Abstract: Aspects of the disclosure relate to voiceprint tracking and anomaly detection. A computing platform may detect voice information from a call management system. The computing platform may establish voiceprints for employees and clients of an enterprise. The computing platform may detect a call between an employee and a caller attempting to access a client account. The computing platform may identify a first voiceprint corresponding to the employee and a second voiceprint corresponding to the caller. The computing platform may compare the second voiceprint to a known voiceprint corresponding to the client. Based on the comparison of the second voiceprint to the known voiceprint, the computing platform may determine that the second voiceprint does not match the known voiceprint. The computing platform may identify that the second voiceprint corresponds to another employee of the enterprise, and may send a security notification indicating potential unauthorized account access to an enterprise computing device.

Abstract: Aspects of the disclosure relate to information masking. A user device may receive a request to access information that includes personal identifiable information (PII) and retrieve source data comprising the PII. The user device may mask, within the source data and based on a data management policy, the PII, resulting in masked information. The user device may display the masked information. The user device may receive a request to unmask the masked information and unmask the PII, resulting in unmasked PII. The user device may display the unmasked PII and send unmasking event information to a PII footprint modeling platform, which may cause the PIT footprint modeling platform to: log the request to unmask the masked information in an unmasking event log, 2) apply a machine learning model to the unmasking event log to identify malicious events, and 3) trigger remediation actions based on identification of the malicious events.

Abstract: Aspects of the disclosure relate to information masking. A user device may receive a request to access information that includes personal identifiable information (PII) and retrieve source data comprising the PII. The user device may mask, within the source data and based on a data management policy, the PII, resulting in masked information. The user device may display the masked information. The user device may receive a request to unmask the masked information and unmask the PII, resulting in unmasked PII. The user device may display the unmasked PII and send unmasking event information to a PII footprint modeling platform, which may cause the PII footprint modeling platform to: log the request to unmask the masked information in an unmasking event log, 2) apply a machine learning model to the unmasking event log to identify malicious events, and 3) trigger remediation actions based on identification of the malicious events.

Abstract: Aspects of the disclosure relate to information masking. A computing platform may receive, from a user computing device, a request to access information that includes personal identifiable information (PII). The computing platform may retrieve source data comprising the PII and mask, within the source data and based on a data management policy, the PII. The computing platform may send the masked information in response to the request to access the information. The computing platform may receive a request to unmask the masked information and unmask the PII. The computing platform may log the request to unmask the masked information in an unmasking event log and send the unmasked PII in response to the request to unmask the masked information. The computing platform may apply a machine learning model to the unmasking event log to identify malicious events and trigger remediation actions based on identification of the malicious events.

Abstract: Systems for dynamically detecting and controlling unauthorized activity are presented. In some examples, a request for a withdrawal may be received by a self-service kiosk. A plurality of bills may be transferred from a storage area to a dispensing device of the self-service kiosk. In some examples, the plurality of bills may be scanned to capture data associated with each bill. This data may be stored in a bill identification database. An error notification may be received by the self-service kiosk from a user. In response, bills within the self-service kiosk may be scanned to identify unique identifiers associated with the bills in the self-service kiosk. If the bills identified as dispensed are present, an error or malfunction has occurred and a user account may be credited. If the bills are not present, the activity may be identified as unauthorized activity and one or more mitigating actions may be executed.

Abstract: Aspects of the disclosure relate to information masking. A computing platform may receive, from a user computing device, a request to access information that includes personal identifiable information (PII). The computing platform may retrieve source data comprising the PII and mask, within the source data and based on a data management policy, the PII. The computing platform may send the masked information in response to the request to access the information. The computing platform may receive a request to unmask the masked information and unmask the PII. The computing platform may log the request to unmask the masked information in an unmasking event log and send the unmasked PII in response to the request to unmask the masked information. The computing platform may apply a machine learning model to the unmasking event log to identify malicious events and trigger remediation actions based on identification of the malicious events.

Abstract: Aspects of the disclosure relate to voiceprint tracking and anomaly detection. A computing platform may detect voice information from a call management system. The computing platform may establish voiceprints for employees and clients of an enterprise. The computing platform may detect a call between an employee and a caller attempting to access a client account. The computing platform may identify a first voiceprint corresponding to the employee and a second voiceprint corresponding to the caller. The computing platform may compare the second voiceprint to a known voiceprint corresponding to the client. Based on the comparison of the second voiceprint to the known voiceprint, the computing platform may determine that the second voiceprint does not match the known voiceprint. The computing platform may identify that the second voiceprint corresponds to another employee of the enterprise, and may send a security notification indicating potential unauthorized account access to an enterprise computing device.

Abstract: Aspects of the disclosure relate to information masking. A computing platform may receive, from a user computing device, a request to access information that includes personal identifiable information (PII). The computing platform may retrieve source data comprising the PII and mask, within the source data and based on a data management policy, the PII. The computing platform may send the masked information in response to the request to access the information. The computing platform may receive a request to unmask the masked information and unmask the PII. The computing platform may log the request to unmask the masked information in an unmasking event log and send the unmasked PII in response to the request to unmask the masked information. The computing platform may apply a machine learning model to the unmasking event log to identify malicious events and trigger remediation actions based on identification of the malicious events.

