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. The detection system with the LDA archetypes and self-calibrating outlier model is implemented with the sliding window technique incorporating new transactions into the topic model and it is capable of operating in real-time for the purpose of identifying frauds and outliers.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for detecting transaction outliers in a dataset representing a plurality of transactions, the method compromising: selecting, by one or more data processors, words of documents associated with a plurality of transaction characteristics in the dataset; executing, by one or more data processors, a topic model using the words of the documents in the dataset to generate a document-archetype probability matrix comprising a plurality of archetypes; assigning, by one or more data processors, each document to at least one archetype of the plurality of archetypes based on probability values associated with the topic model; dividing, by one or more data processors, the documents of the dataset into different subsets based on the at least one archetype; computing, by one or more data processors, statistics of the plurality of transactions within each of the different subsets associated with the at least one archetype; identifying, by one or more data processors, at least one archetype for an incoming transaction; and scoring, by one or more data processors, the one or more incoming transactions based on the statistics of the different subsets associated with the at least one archetype. 2. A method in accordance with claim 1, wherein the scoring the one or more incoming transactions further comprises: selecting at least one subset associated with the archetype of the plurality of archetypes based on transaction characteristics of the new transaction; calculating quantiles for the selected at least one subset for the at least one archetype; scaling variables of the transaction with the quantiles values for the at least one archetype; and calculating the score with the quantile-scaled variables. 3. A method in accordance with claim 2, further comprising: generating, by one or more data processors, a warning for an outlier for conveyance by a computer if the score is higher than a predefined threshold; and updating, by one or more data processors, the topic model in a real time operation environment using the sliding window technique. 4. A method in accordance with claim 1, wherein the transaction characteristics include transaction date, transaction time, amount, location, and/or merchant category code (MCC). 5. A method in accordance with claim 1, wherein the topic model includes a Latent Dirichlet allocation (LDA) model. 6. A computer-implemented method for detecting transaction outliers in a plurality of transactions in a dataset, the method comprising: executing, by one or more data processors, a Latent Dirichlet Allocation (LDA) topic model to obtain a document-topic probability matrix on a collection of documents and words from a plurality of transaction characteristics in a dataset of transactions; splitting, by one or more data processors, the dataset into subsets according to a probability distribution defined by the document-topic probability matrix from the LDA topic model, the probability distribution further defining a plurality of archetypes, each archetype having a designed threshold; associating, by one or more data processors, a new transaction with at least one archetype from the plurality of archetypes; calculating, by one or more data processors, quantiles for each subset of the dataset for the new transaction to generate quantile scaled variables for at least one archetype; scoring, by one or more data processors, the new transaction according to the quantile scaled variables for the at least one archetype; updating, by one or more data processors, the quantiles for each subset for the plurality of transactions including the new transaction; and updating, by one or more data processors, the LDA topic model-based document-topic probability matrix based on the characteristics of the new transaction. 7. A method in accordance with claim 1, wherein the scoring the new transaction further comprises: selecting at least one subset associated with the archetype of the plurality of archetypes based on transaction characteristics of the new transaction; calculating the quantiles for the selected at least one subset; scaling variables of the transaction with the quantiles of the at least one subset; and calculating the score with the quantile-scaled variables associated with the at least one archetype. 8. A method in accordance with claim 2, further comprising: generating, by one or more data processors, a warning for an outlier for conveyance by a computer if the score is higher than a predefined threshold; and updating, by one or more data processors, the topic model in a real time operation environment using the sliding window technique. 9. A method in accordance with claim 1, wherein the plurality of transactions are defined according to transaction characteristics. 10. A method in accordance with claim 9, wherein the transaction characteristics include transaction date, transaction time, amount, location, and/or merchant category code (MCC). 11. A system for detecting transaction outliers in a dataset representing a plurality of transactions, the system comprising: at least one programmable processor; and a machine-readable medium storing instructions that, when executed by the at least one processor, cause the at least one programmable processor to perform operations comprising: selecting words of documents associated with a plurality of transaction characteristics in the dataset; executing a topic model using the words of the documents in the dataset to generate a document-archetype probability matrix comprising a plurality of archetypes; assigning each document to at least one archetype of the plurality of archetypes based on probability values associated with the topic model; dividing the documents of the dataset into different subsets based on the at least one archetype; computing statistics of the plurality of transactions within each of the different subsets associated with the at least one archetype; identifying an archetype of one or more incoming transaction; and scoring the one or more incoming transactions based on the statistics of the different subsets associated with the at least one archetype. 12. A system in accordance with claim 11, wherein the scoring the one or more incoming transactions further includes operations comprising: selecting at least one subset associated with the archetype of the plurality of archetypes based on transaction characteristics of the new transaction; calculating quantiles for the selected at least one subset; scaling variables of the transaction with the quantiles of at least one archetype; and calculating the score with the quantile-scaled variables associated with the at least one archetype. 13. A system in accordance with claim 12, wherein the operations further comprise: generating a warning for an outlier for conveyance by a computer if the score is higher than a predefined threshold; and updating the topic model in a real time operation environment using the sliding window technique. 14. A system in accordance with claim 11, wherein the transaction characteristics include transaction date, transaction time, amount, location, and/or merchant category code (MCC). 15. A system in accordance with claim 11, wherein the topic model includes a Latent Dirichlet allocation (LDA) model.