Abstract: Methods, systems, and computer programs are presented for reconciling a transaction against data in a database to identify the transaction parameters based on text descriptors provided for the transaction. One method includes an operation for identifying features for reconciling transactions of a first entity by a machine-learning program. The features include, at least, a description of the transaction, a name of a second entity in the transaction, a location of the second entity, and an account for the transaction. The machine-learning program is trained with training data that includes values of the features for previously reconciled transactions. A received first transaction includes a description, a date, and an amount. The first transaction is input for the machine-learning program, which generates one or more suggestions for reconciling the first transaction. Each suggestion includes the name of the second entity in the first transaction and an account.

Abstract: Methods, systems, and computer programs are presented for reconciling a transaction against data in a database to identify the transaction parameters based on text descriptors provided for the transaction. One method includes an operation for identifying features for reconciling transactions of a first entity by a machine-learning program. The features include, at least, a description of the transaction, a name of a second entity in the transaction, a location of the second entity, and an account for the transaction. The machine-learning program is trained with training data that includes values of the features for previously reconciled transactions. A received first transaction includes a description, a date, and an amount. The first transaction is input for the machine-learning program, which generates one or more suggestions for reconciling the first transaction. Each suggestion includes the name of the second entity in the first transaction and an account.

Abstract: Methods, systems, and computer programs are presented for reconciling a transaction against data in a database to identify the transaction parameters based on text descriptors provided for the transaction. One method includes an operation for identifying features for reconciling transactions of a first entity by a machine-learning program. The features include, at least, a description of the transaction, a name of a second entity in the transaction, a location of the second entity, and an account for the transaction. The machine-learning program is trained with training data that includes values of the features for previously reconciled transactions. A received first transaction includes a description, a date, and an amount. The first transaction is input for the machine-learning program, which generates one or more suggestions for reconciling the first transaction. Each suggestion includes the name of the second entity in the first transaction and an account.

Abstract: Methods, systems, and computer programs are presented for reconciling a transaction against data in a database to identify the transaction parameters based on text descriptors provided for the transaction. One method includes an operation for identifying features for reconciling transactions of a first entity by a machine-learning program. The features include, at least, a description of the transaction, a name of a second entity in the transaction, a location of the second entity, and an account for the transaction. The machine-learning program is trained with training data that includes values of the features for previously reconciled transactions. A received first transaction includes a description, a date, and an amount. The first transaction is input for the machine-learning program, which generates one or more suggestions for reconciling the first transaction. Each suggestion includes the name of the second entity in the first transaction and an account.

Abstract: Methods, systems, and computer programs are presented for reconciling a transaction against data in a database to identify the transaction parameters based on text descriptors provided for the transaction. One method includes an operation for identifying features for reconciling transactions of a first entity by a machine-learning program. The features include, at least, a description of the transaction, a name of a second entity in the transaction, a location of the second entity, and an account for the transaction. The machine-learning program is trained with training data that includes values of the features for previously reconciled transactions. A received first transaction includes a description, a date, and an amount. The first transaction is input for the machine-learning program, which generates one or more suggestions for reconciling the first transaction. Each suggestion includes the name of the second entity in the first transaction and an account.

Abstract: Methods, systems, and computer programs are presented for reconciling a transaction against data in a database to identify the transaction parameters based on text descriptors provided for the transaction. One method includes an operation for identifying features for reconciling transactions of a first entity by a machine-learning program. The features include, at least, a description of the transaction, a name of a second entity in the transaction, a location of the second entity, and an account for the transaction. The machine-learning program is trained with training data that includes values of the features for previously reconciled transactions. A received first transaction includes a description, a date, and an amount. The first transaction is input for the machine-learning program, which generates one or more suggestions for reconciling the first transaction. Each suggestion includes the name of the second entity in the first transaction and an account.

Abstract: Methods, systems, and computer programs are presented for reconciling a transaction against data in a database to identify the transaction parameters based on text descriptors provided for the transaction. One method includes an operation for identifying features for reconciling transactions of a first entity by a machine-learning program. The features include, at least, a description of the transaction, a name of a second entity in the transaction, a location of the second entity, and an account for the transaction. The machine-learning program is trained with training data that includes values of the features for previously reconciled transactions. A received first transaction includes a description, a date, and an amount. The first transaction is input for the machine-learning program, which generates one or more suggestions for reconciling the first transaction. Each suggestion includes the name of the second entity in the first transaction and an account.