Abstract: This disclosure relates to method and system of advanced summarization of documents using topic intelligence-based Retrieval Augmented Generation (RAG). The method may include receiving, through a GUI, a set of documents and summarization instructions for the set of documents. For each of the set of documents, the method may further include determining a set of topics in a document LSI and a hybrid ensemble of statistical techniques. For each of the set of topics, the method may further include identifying a set of relevant sentence chunks from the plurality of sentence chunks through the LSI based on the summarization instructions. For each of the set of topics, the method may further include generating, through an LLM, a topic summary based on the set of relevant sentence chunks using a RAG model, and generating an advanced summary of document based on topic summary of set of topics and general RAG-based summary.

Abstract: A method for generating target concept-based knowledge graphs is disclosed. The method includes receiving, via a GUI, input data from a user device. The method further includes generating a factor-category list includes a plurality of factors, each associated with one of a set of categories, based on the input data using a Large Language Model (LLM). The method further includes creating a plurality of target concept embeddings from the input data and the factor-category list using an embedding model. The method further includes identifying a relevant set of a plurality of domain embeddings from a Retrieval Augmented Generation (RAG) model based on a similarity analysis of the plurality of target concept embeddings with the plurality of domain embeddings. The method further includes providing a knowledge graph generation prompt to the LLM. The method further includes recursively generating a knowledge graph based on the knowledge graph generation prompt, using the LLM.

Abstract: At least some embodiments are directed to an entity classification system that receives informational data associated with an entity. The informational data includes sentences associated with the entity. The entity classification system utilizes a first machine learning model to determine a first contextual meaning among words of a sentence associated with the entity based on a first word embedding technique, and determines at least one category associated with the entity based at least in part on the first contextual meaning. The entity classification system utilizes a second machine learning model to determine a second contextual meaning shared by a set of sentences based on a second embedding technique, and determines a subcategory of the category associated with the entity based at least in part on the second contextual meaning. The entity classification system generates an output including the category and subcategory associated with the entity.

Abstract: At least some embodiments are directed to an entity classification system receives informational data associated with an entity. The informational data includes sentences associated with the entity. The entity classification system utilizes a first machine learning model to determine a first contextual meaning among words of a sentence associated with the entity based on a first word embedding technique, and determines at least one category associated with the entity based at least in part on the first contextual meaning. The entity classification system utilizes a second machine learning model to determine a second contextual meaning shared by a set of sentences based on a second embedding technique, and determines a subcategory of the category associated with the entity based at least in part on the second contextual meaning. The entity classification system generates an output including the category and subcategory associated with the entity.

Abstract: At least some embodiments are directed to an entity classification system receives informational data associated with an entity. The informational data includes sentences associated with the entity. The entity classification system utilizes a first machine learning model to determine a first contextual meaning among words of a sentence associated with the entity based on a first word embedding technique, and determines at least one category associated with the entity based at least in part on the first contextual meaning. The entity classification system utilizes a second machine learning model to determine a second contextual meaning shared by a set of sentences based on a second embedding technique, and determines a subcategory of the category associated with the entity based at least in part on the second contextual meaning. The entity classification system generates an output including the category and subcategory associated with the entity.

Abstract: At least some embodiments are directed to an entity classification system receives informational data associated with an entity. The informational data includes sentences associated with the entity. The entity classification system utilizes a first machine learning model to determine a first contextual meaning among words of a sentence associated with the entity based on a first word embedding technique, and determines at least one category associated with the entity based at least in part on the first contextual meaning. The entity classification system utilizes a second machine learning model to determine a second contextual meaning shared by a set of sentences based on a second embedding technique, and determines a subcategory of the category associated with the entity based at least in part on the second contextual meaning. The entity classification system generates an output including the category and subcategory associated with the entity.

Abstract: At least some embodiments are directed to an entity classification system receives informational data associated with an entity. The informational data includes sentences associated with the entity. The entity classification system utilizes a first machine learning model to determine a first contextual meaning among words of a sentence associated with the entity based on a first word embedding technique, and determines at least one category associated with the entity based at least in part on the first contextual meaning. The entity classification system utilizes a second machine learning model to determine a second contextual meaning shared by a set of sentences based on a second embedding technique, and determines a subcategory of the category associated with the entity based at least in part on the second contextual meaning. The entity classification system generates an output including the category and subcategory associated with the entity.

Abstract: Disclosed are various embodiments for generating training data for machine-learning models. A plurality of original records are analyze to identify a probability distribution function (PDF), wherein a sample space of the PDF comprises the plurality of original records. A plurality of new records are generated using the PDF. An augmented dataset that includes the plurality of new records is created. Then, a machine-learning model is trained using the augmented dataset.

Abstract: The system may be configured to perform operations including receiving a transaction authorization request comprising transaction details; inputting the transaction details into a fraud scoring system comprising a fixed fraud detection model; inputting the transaction details into a neural network comprising an improvable fraud detection model; applying the fixed fraud detection model and the improvable fraud detection model to the transaction details; producing a fraud score in response to applying the fixed fraud detection model to the transaction details and a neural network fraud score in response to applying the improvable fraud detection model to the transaction details; analyzing the fraud score and the neural network fraud score; and/or sending an authorization response in response to analyzing the fraud score and the neural network fraud score.

Abstract: A computer-implemented method of tagging a transaction that includes tagging a transaction with one of a first tag value or a second tag value, forming a set of clusters associated with the tagged transactions having a first value, and forming a second set of clusters associated with the tagged transactions having a second value. The computer implemented method also includes determining a fuzzy tag value based on a relationship between the transaction and one of the centroids of the clusters having a first tag value, and one of the centroids of the clusters having second value. The method also includes replacing the first tag value or the second tag value with the fuzzy tag value.

Abstract: A system and method for automated variable creation for adaptive fraud analytics are disclosed. A data structure for creation of rules is generated. The data structure represents nodes and associations between nodes from inputs for fraud/non-fraud conditions, and is generated from fraud and non-fraud data collected in an adaptive modeling process from past transactions. All unique paths between nodes of the data structure are determined to define a rule for each path. Each rule is then converted to a binary indicator variable to generate a set of binary indicator variables, and one or more complex variables is derived from the set of binary indicator variables. The one or more binary indicator variables and one or more complex variables can be provided to an adaptive scoring engine to score new transactions or to predict future behaviors.

Abstract: A system and method for automated variable creation for adaptive fraud analytics are disclosed. A data structure for creation of rules is generated. The data structure represents nodes and associations between nodes from inputs for fraud/non-fraud conditions, and is generated from fraud and non-fraud data collected in an adaptive modeling process from past transactions. All unique paths between nodes of the data structure are determined to define a rule for each path. Each rule is then converted to a binary indicator variable to generate a set of binary indicator variables, and one or more complex variables is derived from the set of binary indicator variables. The one or more binary indicator variables and one or more complex variables can be provided to an adaptive scoring engine to score new transactions or to predict future behaviors.

Abstract: A computer-implemented method of tagging a transaction that includes tagging a transaction with one of a first tag value or a second tag value, forming a set of clusters associated with the tagged transactions having a first value, and forming a second set of clusters associated with the tagged transactions having a second value. The computer implemented method also includes determining a fuzzy tag value based on a relationship between the transaction and one of the centroids of the clusters having a first tag value, and one of the centroids of the clusters having second value. The method also includes replacing the first tag value or the second tag value with the fuzzy tag value.