Abstract: Embodiments for extracting data for online account includes systems that receive at least one check images, apply one or more optical character recognition processes to at least a portion of the at least one check image, and convert image based data to text based data. The embodiments further identify check data in the text based data, identify online data from the check data, incorporate at least a portion of the online data into a portion of an online account, and provide the online account portion to a customer.

Abstract: Embodiments for extracting data for online account includes systems that receive at least one check images, apply one or more optical character recognition processes to at least a portion of the at least one check image, and convert image based data to text based data. The embodiments further identify check data in the text based data, identify online data from the check data, incorporate at least a portion of the online data into a portion of an online account, and provide the online account portion to a customer.

Abstract: Embodiments of the invention include systems, methods, and computer-program products for lifting metadata from financial documents to allow for automated exception processing. As such, allowing for automated decisions for exception processing to systematically be resolved base on matches between lifted metadata. The exceptions may include one or more irregularities such as bad micro line reads, outdated check stock, or misrepresentative checks that may result in a failure to match the check to an associated account for processing. As such, once an exception is identified during the processing the metadata lifted from the document with the exception may be utilized to search financial records at the financial institution to attempt to identify the correct version of the document. Subsequently, the system may correct the irregularity systematically and automatically.

Abstract: Embodiments of the invention include systems, methods, and computer-program products for lifting metadata from financial documents to allow for automated exception processing. As such, allowing for automated decisions for exception processing to systematically be resolved base on matches between lifted metadata. The exceptions may include one or more irregularities such as bad micro line reads, outdated check stork, or misrepresentative checks that may result in a failure to match the check to an associated account for processing. As such, once an exception is identified during the processing the metadata lifted from the document with the exception may be utilized to search financial records at the financial institution to attempt to identify the correct version of the document. Subsequently, the system may correct the irregularity systematically and automatically.

Abstract: Embodiments for extracting data for online account includes systems that receive at least one check images, apply one or more optical character recognition processes to at least a portion of the at least one check image, and convert image based data to text based data. The embodiments further identify check data in the text based data, identify online data from the check data, incorporate at least a portion of the online data into a portion of an online account, and provide the online account portion to a customer.

Abstract: Embodiments for extracting data for online account includes systems that receive at least one check images, apply one or more optical character recognition processes to at least a portion of the at least one check image, and convert image based data to text based data. The embodiments further identify check data in the text based data, identify online data from the check data, incorporate at least a portion of the online data into a portion of an online account, and provide the online account portion to a customer.

Abstract: Embodiments for extracting data for online account includes systems that receive at least one check images, apply one or more optical character recognition processes to at least a portion of the at least one check image, and convert image based data to text based data. The embodiments further identify check data in the text based data, identify online data from the check data, incorporate at least a portion of the online data into a portion of an online account, and provide the online account portion to a customer.

Abstract: Embodiments for identifying errors based on data extracted from financial record images includes systems that receive one or more financial record images from a user, apply an optical character recognition process to at least a portion of the one or more financial record images, and identify record data based on the applied optical character recognition process comprising at least a name of a party to the financial record. The systems further identify errors associated with the one or more financial record images based on the record data.

Abstract: Embodiments of the invention relate to systems, methods, and computer program products for extracting check data and using the check data for determining a disposition of a check. The system, method, and computer program product are configured to: a) receive an image of a check; b) apply an optical character recognition process to at least a portion of the image of the check; c) convert one or more image portions of the at least a portion of the image to text based data based at least partially on the application; d) identify a date of the check based on the text based data obtained from the one or more image portions; e) compare the date of the check to one or more authorization parameters; and f) provide a disposition of the check based on the comparison of the date of the check to the one or more authorization parameters.

Abstract: Embodiments of the invention include systems, methods, and computer-program products for lifting metadata from financial documents to allow for automated exception processing. As such, allowing for automated decisions for exception processing to systematically be resolved base on matches between lifted metadata. The exceptions may include one or more irregularities such as bad micro line reads, outdated check stork, or misrepresentative checks that may result in a failure to match the check to an associated account for processing. As such, once an exception is identified during the processing the metadata lifted from the document with the exception may be utilized to search financial records at the financial institution to attempt to identify the correct version of the document. Subsequently, the system may correct the irregularity systematically and automatically.

Abstract: Embodiments of the invention include systems, methods, and computer-program products for lifting metadata from financial documents to allow for automated exception processing. As such, allowing for automated decisions for exception processing to systematically be resolved base on matches between lifted metadata. The exceptions may include one or more irregularities such as bad micro line reads, outdated check stock, or misrepresentative checks that may result in a failure to match the check to an associated account for processing. As such, once an exception is identified during the processing the metadata lifted from the document with the exception may be utilized to search financial records at the financial institution to attempt to identify the correct version of the document. Subsequently, the system may correct the irregularity systematically and automatically.

