Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.