Abstract: Aspects of the disclosure relate to voiceprint tracking and anomaly detection. A computing platform may detect voice information from a call management system. The computing platform may establish voiceprints for employees and clients of an enterprise. The computing platform may detect a call between an employee and a caller attempting to access a client account. The computing platform may identify a first voiceprint corresponding to the employee and a second voiceprint corresponding to the caller. The computing platform may compare the second voiceprint to a known voiceprint corresponding to the client. Based on the comparison of the second voiceprint to the known voiceprint, the computing platform may determine that the second voiceprint does not match the known voiceprint. The computing platform may identify that the second voiceprint corresponds to another employee of the enterprise, and may send a security notification indicating potential unauthorized account access to an enterprise computing device.

Abstract: Aspects of the disclosure relate to information masking. A user device may receive a request to access information that includes personal identifiable information (PII) and retrieve source data comprising the PII. The user device may mask, within the source data and based on a data management policy, the PII, resulting in masked information. The user device may display the masked information. The user device may receive a request to unmask the masked information and unmask the PII, resulting in unmasked PII. The user device may display the unmasked PII and send unmasking event information to a PII footprint modeling platform, which may cause the PII footprint modeling platform to: log the request to unmask the masked information in an unmasking event log, 2) apply a machine learning model to the unmasking event log to identify malicious events, and 3) trigger remediation actions based on identification of the malicious events.

Abstract: Aspects of the disclosure relate to information masking. A computing platform may receive, from a user computing device, a request to access information that includes personal identifiable information (PII). The computing platform may retrieve source data comprising the PII and mask, within the source data and based on a data management policy, the PII. The computing platform may send the masked information in response to the request to access the information. The computing platform may receive a request to unmask the masked information and unmask the PII. The computing platform may log the request to unmask the masked information in an unmasking event log and send the unmasked PII in response to the request to unmask the masked information. The computing platform may apply a machine learning model to the unmasking event log to identify malicious events and trigger remediation actions based on identification of the malicious events.

Abstract: Aspects of the disclosure relate to voiceprint tracking and anomaly detection. A computing platform may detect voice information from a call management system. The computing platform may establish voiceprints for employees and clients of an enterprise. The computing platform may detect a call between an employee and a caller attempting to access a client account. The computing platform may identify a first voiceprint corresponding to the employee and a second voiceprint corresponding to the caller. The computing platform may compare the second voiceprint to a known voiceprint corresponding to the client. Based on the comparison of the second voiceprint to the known voiceprint, the computing platform may determine that the second voiceprint does not match the known voiceprint. The computing platform may identify that the second voiceprint corresponds to another employee of the enterprise, and may send a security notification indicating potential unauthorized account access to an enterprise computing device.

Abstract: Systems for dynamically detecting and controlling unauthorized activity are presented. In some examples, a request for a withdrawal may be received by a self-service kiosk. A plurality of bills may be transferred from a storage area to a dispensing device of the self-service kiosk. In some examples, the plurality of bills may be scanned to capture data associated with each bill. This data may be stored in a bill identification database. An error notification may be received by the self-service kiosk from a user. In response, bills within the self-service kiosk may be scanned to identify unique identifiers associated with the bills in the self-service kiosk. If the bills identified as dispensed are present, an error or malfunction has occurred and a user account may be credited. If the bills are not present, the activity may be identified as unauthorized activity and one or more mitigating actions may be executed.

Abstract: Systems for dynamically detecting and controlling unauthorized activity are presented. In some examples, a request for deposit may be received by a self-service kiosk. One or more items may be submitted for deposit and may be evaluated. In some examples, evaluating the one or more items for deposit may include evaluating the items to determine whether they include metal or other material not associated with valid items for deposit, evaluating security features such as watermarks, embedded threads, ink colors, and the like, to determine whether they correspond to valid items for deposit, and the like. If the evaluated items are valid items for deposit, the deposit may be processed. If not, one or more mitigating actions may be executed to capture additional information, lock a user account, or the like.

Abstract: Systems for dynamically detecting and controlling unauthorized activity are presented. In some examples, a request for a withdrawal may be received by a self-service kiosk. A plurality of bills may be transferred from a storage area to a dispensing device of the self-service kiosk. In some examples, the plurality of bills may be scanned to capture data associated with each bill. This data may be stored in a bill identification database. An error notification may be received by the self-service kiosk from a user. In response, bills within the self-service kiosk may be scanned to identify unique identifiers associated with the bills in the self-service kiosk. If the bills identified as dispensed are present, an error or malfunction has occurred and a user account may be credited. If the bills are not present, the activity may be identified as unauthorized activity and one or more mitigating actions may be executed.

Abstract: Systems for dynamically detecting and controlling unauthorized activity are presented. In some examples, a request for a withdrawal may be received by a self-service kiosk. A plurality of bills may be transferred from a storage area to a dispensing device of the self-service kiosk. In some examples, the plurality of bills may be scanned to capture data associated with each bill. This data may be stored in a bill identification database. An error notification may be received by the self-service kiosk from a user. In response, bills within the self-service kiosk may be scanned to identify unique identifiers associated with the bills in the self-service kiosk. If the bills identified as dispensed are present, an error or malfunction has occurred and a user account may be credited. If the bills are not present, the activity may be identified as unauthorized activity and one or more mitigating actions may be executed.