Resource Activity Confidence Scoring via Baseline Deviation Analysis
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Solution Overview
Problem
In the context of increasing remote resource transfers and electronically automated activities, it is challenging to detect or predict malicious activity, necessitating a system to quickly and intuitively assess confidence in future resource activity with a user or entity.
Innovation Solution
A system that analyzes and weights historical resource activity data to generate a confidence value, allowing seamless communication through multiple channels, authenticates users based on unique patterns, and provides real-time alerts and reward eligibility, using a combination of AI and machine learning to identify trends and potential risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring methods are used for resource activity, then system complexity is low, but the ability to detect malicious activity in remote transfers and automated operations is insufficient
Solution Approach 1:
The system performs preliminary actions by establishing a baseline of normal user behavior patterns before monitoring begins. This baseline includes typical communication channels, resource access patterns, and activity timing. When monitoring starts, deviations from this pre-established baseline are immediately detectable, enabling early detection of malicious activity without requiring complex real-time analysis of every action from scratch.
Solution Approach 2:
The system introduces an intermediary layer that sits between the user's resource activities and the monitoring system. This intermediary captures and analyzes communication patterns across multiple channels (text, audio, video) and translates them into behavioral metrics. This mediation simplifies the monitoring task by preprocessing data into meaningful patterns while maintaining comprehensive detection capability.
2Measurement precision
If comprehensive resource activity data is collected for evaluation, then detection accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system segments the comprehensive resource activity data into distinct categories such as communication patterns, resource access behaviors, temporal patterns, and channel usage. Each segment is processed independently through specialized analysis routines. This segmentation enables parallel processing of different data types, maintaining high evaluation accuracy while reducing overall processing time by avoiding sequential analysis of the entire dataset.
Solution Approach 2:
The system dynamically changes processing parameters based on the specific evaluation context. For low-risk users, it processes fewer parameters at lower computational depth. For high-risk or unusual activities, it increases both the number of parameters analyzed and the computational depth. This adaptive parameter adjustment maintains measurement precision for critical cases while minimizing processing time for routine evaluations.
3Speed
If real-time monitoring and evaluation is implemented, then response time to malicious activity improves, but system resource consumption increases
Solution Approach 1:
The system implements partial monitoring by focusing computational resources on the most critical evaluation aspects rather than analyzing every single data point in real-time. It uses lightweight baseline comparisons for routine activities and reserves intensive analysis for suspicious patterns. This approach maintains fast response speed for detecting malicious activity while consuming fewer system resources by avoiding excessive processing of normal, low-risk behaviors.
Data Source
AI summary
Embodiments of the present invention provide systems and methods for evaluating and weighting resource usage activity data. The system may establish a communicable link to a user device via a user application to receive resource activity data and historical data from one or more users or systems via multiple communication channels. The system may evaluate the historical data and determine evaluation criteria based on perceived chance of loss associated with particular metadata characteristics, and use the evaluation criteria as weighted metrics for determining an overall evaluation score for the user based on indication from resource activity data that the user has conducted resource transfers with entities or channels identified in the historical data.