Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize transformation and projection layers to implement neural networks in tabular data environments. For example, the disclosed systems identify tabular data sets and segregate measured tabular data values and placeholder tabular data values. In one or more embodiments, the disclosed systems transform the measured tabular data values to a neural network value range based on the distribution of the measured tabular data values. Moreover, the disclosed systems replace placeholder tabular data values with a constant value within the neural network value range. In addition, the disclosed systems identify numerical values and utilize a projection layer to generate feature vectors within a high-dimensionality feature space. In one or more embodiments, the disclosed systems utilize the resulting high-dimensionality tabular data set with a neural network to generate prediction results (e.g.

Abstract: A method, apparatus, system, and computer program code for forecasting a data anomaly to a supply chain. A plurality of data records is identified for a plurality of entities. The data records include import records and export records. The data fields in the data records are categorized into generic field types. The generic field types include numeric fields, categorical fields, and date fields. For each of the plurality of entities, an entity-specific model is constructed for forecasting imports and exports based on the generic field types. The entity-specific model for each of the plurality of entities is combined into a global supply chain model. Based on the global supply chain model, a data anomaly is forecast to a supply chain that is associated with a particular entity.

Abstract: A method for identifying a parent company in a corporate hierarchy is provided. A neural network embeds a number of company names into respective numeric vectors and then indexes the vectors into an approximate nearest neighbors (ANN) index according to euclidean distances between them. In response to a query regarding a parent company of a specified query company, a top N number of nearest neighbor companies to the query company is extracted from the ANN index. A machine learning voting model determines which, if any, of the extracted nearest neighbor companies has a parent company that best corresponds to the query company. If an extracted nearest neighbor company has a parent company that best corresponds to the query company, the parent company is displayed to a user through a user interface.

Abstract: A method, apparatus, system, and computer program code for forecasting a data anomaly to a supply chain. A plurality of data records is identified for a plurality of entities. The data records include import records and export records. The data fields in the data records are categorized into generic field types. The generic field types include numeric fields, categorical fields, and date fields. For each of the plurality of entities, an entity-specific model is constructed for forecasting imports and exports based on the generic field types. The entity-specific model for each of the plurality of entities is combined into a global supply chain model. Based on the global supply chain model, a data anomaly is forecast to a supply chain that is associated with a particular entity.