Business Anomaly Detection via Information Velocity Vectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Businesses in highly regulated industries, such as banking, face difficulties in detecting compliance concerns and anomalies within their internal processes, as current methods lack the ability to programmatically recognize underwriting errors or omissions, leading to issues in ensuring regulatory compliance.
Innovation Solution
A method that monitors enterprise data flow, analyzes compliance rules, and represents entities and concepts as multi-dimensional vectors to determine information velocity and acceleration, applying statistical analysis to predict and detect business anomalies by comparing historical and projected data utilization, and performing actions when anomalies exceed a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If businesses use traditional monitoring methods to detect compliance concerns, then they can identify issues after they occur, but they cannot proactively detect anomalies or ensure process consistency
Solution Approach 1:
The system performs preliminary actions by establishing baseline patterns of normal data flow and compliance rule violations before actual anomalies occur. It proactively monitors and compares real-time data against these pre-established patterns, enabling early detection of compliance concerns before they manifest as actual problems, thus resolving the contradiction between reliable detection and timely response.
Solution Approach 2:
The system implements continuous feedback loops where compliance data is constantly monitored, analyzed, and compared against established patterns. When deviations are detected, the system provides immediate feedback through alerts and notifications, enabling real-time corrective actions. This feedback mechanism transforms reactive compliance checking into proactive anomaly detection, improving both reliability and response time.
2Measurement precision
If businesses manually review compliance cases on a case-by-case basis, then they can identify specific issues, but they cannot programmatically recognize patterns or trends across multiple cases
Solution Approach 1:
The system replaces manual mechanical review processes with automated computational analysis. Machine learning algorithms and pattern recognition systems analyze compliance data at scale, identifying patterns and anomalies that would be impossible to detect through manual case-by-case review. This substitution maintains high detection precision while dramatically increasing review throughput and productivity.
Solution Approach 2:
The system creates a universal compliance monitoring platform that handles multiple types of compliance rules, data sources, and anomaly patterns through a single automated system. This multi-functional approach enables consistent precision across diverse compliance domains while processing large volumes of cases simultaneously, resolving the contradiction between precise detection and high productivity.
3Reliability
If businesses implement comprehensive compliance monitoring across all processes, then they can ensure regulatory compliance, but the complexity of tracking multiple entities, rules, and data classifications increases
Solution Approach 1:
The system segments the complex compliance monitoring task into manageable components: data collection modules, pattern recognition engines, rule evaluation systems, and alert generation mechanisms. Each component handles specific aspects of compliance monitoring independently, reducing overall system complexity while maintaining comprehensive coverage. This modular segmentation enables reliable compliance assurance across multiple entities and rules without overwhelming system complexity.
Solution Approach 2:
The system introduces intermediary layers including data normalization interfaces, pattern abstraction models, and rule translation mechanisms that simplify the interaction between diverse data sources and compliance requirements. These intermediaries translate complex multi-source data into standardized formats and convert regulatory rules into executable patterns, reducing the apparent complexity of comprehensive monitoring while ensuring reliable compliance detection.
Data Source
AI summary
A method, system and computer program product for detecting business anomalies. Enterprise data as well as compliance rules (e.g., business operational rules) are analyzed to identify entities and concepts to be placed in an ontology. The identified entities and concepts are represented as multi-dimensional vectors. The updates, movements and access to the enterprise data and compliance rules are tracked to determine parameters, such as information velocity, associated with at least some of the elements of the multi-dimensional vectors. The meaning from the enterprise data as well as from the data parameters is discerned. The discerned enterprise data and discerned data parameters are compared with the historical and/or projected utilization of the discerned enterprise data and discerned data parameters to identify any differences. Statistical analysis is then applied based on the enterprise data and the identified differences to generate a value corresponding to a prediction of a business anomaly.