Data digitization via custom integrated machine learning ensembles

A method comprising:

• receiving, by one or more processors coupled with memory, a data set comprising sheets in a first file type from a plurality of sources, the data set in one of a plurality of formats corresponding to one or more of the plurality of sources;
• identifying, by the one or more processors, a plurality of ensembles, each ensemble of the plurality of ensembles comprising one or more machine learning models and each ensemble to determine an outcome based on an outcome of each machine learning model of each respective ensemble;
• filtering, by the one or more processors using a first ensemble of the plurality of ensembles, a subset of data from the data set based on a threshold of the first ensemble;
• identifying, by the one or more processors using a second ensemble of the plurality of ensembles, a layout for each sheet of the data set based on a vendor type, data type, and the data set;
• executing, by the one or more processors using a third ensemble of the plurality of ensembles, a block detection module to identify blocks of the layout for each sheet of the data set, wherein each block of the blocks comprises a subset of the data set;
• executing, by the one or more processors using a fourth ensemble of the plurality of ensembles, a header detection module to identify headers of each sheet of the data set according to the layout;
• executing, by the one or more processors using a fifth ensemble of the plurality of ensembles, a policy detection module to identify one or more of the headers as policies using a comparison of a first header of the headers to a second header of the headers for each of the headers;
• transforming, by the one or more processors using a sixth ensemble of the plurality of ensembles based on the headers, the layout, the blocks, and the policies, the data set into a format of a second file type different from the plurality of formats;
• and
• presenting, by a display device coupled with the one or more processors, the transformed data set for integration into a capital management system.

A non-transitory computer-readable medium, executing instructions embodied thereon, the instructions to cause one or more processors to:

• receive a data set comprising sheets in a first file type from a plurality of sources, the data set in one of a plurality of formats corresponding to one or more of the plurality of sources;
• identify a plurality of ensembles, each ensemble of the plurality of ensembles comprising one or more machine learning models and each ensemble to determine an outcome based on an outcome of each machine learning model of each respective ensemble;
• filter, using a first ensemble of the plurality of ensembles, a subset of data from the data set based on a threshold of the first ensemble;
• identify, using a second ensemble of the plurality of ensembles, a layout for each sheet of the data set based on a vendor type, data type, and the data set;
• execute, using a third ensemble of the plurality of ensembles, a block detection module to identify blocks for each sheet of the data set based on the layout, wherein each block of the blocks comprises a subset of the data set;
• execute, using a fourth ensemble of the plurality of ensembles, a header detection module to identify headers of each sheet of the data set according to the layout;
• execute, using a fifth ensemble of the plurality of ensembles, a policy detection module to identify one or more of the headers as policies using a comparison of a first header of the headers to a second header of the headers for each of the headers;
• transform, using a sixth ensemble of the plurality of ensembles based on the headers, the layout, the blocks, and the policies, the data set into a format of a second file type different from the plurality of formats;
• and
• present, by a display device coupled with the one or more processors, the transformed data set for integration into an electronic transaction system.