Abstract: A method for detecting fraud and non-fraud pattern changes can be based on transaction pathway transversal analysis. A decision tree can be built based on a training dataset from a reference dataset. Pathway transversal information can be recorded along each pathway for the reference dataset. A first mean and a first variance of a class probability can be calculated of all samples over each pathway. A pathway distribution for a new transaction dataset under investigation and a second mean and a second variance of all samples of the new transaction dataset can be obtained. The second mean and the second variance can represent a fraud probability. The deviation metrics between one or more feature statistics of a feature along each pathway for the reference dataset and the new dataset can be determined on a local level. Feature contributors to pattern changes can be determined by analyzing the deviation metrics.

Abstract: Computer implementation methods of processing transactions to determine the fraud risk of transactions incorporating card issuer bin and cardholder location associated with a multitude of customers. The artificial intelligence models developed with such information provide an output of likelihood of fraud for payment card transactions. Disclosed are the methods of utilizing aggregated payment card transaction data at the card issuer bin and card holder location level to improve fraud detection. The implementation of the method is demonstrated to have boosted the performance of the developed models in detection of fraudulent payment cards.

Abstract: The current subject matter describes a method and system of detecting frauds or anomalous behavior. The procedures include extracting characteristics from a dataset to generate words and documents, executing a topic model to obtain the respective probabilities of appearance of a document in each latent archetype, dividing the dataset into a plurality of subsets based upon the archetypes. The formed subsets are further utilized to estimate the quantiles and calculate scores using a self-calibrating outlier model. The score of each new transaction is determined based on a single archetype or based on the sum of weighted scores determined from all the archetypes and associated statistics. Such methods are superior to a simple self-calibration outlier model without an LDA archetype.

Abstract: Computer implementation methods of processing transactions to determine the fraud risk of transactions incorporating card issuer bin and cardholder location associated with a multitude of customers. The artificial intelligence models developed with such information provide an output of likelihood of fraud for payment card transactions. Disclosed are the methods of utilizing aggregated payment card transaction data at the card issuer bin and card holder location level to improve fraud detection. The implementation of the method is demonstrated to have boosted the performance of the developed models in detection of fraudulent payment cards.

Abstract: This document describes detecting fraudulent and anomalous behavior of payment cards. A process includes extracting characteristics from a transaction dataset to generate words and documents associated with payment cards, executing a topic model to obtain the respective probabilities of appearance of a card in each latent archetype, and dividing the card dataset into a plurality of subsets based upon the archetype probability distributions and clustering techniques. The formed subsets are utilized to obtain archetype cluster distribution(s) for each merchant in the dataset. The archetypes are investigated where misalignment with major clusters of archetypes for a merchant may be related to fraudulent transactions. Calculated transaction risks are associated with global archetype cluster membership, merchant-specific archetype cluster membership, and recurrence list positions of transaction details.

Abstract: The current subject matter describes a method and system of detecting frauds or anomalous behavior. The procedures include extracting characteristics from a dataset to generate words and documents, executing a topic model to obtain the respective probabilities of appearance of a document in each latent archetype, dividing the dataset into a plurality of subsets based upon the archetypes. The formed subsets are further utilized to estimate the quantiles and calculate scores using a self-calibrating outlier model. The score of each new transaction is determined based on a single archetype or based on the sum of weighted scores determined from all the archetypes and associated statistics. Such methods are superior to a simple self-calibration outlier model without an LDA archetype.

Abstract: A computer-implemented method of fraud detection includes clustering samples on the tree nodes in the decision tree model on the training dataset, calculating the cluster centroids and determining the high fidelity radius for a preset threshold probability for each cluster and determining the left-over class probability for each node. The new transactional data is classified in three steps: first to determine based on the decision tree what leaf node the transaction is associated, second to determine the membership to a cluster of the leaf node using the shortest distance to the cluster centroid and then third to compare the distance with the high fidelity radius and then to determine the eventual class probability for a new data. The new method demonstrates better performance than the decision-tree alone model.

Abstract: The subject matter disclosed herein provides methods for detecting fraud and non-fraud pattern changes based on transaction pathway transversal analysis. A decision tree can be built based on a training dataset from a reference dataset. Pathway transversal information can be recorded along each pathway for the reference dataset. A first mean and a first variance of a class probability can be calculated of all samples over each pathway. A pathway distribution for a new transaction dataset under investigation and a second mean and a second variance of all samples of the new transaction dataset can be obtained. The second mean and the second variance can represent a fraud probability. A first pathway density distribution can be retrieved for the reference dataset. A second pathway density distribution can be generated for the new transaction dataset.