Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for expedited processing of image data obtained through optical character recognition (OCR) processing. In some embodiments, the method includes receiving a first image file obtained by an image capture device; performing OCR processing on the first image file to generate an OCR data file, where the OCR data file includes a confidence score; based on the confidence score, identifying a settlement rail compatible with the OCR data file; converting the OCR data file to a format associated with the identified settlement rail; and processing the converted OCR data file over the identified settlement rail.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for enhanced accuracy of image data obtained through optical character recognition (OCR) processing. In some embodiments, the method includes receiving a first image file obtained by an image capture device; performing OCR on the first image file to generate an OCR data file; inputting the OCR data file into at least two machine learning engines, where the at least two machine learning engines are configured to operate in parallel; based on an output of the at least two machine learning engines, generating a confidence score associated with the OCR data file; and based on the confidence score, processing the OCR data file over a real-time settlement rail.

Abstract: Aspects of the disclosure relate to performing enhanced exception processing using cognitive automation tools. In some embodiments, a computing platform may receive interaction data identifying one or more actions performed by one or more users in resolving a plurality of exception items associated with an exception queue. Subsequently, the computing platform may train, using a learning engine, a machine learning model to resolve a first exception and a second exception of one or more exceptions based on the interaction data. Based on training the machine learning model, the computing platform may generate one or more configuration commands directing a processing module to implement the machine learning model to process additional exception items associated with the exception queue. The computing platform then may send the one or more configuration commands to the processing module.

Abstract: Aspects of the disclosure relate to performing enhanced exception processing using cognitive automation tools. In some embodiments, a computing platform may receive interaction data identifying one or more actions performed by one or more users in resolving a plurality of exception items associated with an exception queue. Subsequently, the computing platform may train, using a learning engine, a machine learning model to resolve a first exception and a second exception of one or more exceptions based on the interaction data. Based on training the machine learning model, the computing platform may generate one or more configuration commands directing a processing module to implement the machine learning model to process additional exception items associated with the exception queue. The computing platform then may send the one or more configuration commands to the processing module.

Abstract: The present invention provides systems, methods, and computer program products for performing validation and clearance at a transaction item level for transaction received in a batch format. Each transaction of the batch is stored separately in a storage system, such as a logical database. Typically, image data and electronic data for a transaction are stored separately in the storage system and may be linked to each other. Each transaction is separately accessible and/or updateable. Each transaction stored in the storage system is separately analyzed to validate that the transaction is at least balanced. Transactions that determined to be balanced are cleared irrespective of a balance status associated with other transactions associated with the batch of transactions, such that transactions are individually cleared as opposed to being cleared in a batch format.

Abstract: The present invention provides systems, methods, and computer program products for performing validation and clearance at a transaction item level for transaction received in a batch format. Each transaction of the batch is stored separately in a storage system, such as a logical database. Typically, image data and electronic data for a transaction are stored separately in the storage system and may be linked to each other. Each transaction is separately accessible and/or updateable. Each transaction stored in the storage system is separately analyzed to validate that the transaction is at least balanced. Transactions that determined to be balanced are cleared irrespective of a balance status associated with other transactions associated with the batch of transactions, such that transactions are individually cleared as opposed to being cleared in a batch format.

Abstract: A method and system for enhanced check image privacy are disclosed. Embodiments of the present invention provide a way to automatically link the results from quality assurance software to a check image archive to appropriately mark images as unretrievable by customers. Images are interrogated with a data matching algorithm to determine whether a confidence score expressing a likelihood that the image matches associated, stored magnetic ink character recognition (MICR) data is below a pre-set threshold. The image can be then automatically designated in the financial document archive. The image can be displayed to an operator for analysis when the confidence score is above the pre-set threshold and below a pre-set limit, or in cases where the confidence score cannot be determined by the data matching algorithm.