Anomaly Detection in Clustered Financial Data
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Solution Overview
Problem
Complex data sets with clustered elements pose challenges in anomaly detection due to their inherent complexities and interrelationships, particularly in financial assets where comparing and analyzing these data sets is difficult.
Innovation Solution
A computer-implemented method and system for generating visual representations of financial interests by receiving input data sets, extracting features, generating clusters, detecting anomalies, and providing adjustment recommendations to modify fund characteristics and enforce rules such as diversification and risk management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If complex data sets with clustered elements are analyzed using traditional methods, then analysis completeness is maintained, but analysis difficulty and time consumption increase significantly
Solution Approach 1:
The patent segments the complex analysis task into multiple hierarchical clustering levels. First, data elements are clustered into groups based on shared characteristics, then anomalies are detected within each cluster separately. This segmentation reduces the overall complexity by breaking down the large-scale anomaly detection problem into smaller, more manageable sub-problems, thereby improving analysis efficiency while maintaining detection accuracy.
2Measurement precision
If traditional anomaly detection methods are applied to clustered financial data, then comprehensive analysis is achieved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary clustering actions before anomaly detection. By pre-organizing financial data into clusters based on characteristics such as asset type, risk profile, and performance metrics, the system prepares the data structure in advance. This preliminary organization enables faster anomaly detection subsequent to clustering, as the detection process can focus on comparing elements within pre-defined groups rather than analyzing the entire dataset from scratch, thus reducing processing time while maintaining detection precision.
3Measurement precision
If detailed feature extraction is performed on all financial assets, then detection accuracy improves, but computational resources and processing complexity increase
Solution Approach 1:
The patent applies local quality by extracting features specifically relevant to each cluster type rather than uniformly across all data. Different clusters (e.g., equity funds, bond funds, mixed-asset funds) have different characteristic features that are extracted and analyzed. This localized feature extraction approach improves detection accuracy for each specific cluster while reducing overall system complexity by avoiding the extraction and processing of all possible features for every data point.
Data Source
AI summary
A method for generating visual representations of financial interests includes: receiving an input data set including one or more data structures storing data fields and data values representative of financial interests; extracting, from the input data, one or more extracted features from the funds, the extracted features collectively indicative of a distance between different funds; generating one or more clusters of funds, based on the extracted features of the funds; determining, based on identified differences between one or more funds relative to at least one other fund in a corresponding cluster of funds, one or more fund anomalies based on the one or more extracted features; generating one or more adjustment recommendations based on the one or more fund anomalies, the one or more adjustment recommendations representing control instruction sets for automatically modifying characteristics of the corresponding fund.