Abstract: A method and system assist users of an electronic bookkeeping system to sort financial transactions into accounts representing bookkeeping categories. The method and system generate vectors representing the accounts and the merchants known to the bookkeeping system. The method and system generate compressed versions of the vectors by compressing the vectors. The method and system assist users to sort the financial transactions by analyzing the compressed vectors.

Abstract: Systems and methods for authenticating a user are disclosed.

Abstract: A method including training predictor machine learning models (MLMs) using a first data set. The trained predictor MLMs are trained to predict classifications of data items in the first data set. The method also includes training confidence MLMs using second classifications, output by the trained predictor MLMs. The method also includes generating an aggregated ranked list of classes based on third classifications output by the trained predictor MLMs and second confidences output by the trained confidence MLMs. The method also includes training an ensemble confidence MLM using the aggregated ranked list of classes to generate a trained ensemble confidence MLM. The trained ensemble confidence MLM is trained to predict a corresponding selected classification for each corresponding data item in a training data set containing second data items similar to the first data items.

Abstract: A method including training predictor machine learning models (MLMs) using a first data set. The trained predictor MLMs are trained to predict classifications of data items in the first data set. The method also includes training confidence MLMs using second classifications, output by the trained predictor MLMs. The method also includes generating an aggregated ranked list of classes based on third classifications output by the trained predictor MLMs and second confidences output by the trained confidence MLMs. The method also includes training an ensemble confidence MLM using the aggregated ranked list of classes to generate a trained ensemble confidence MLM. The trained ensemble confidence MLM is trained to predict a corresponding selected classification for each corresponding data item in a training data set containing second data items similar to the first data items.

Abstract: A method and system assist users of an electronic bookkeeping system to sort financial transactions into accounts representing bookkeeping categories. The method and system generate vectors representing the accounts and the merchants known to the bookkeeping system. The method and system generate compressed versions of the vectors by compressing the vectors. The method and system assist users to sort the financial transactions by analyzing the compressed vectors.

Abstract: A method and system assists users of an electronic bookkeeping system to sort financial transactions into accounts representing bookkeeping categories. The method and system generates vectors representing the accounts and the merchants known to the bookkeeping system. The method and system generates compressed versions of the vectors by compressing the vectors. The method and system assists users to sort the financial transactions by analyzing the compressed vectors.

Abstract: Systems and methods for authenticating a user are disclosed.

Abstract: A method and system authenticates a user of a data management system. The method and system store an identification tag in the transaction description for a transaction between the user and the data management system. When the user attempts to access the data management system at a later date, the method and system requests that the user provide access to the transaction description. If the user is able to provide access to a transaction description that includes the identification tag, then the method and system can authenticate the user.

Abstract: A method may include generating a source transaction description, encoding, by an encoder model of a machine translation model executing on a computer processor, the source transaction description to create a context vector, decoding, by a decoder model of the machine translation model, the context vector to predict a target entity description, generating a transaction including the target entity description, detecting an acceptance, by a user, of an action performed on the transaction, in response to detecting the acceptance, updating a translation accuracy metric for the target entity description, determining that the updated translation accuracy metric satisfies a translation accuracy criterion, and in response to determining that the updated translation accuracy metric satisfies the translation accuracy criterion, adding the target entity description to golden entity descriptions.

Abstract: A method including training predictor machine learning models (MLMs) using a first data set. The trained predictor MLMs are trained to predict classifications of data items in the first data set. The method also includes training confidence MLMs using second classifications, output by the trained predictor MLMs. The method also includes generating an aggregated ranked list of classes based on third classifications output by the trained predictor MLMs and second confidences output by the trained confidence MLMs. The method also includes training an ensemble confidence MLM using the aggregated ranked list of classes to generate a trained ensemble confidence MLM. The trained ensemble confidence MLM is trained to predict a corresponding selected classification for each corresponding data item in a training data set containing second data items similar to the first data items.

Abstract: Embodiments disclosed herein relate to a method of writing a transaction to a data store having one or more data tables. An application agent receives transaction data for a first transaction of a first type. Application agent attempts to write the first transaction of the first type to a data table in the data store, provided that a transaction of the first type has been committed to the data store. Application agent scans a plurality of additional data stores for previously committed transactions of the first type Application agent determines additional transactions of another type that are co-located in a data table. Application agent ranks the additional transactions of the other type. Application agent identifies a data table in the data store to which a top ranking additional transaction of the other type has been committed. Application agent writes the first transaction to identified data table in the data store.

