Abstract: A system for monitoring financial threats includes: at least one central processing unit and system memory that causes the system to: receive information about a threat associated with a financial services device; identify a type of the threat; identify a location associated with the threat; and notify one or more customers associated with the financial services device based upon the type or the location of the threat.

Abstract: A method implemented on an augmented reality electronic device includes establishing a wireless connection with an automated teller machine (ATM). The AR electronic device is used to authenticate a user of the AR electronic device at the ATM. An initiation is permitted of a financial transaction through user interface functionality for the ATM that is displayed on the AR electronic device. Financial information for the financial transaction is displayed on a display screen of the AR electronic device, instead of displaying the financial information at the ATM.

Abstract: A method implemented on an augmented reality electronic device includes establishing a wireless connection with an automated teller machine (ATM). The AR electronic device is used to authenticate a user of the AR electronic device at the ATM. An initiation is permitted of a financial transaction through user interface functionality for the ATM that is displayed on the AR electronic device. Financial information for the financial transaction is displayed on a display screen of the AR electronic device, instead of displaying the financial information at the ATM.

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: An example method for displaying renovation options for a structure includes: receiving homeowner financial information; receiving a selection of a renovation option; receiving pricing information of the renovation option; generating a financial impact analysis based on the homeowner financial information and the pricing information; displaying a renovation view on an augmented reality device, the renovation view displaying the structure with a representation of the renovation option depicted thereon; and displaying a financial impact analysis view showing the financial impact analysis, including a renovation budget amount.

Abstract: A system for monitoring financial threats includes: at least one central processing unit and system memory that causes the system to: receive information about a threat associated with a financial services device; identify a type of the threat; identify a location associated with the threat; and notify one or more customers associated with the financial services device based upon the type or the location of the threat.

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: 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.