Abstract: Embodiments of the invention include systems, methods, and computer-program products for lifting metadata from financial documents to allow for automated exception processing. As such, allowing for automated decisions for exception processing to systematically be resolved base on matches between lifted metadata. The exceptions may include one or more irregularities such as bad micro line reads, outdated check stork, or misrepresentative checks that may result in a failure to match the check to an associated account for processing. As such, once an exception is identified during the processing the metadata lifted from the document with the exception may be utilized to search financial records at the financial institution to attempt to identify the correct version of the document. Subsequently, the system may correct the irregularity systematically and automatically.

Abstract: Embodiments of the invention include systems, methods, and computer-program products for lifting metadata from financial documents for eliminating duplicate financial document processing. As such, when financial documents are received, the invention utilizes optical character recognition to lift data from the documents. This data is then stored in metadata form. When subsequent financial documents are received, the invention may compare the received documents with the stored metadata of previously received financial documents. Upon identifying a match between the metadata, the system will notify the user, notify a financial representative, and/or eliminate the matched document. A matching or corresponding document indicates a duplicate in the document. As such, elimination the document prior to financial document processing prevents multiplicative document processing identified, the system will stop the payment associated with that financial document.

Abstract: Disclosed is a system and associated method for categorizing a transaction performed using a negotiable instrument. The system is typically configured for receiving an indication of the transaction performed using the negotiable instrument, including receiving an image of the negotiable instrument; extracting transaction data from the image of the negotiable instrument, including extracting memo data from the image of the negotiable instrument; based at least in part on the extracted memo data, determining if the transaction is associated with a first predefined transaction category; and presenting at least a portion of the transaction data extracted from the image of the negotiable instrument to the customer, wherein, if the transaction is associated with the first predefined transaction category, presenting at least a portion of the extracted transaction data to the customer includes presenting an indication that the first predefined transaction category is associated with the transaction.

Abstract: Embodiments for identifying errors based on data extracted from financial record images includes systems that receive one or more financial record images from a user, apply an optical character recognition process to at least a portion of the one or more financial record images, and identify record data based on the applied optical character recognition process comprising at least a name of a party to the financial record. The systems further identify errors associated with the one or more financial record images based on the record data.

Abstract: Embodiments of the invention include systems, methods, and computer-program products for lifting metadata from financial documents to allow for automated exception processing. As such, allowing for automated decisions for exception processing to systematically be resolved base on matches between lifted metadata. The exceptions may include one or more irregularities such as bad micro line reads, outdated check stork, or misrepresentative checks that may result in a failure to match the check to an associated account for processing. As such, once an exception is identified during the processing the metadata lifted from the document with the exception may be utilized to search financial records at the financial institution to attempt to identify the correct version of the document. Subsequently, the system may correct the irregularity systematically and automatically.

Abstract: Embodiments for extracting data for ACH transactions includes systems that receive at least one check image, apply one or more optical character recognition processes to at least a portion of the at least one check image, and identify check data. The systems further insert at least a portion of the check data into an ACH transaction entry for ACH transaction processing.

Abstract: Embodiments of the invention include systems, methods, and computer-program products for lifting metadata from financial documents to allow for automated financial document stop payment requests. As such, allowing for automated determining of an appropriate financial document to stop payment on based on user information comparison to stored metadata for financial documents. As such, the user may provide information about the requested stop payment, such as a payee, date, amount, or the like associated with a payment. The system may then utilize the user provided information and compare that information to the metadata lifted from the financial documents. As such, identifying the financial document associated with the stop payment request. Once identified, the system will stop the payment associated with that financial document.

Abstract: Embodiments of the invention relate to systems, methods, and computer program products for extracting check data and using the check data for determining a disposition of a check. The system, method, and computer program product are configured to: a) receive an image of a check; b) apply an optical character recognition process to at least a portion of the image of the check; c) convert one or more image portions of the at least a portion of the image to text based data based at least partially on the application; d) identify a date of the check based on the text based data obtained from the one or more image portions; e) compare the date of the check to one or more authorization parameters; and f) provide a disposition of the check based on the comparison of the date of the check to the one or more authorization parameters.

Abstract: Embodiments for extracting data for online account includes systems that receive at least one check images, apply one or more optical character recognition processes to at least a portion of the at least one check image, and convert image based data to text based data. The embodiments further identify check data in the text based data, identify online data from the check data, incorporate at least a portion of the online data into a portion of an online account, and provide the online account portion to a customer.