Employee Activity Outlier Detection via Peer Group Z-Score Analysis
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Solution Overview
Problem
Business entities face challenges in detecting unauthorized or illegal activities by employees that do not individually rise to a level of concern but may be part of a larger scheme, as existing monitoring systems often focus on significant events rather than overall activity rates within peer groups.
Innovation Solution
Establishing consistent peer groupings of employees based on line of business hierarchies and job titles, monitoring activity occurrences, calculating z-scores to identify outliers, and investigating positive z-scores for potential adverse impacts on the business.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If monitoring focuses on significant events with additional approval requirements, then major unauthorized activities are detected, but smaller activities that are part of larger illegal schemes remain undetected
Solution Approach 1:
The patent segments the monitoring approach by dividing employees into peer groups based on job title and organizational hierarchy. Instead of treating all employees uniformly or only monitoring high-level activities, the system segments monitoring into comparable peer groups where statistical anomalies can be detected. This allows the system to focus on relative deviations within homogeneous groups rather than attempting to monitor all activities at all levels with the same intensity.
Solution Approach 2:
The patent changes the monitoring parameter from absolute activity thresholds to relative statistical deviations (z-scores). Instead of setting fixed thresholds for what constitutes suspicious activity, the system calculates each employee's z-score based on their peer group's average activity rate and standard deviation. This parameter transformation enables detection of subtle anomalies that would be invisible under fixed-threshold approaches.
2Reliability
If the business entity monitors all employee activities in detail, then unauthorized activities are detected, but the complexity and cost of the monitoring system increases significantly
Solution Approach 1:
The patent applies local quality by tailoring the monitoring intensity and comparison baseline to each employee's local context (peer group). Instead of applying a uniform monitoring approach across the entire organization, the system creates localized statistical models for each peer group based on their specific job functions and historical activity patterns. This allows reliable detection adapted to local norms without requiring overly complex system-wide modeling.
Solution Approach 2:
The patent uses copying by creating virtual peer group profiles that represent typical activity patterns for each job category. These peer group averages and standard deviations serve as copied templates against which individual employee behaviors are compared. Rather than building complex individual baselines for each employee, the system copies the statistical characteristics of their peer group and uses that as the comparison standard.
3Object-affected harmful factors
If single significant activities trigger preventive measures, then major risks are mitigated, but smaller activities that may indicate larger illegal schemes are not detected
Solution Approach 1:
The patent implements feedback by continuously comparing each employee's activity rate against their peer group's statistical baseline and using the z-score to identify anomalies. This feedback mechanism allows the system to detect deviations at any scale - whether an employee is performing slightly more or significantly more activities than their peers. The continuous feedback loop ensures that both small systematic deviations and large anomalies generate alerts, preventing information loss about potential illegal schemes.
Data Source
AI summary
Systems, methods, and computer program products are provided for identifying activity outliers from amongst employees/associates within a predetermined peer group of employees. The inventive concepts herein disclosed require establishing consistent peer groupings of employees/associates. In specific embodiments, the peer groupings may be defined by combining one or more and, typically two, line of business hierarchies and job title. Once the peer grouping is established, monitoring of pre-determined activities within designated applications is performed to determine the number of occurrence of the predetermined activities over a designated period of time. Activity outliers are subsequently determined based on the number of occurrence of the predetermined activities over the designated period of time. In specific embodiments, such determination of activity outliers includes determining peer group averages, an employee/associate's variance from the average, the employee/associate's standard deviation and the employee/associate's z-score.