Abstract: Systems and methods for virtual and physical tracking of assets is provided. A system may receive, from a first node of multiple nodes, a request to perform a transaction of an asset between the first node and a second node of the nodes. Each node may be tokenized representations of a respective physical entity for managing assets. The system may generate a block. The system may transmit the block to each of the nodes. The system may receive, from each of the nodes, a respective message indicating validation of the transaction. The system may append the block onto a blockchain based on receiving the validation. The blockchain may include multiple blocks associated with respective transactions between nodes.

Abstract: Examples described herein relate to apparatuses and methods of detecting fraudulent activity at an automated teller machine (ATM) using a machine learning model. A method includes receiving ATM activity data indicative of one or more withdrawal transactions at one or more ATMs using a transaction card, receiving transaction data and ATM data, ingesting the transaction data and the ATM data, analyzing the ingested transaction data and the ingested ATM data using a machine learning model, determining that the ingested transaction data and the ingested ATM data indicate fraudulent activity using the machine learning model, and performing one or more remedial actions based on the determination of fraudulent activity using the machine learning model.

Abstract: Examples described herein relate to apparatuses and methods of detecting fraudulent activity at an automated teller machine (ATM) using a machine learning model. A method includes receiving ATM activity data indicative of one or more withdrawal transactions at one or more ATMs using a transaction card, receiving transaction data and ATM data, ingesting the transaction data and the ATM data, analyzing the ingested transaction data and the ingested ATM data using a machine learning model, determining that the ingested transaction data and the ingested ATM data indicate a likelihood of fraudulent activity using the machine learning model, and performing, using the machine learning model, one or more remedial actions based on the determined likelihood of fraudulent activity.

Abstract: An application can be converted to new programming language or framework. A source application written for a legacy framework is analyzed and divided into smaller segments of code. The smaller segments are evaluated for quality in view of best practices design for writing applications. A rubric is determined for each segment and compared to a best practice threshold. Segments can be evaluated for features that can be passed through a model. The model converts the features into new code adapted for target framework. Machine learning and rules databases are updated with details of the conversion.

Abstract: Various systems and methods for providing ATM screen personalization are described herein. A system includes a display; a processor subsystem; and a memory including instructions, which when executed by the processor subsystem, cause the processor subsystem to: receiving, at an ATM, an ATM configuration setting from a non-ATM channel, the ATM configuration setting set by a user of the ATM; and displaying a user interface on the display, the user interface configured with the ATM configuration setting.

Abstract: A method of managing customer sentiment includes: monitoring an interaction of a customer with customer interactive media; deriving a sentiment of the customer from the interaction of the customer with the customer interactive media; generating sentiment data for the customer; and updating customer data in a customer database with the sentiment data in real-time.

Abstract: Systems and methods for converting an application to new programming language or framework is provided. A source application written in a legacy framework is analyzed and divided into smaller segments of code. The smaller segments are evaluated for quality in view of best practices design for writing applications. A rubric is determined for each segment and compared to a best practice threshold. Segments can be evaluated for features that can be passed through a model. The model converts the features into code in the new programming language. Machine learning and rules databases are updated with details of the conversion.

Abstract: A method of managing customer sentiment includes: monitoring an interaction of a customer with customer interactive media; deriving a sentiment of the customer from the interaction of the customer with the customer interactive media; generating sentiment data for the customer; and updating customer data in a customer database with the sentiment data in real-time.

Abstract: Systems and methods for converting an application to new programming language or framework is provided. A source application written in a legacy framework is analyzed and divided into smaller segments of code. The smaller segments are evaluated for quality in view of best practices design for writing applications. A rubric is determined for each segment and compared to a best practice threshold. Segments can be evaluated for features that can be passed through a model. The model converts the features into code in the new programming language. Machine learning and rules databases are updated with details of the conversion.

Abstract: A method for creating a push notification at a call center includes: receiving a call from a caller device; receiving, from the caller device, caller identification information; receiving, from the caller device, an inquiry; generating an encrypted token including the caller identification information and the inquiry; and when an agent is available, sending a push notification to the caller device for connecting the caller to the agent, the push notification being associated with the encrypted token, and wherein the agent uses the caller identification information from the encrypted token when the agent is addressing the inquiry.

Abstract: A method for creating a push notification at a call center includes: receiving a call from a caller device; receiving, from the caller device, caller identification information; receiving, from the caller device, an inquiry; generating an encrypted token including the caller identification information and the inquiry; and when an agent is available, sending a push notification to the caller device for connecting the caller to the agent, the push notification being associated with the encrypted token, and wherein the agent uses the caller identification information from the encrypted token when the agent is addressing the inquiry.

Abstract: A method for creating a push notification at a call center includes: receiving a call from a caller device; receiving, from the caller device, caller identification information; receiving, from the caller device, an inquiry; generating an encrypted token including the caller identification information and the inquiry; and when an agent is available, sending a push notification to the caller device for connecting the caller to the agent, the push notification being associated with the encrypted token, and wherein the agent uses the caller identification information from the encrypted token when the agent is addressing the inquiry.

Abstract: A method of managing customer sentiment includes: monitoring an interaction of a customer with customer interactive media; deriving a sentiment of the customer from the interaction of the customer with the customer interactive media; generating sentiment data for the customer; and updating customer data in a customer database with the sentiment data in real-time.

Abstract: A method for creating a push notification at a call center includes: receiving a call from a caller device; receiving, from the caller device, caller identification information; receiving, from the caller device, an inquiry; generating an encrypted token including the caller identification information and the inquiry; and when an agent is available, sending a push notification to the caller device for connecting the caller to the agent, the push notification being associated with the encrypted token, and wherein the agent uses the caller identification information from the encrypted token when the agent is addressing the inquiry.