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 deposit item processing using cognitive automation tools. In some embodiments, a computing platform may receive, from a deposit item input support server, image data associated with a deposit item. Subsequently, the computing platform may apply a machine learning classification model to the image data associated with the deposit item. In doing so, the computing platform may produce one or more predicted values for one or more fields of the deposit item and one or more confidence scores for the one or more predicted values. Based on these predicted values and confidence scores, the computing platform may generate and send one or more commands directing the deposit item input support server to accept or reject the deposit item, which in turn may cause the deposit item input support server to accept or reject the deposit item.

Abstract: A device includes a memory configured to store prioritization thresholds. The prioritization thresholds include one or both of threshold values and threshold ranges associated with an age and an amount of exceptions. A processor of the device receives exceptions associated with operation of at least a portion of the plurality of ATMs. For each exception of the plurality of exceptions, the processor determines, based on a comparison of an age property of the exception to an age threshold of the prioritization thresholds, an age score for the exception. The processor determines, based on a comparison of an amount property of the exception to an amount threshold of the prioritization thresholds, an amount score for the exception. Based on the age and amount scores, a prioritization score is determined for the exception which corresponds to a measure of an order in which action should be taken to resolve the exception.

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 deposit item processing using cognitive automation tools. In some embodiments, a computing platform may receive, from a deposit item input support server, image data associated with a deposit item. Subsequently, the computing platform may apply a machine learning classification model to the image data associated with the deposit item. In doing so, the computing platform may produce one or more predicted values for one or more fields of the deposit item and one or more confidence scores for the one or more predicted values. Based on these predicted values and confidence scores, the computing platform may generate and send one or more commands directing the deposit item input support server to accept or reject the deposit item, which in turn may cause the deposit item input support server to accept or reject the deposit item.

Abstract: Aspects of the disclosure relate to live reporting of check image keying issues. A computing platform may receive a first dataset and may transmit a first set of instructions for triggering transaction balancing and the first dataset to a first client computer system. Subsequently, the computing platform may receive from the first client computer system, a request to report an issue. Responsive to receiving the request to report an issue, the computing platform may receive from the first client computer a second dataset relating to the issue. Subsequently, the computing platform may generate, based on the first dataset and second dataset, an issue record, and may store the issue record in a second database. Then, the computing platform may transmit, via the communication interface, a second set of instructions for triggering issue investigation and the issue record relating to the issue to a second client computer system.

Abstract: Aspects of the disclosure relate to live reporting of check image keying issues. A computing platform may receive a first dataset and may transmit a first set of instructions for triggering transaction balancing and the first dataset to a first client computer system. Subsequently, the computing platform may receive from the first client computer system, a request to report an issue. Responsive to receiving the request to report an issue, the computing platform may receive from the first client computer a second dataset relating to the issue. Subsequently, the computing platform may generate, based on the first dataset and second dataset, an issue record, and may store the issue record in a second database. Then, the computing platform may transmit, via the communication interface, a second set of instructions for triggering issue investigation and the issue record relating to the issue to a second client computer system.

Abstract: A network interface receives transaction information. A processor determines private information and public information from the received transaction information. The network interface communicates the private information to an obfuscation engine, and receives obfuscated private information. The processor then merges the obfuscated private information with the public information to create a temporary identifier, and then converts the temporary identifier into a transaction identifier. A printer then prints a portion of the public information and the transaction identifier on a transaction receipt.

Abstract: A network interface receives transaction information. A processor determines private information and public information from the received transaction information. The network interface communicates the private information to an obfuscation engine, and receives obfuscated private information. The processor then merges the obfuscated private information with the public information to create a temporary identifier, and then converts the temporary identifier into a transaction identifier. A printer then prints a portion of the public information and the transaction identifier on a transaction receipt.

Abstract: A network interface receives transaction information. A processor determines private information and public information from the received transaction information. The network interface communicates the private information to an obfuscation engine, and receives obfuscated private information. The processor then merges the obfuscated private information with the public information to create a temporary identifier, and then converts the temporary identifier into a transaction identifier. A printer then prints a portion of the public information and the transaction identifier on a transaction receipt.

Abstract: A network interface receives transaction information. A processor determines private information and public information from the received transaction information. The network interface communicates the private information to an obfuscation engine, and receives obfuscated private information. The processor then merges the obfuscated private information with the public information to create a temporary identifier, and then converts the temporary identifier into a transaction identifier. A printer then prints a portion of the public information and the transaction identifier on a transaction receipt.

Abstract: A network interface receives transaction information. A processor determines private information and public information from the received transaction information. The network interface communicates the private information to an obfuscation engine, and receives obfuscated private information. The processor then merges the obfuscated private information with the public information to create a temporary identifier, and then converts the temporary identifier into a transaction identifier. A printer then prints a portion of the public information and the transaction identifier on a transaction receipt.