Abstract: A system and method for use with a data management service provides automatic assistance of transaction data sorting to an account based on account name inferences. A table is generated from transactions that have been previously sorted to accounts, in which the table contains occurrence frequencies of associations between merchants and accounts. The occurrence frequencies of the table are utilized to analyze an unsorted transaction. The merchant associated with the unsorted transaction is matched to a merchant of the table. The occurrence frequencies of the accounts of the table associated with the matched merchant are determined. The accounts of the table are matched to accounts of the user's chart of accounts. A determined likelihood account is calculated for the unsorted transaction for account sorting.

Abstract: A method and system groups user accounts in a financial management system based on the similarities of the financial transactions associated with the accounts. The method and system groups merchants involved in the financial transactions based on how financial transactions involving the merchants are sorted into groups of merchants. The method and system group users based on how the users sort financial transactions into their accounts. The method and system assist users to sort future financial transactions based on the groups of accounts, the groups of merchants, and the groups of users.

Abstract: A method may include generating a source transaction description, encoding, by an encoder model of a machine translation model executing on a computer processor, the source transaction description to create a context vector, decoding, by a decoder model of the machine translation model, the context vector to predict a target entity description, generating a transaction including the target entity description, detecting an acceptance, by a user, of an action performed on the transaction, in response to detecting the acceptance, updating a translation accuracy metric for the target entity description, determining that the updated translation accuracy metric satisfies a translation accuracy criterion, and in response to determining that the updated translation accuracy metric satisfies the translation accuracy criterion, adding the target entity description to golden entity descriptions.

Abstract: A method and system identify characteristics of financial transaction description strings. The method and system trains a dictionary generation model with a machine learning process to classify financial transaction description strings. The dictionary generation model generates a dictionary that indicates key substrings from the financial transaction description strings that were most predictive in classifying the financial transaction description strings.

Abstract: A method may include generating a source transaction description, encoding, by an encoder model of a machine translation model executing on a computer processor, the source transaction description to create a context vector, decoding, by a decoder model of the machine translation model, the context vector to predict a target entity description, generating a transaction including the target entity description, detecting an acceptance, by a user, of an action performed on the transaction, in response to detecting the acceptance, updating a translation accuracy metric for the target entity description, determining that the updated translation accuracy metric satisfies a translation accuracy criterion, and in response to determining that the updated translation accuracy metric satisfies the translation accuracy criterion, adding the target entity description to golden entity descriptions.

Abstract: A method, system, and non-transitory computer readable medium for removing noise ngrams from transaction records. The method may include obtaining noise ngrams; ordering the noise ngrams based on frequency of occurrence; discarding a portion of the noise ngrams below a frequency threshold to obtain a higher frequency subset of the noise ngrams; obtaining a transaction record of interest; and identifying a portion of the higher frequency subset within the transaction record of interest. Identifying the portion of the higher frequency subset may include constructing a regular expression based on the higher frequency subset; constructing a finite state machine based on the regular expression; providing the transaction record of interest as an input to the finite state machine; and executing the finite state machine. The method may also include removing, based on the identification, the portion of the higher frequency subset from the transaction record of interest.

Abstract: A method and system detects and addresses financial account swaps in a financial management system. The method and system include obtaining financial transaction data related to financial transactions associated with a plurality of financial accounts of the user. The method and system include detecting, for each financial account, a change point in a characteristic distribution of the financial transactions of the financial account. The method and system include, if two or more of the change points correlate with each other and time, swapping the financial transactions between the two or more financial accounts until the change points disappear and determining that an account swap occurred in the financial data collection feeds associated with the financial accounts. The method and system include adjusting financial data collection feeds to repair the account swaps.

Abstract: A method and system groups user accounts in a financial management system based on the similarities of the financial transactions associated with the accounts. The financial management system receives financial transaction data related to a plurality of financial transactions of a plurality of users. Each of the financial transactions is associated with an account of a user. The financial management system generates account characteristics vector data including, for each account, an account characteristics vector based on the financial transactions associated with the account. The financial management system groups the accounts by performing grouping analysis on the account characteristics vectors.

Abstract: A method for presenting a verified rental payment history of a rental user. The method includes selecting verified rental payment history factors related to a rental user's rental history and verified accounts of the rental user. The method further includes receiving authentication credentials for the verified accounts and receiving payment history records from the verified accounts using the authentication credentials. The method further includes generating renter analytics using the verified rental payment history factors and the payment history records. The method further includes generating the verified rental payment history using renter analytics and delivering the verified rental payment history to a landlord user.