficient
Solution Approach 1:
The patent implements dynamic automated decisioning workflows that can be reconfigured in real-time based on detected anomalies and simulated performance metrics. The system transitions from static detection rules to dynamic, adaptive workflows that respond to changing threat patterns, thereby improving both detection accuracy and real-time response capability simultaneously.
Solution Approach 2:
The system performs preliminary simulation and evaluation of new decisioning routes before deploying them to production. By pre-testing workflows in a virtual environment using historical and real-time data, the system ensures high detection accuracy is achieved without compromising real-time response, as only pre-validated routes are activated.
2Adaptability or versatility
If traditional fraud detection systems are used, then basic threat detection is provided, but the ability to detect and respond to new threats is lacking
Solution Approach 1:
The system implements continuous feedback loops where detection outcomes, anomaly patterns, and performance metrics are fed back into the workflow reconfiguration process. This automated feedback mechanism enables the system to automatically evolve and adapt to new threats by learning from past detection results and adjusting decisioning routes accordingly, enhancing both adaptability and automation.
Solution Approach 2:
The system performs self-configuration and self-optimization of detection workflows without requiring manual intervention for each new threat pattern. The automated decisioning system independently evaluates performance metrics, identifies anomalies, and reconfigures routes to address emerging threats, thereby achieving high adaptability through full automation rather than reducing it.
3Productivity
If manual configuration of detection workflows is used, then precise control is achieved, but response time and adaptability to new threats are reduced
Solution Approach 1:
The patent replaces manual mechanical configuration processes with automated computational systems that use machine learning and simulation to optimize detection workflows. This substitution enables rapid response to new threats while maintaining or improving detection precision through algorithmic optimization rather than human judgment, achieving both speed and accuracy simultaneously.
Solution Approach 2:
The system dynamically adjusts decisioning parameters, thresholds, and route configurations based on real-time performance metrics and detected anomalies. By automatically changing these parameters in response to evolving threat patterns, the system maintains high detection precision while achieving rapid response times, eliminating the trade-off between manual precision and automated speed.
4Extent of automation
If existing automated decisioning workflows are used, then basic automation is provided, but the ability to adapt and evolve based on performance metrics is insufficient
Solution Approach 1:
The patent transforms static automated workflows into dynamic, self-adapting systems that automatically reconfigure based on performance metrics and detected anomalies. The decisioning routes are no longer fixed but can be dynamically adjusted through automated simulation and evaluation processes, enabling the system to maintain high automation levels while simultaneously achieving adaptability to new and evolving threats.
Data Source
AI summary
A system and method for adapting an errant automated decisioning workflow includes reconfiguring digital abuse or digital fraud logic parameters associated with automated decisioning routes of an automated decisioning workflow in response to identifying an anomalous drift or an anomalous shift in efficacy metrics of the automated decisioning workflow, wherein the automated decisioning workflow includes a plurality of distinct automated decisioning routes that, when applied in a digital threat evaluation of data associated with a target digital event, automatically compute a decision for disposing the target digital event based on a probability digital fraud; simulating, by computers, a performance of the automated decisioning routes in a reconfigured state based on inputs of historical digital event data; calculating simulation metrics based on simulation output data of the simulation; and promoting to an in-production state the automated decisioning workflow having the automated decisioning routes in the reconfigured state.