Abstract: Provided is a system and methodology which enhances the readability of barcodes using machine learning techniques wherein a model is trained using exemplary data and the resulting model is used in production to increase the chances of successfully capturing the data in a barcode notwithstanding that the barcode may be of low quality. According to the teachings of the present invention, the system is trained using large quantities of synthetic personal information associated with barcodes. Once the system is placed into production, the trained model is applied as against newly identified barcodes to enhance the readability of the original barcode based on features associated with the trained model.

Abstract: The system and methodology of the present invention employs available data obtained in connection with previous transactions to create one or more databases comprising feature sets which are used in transaction decision making solutions. The available data from previous transactions which is used in creating feature sets may include all available production data or the data may be stratified across specific industries and/or across specific decision support customers to optimize the expected decision making results. A feature engine is provided which uses a combination of data, time and combinational aggregate functions to feature engineer one or more feature sets used for one or more purposes, such purposes to include identity verification, fraud assessment, document verification as well as other assessments related to selectively permit or not permit transactions to proceed.

Abstract: Tools, strategies, and techniques are provided for evaluating the identities of different entities to protect business enterprises, consumers, and other entities from fraud by combining biometric activity data with facial recognition data for end users. Risks associated with various entities can be analyzed and assessed based on a combination of user liveliness check data, facial image data, social network data, and/or professional network data, among other data sources. In various embodiments, the risk assessment may include calculating an authorization score or authenticity score based on different portions or combinations of the collected and processed data.

Abstract: Provided are methodology and system countering fraudulent document and/or image use when authentication of a transaction based on a given document or image use is required. Additionally provided is a manner of machine learning adapting the methodology for implementation thereof.

Abstract: Provided are a system and methodology for iteratively measuring data, as between multiple sets thereof, that accounts for underlying data generation sources and bases. Doing so, via normalization of the data, enables uniformity of interpretation and presentation of the data no matter the machine learning model that produced the data.

Abstract: Provided are a system and methodology for iteratively measuring data, as between multiple sets thereof, that accounts for underlying data generation sources and bases. Doing so, via normalization of the data, enables uniformity of interpretation and presentation of the data no matter the machine learning model that produced the data.

Abstract: Tools, strategies, and techniques are provided for evaluating the identities of different entities to protect individual consumers, business enterprises, and other organizations from identity theft and fraud. Risks associated with various entities can be analyzed and assessed based on analysis of social network data, professional network data, or other networking connections, among other data sources. In various embodiments, the risk assessment may include calculating an authenticity score based on the collected network data.

Abstract: Provided are a system and methodology for iteratively transforming data, as between multiple sets thereof, that account for underlying data generation sources and bases. Doing so, via normalization of the data, enables uniformity of interpretation and presentation of the data no matter the machine learning model that produced the data.

Abstract: Provided are a system and methodology for iteratively transforming data, as between multiple sets thereof, that account for underlying data generation sources and bases. Doing so, via normalization of the data, enables uniformity of interpretation and presentation of the data no matter the machine learning model that produced the data.

Abstract: Provided are a system and methodology for iteratively transforming data as between multiple sets thereof. Doing so, via normalization of the data, enables uniformity of interpretation and presentation of the data no matter the machine learning model that produced the data.

Abstract: The system and methodology of the present invention uses feedback data received as a result of previous binary decisions made and uses an applicable score as well as other data, in some cases, to update and optimize a rules base such that scoring is incrementally improved over time as more and more feedback data is provided to the system.

Abstract: Tools, strategies, and techniques are provided for evaluating the identities of different entities to protect business enterprises, consumers, and other entities from fraud by combining biometric activity data with facial recognition data for end users. Risks associated with various entities can be analyzed and assessed based on a combination of user liveliness check data, facial image data, social network data, and/or professional network data, among other data sources. In various embodiments, the risk assessment may include calculating an authorization score or authenticity score based on different portions or combinations of the collected and processed data.

Abstract: The system and methodology of the present invention employs novel machine learning techniques in order to periodically update and supplement a set of identity verification databases used in connection with KYC determinations. The system and methodology of the present invention periodically receives updated identity data from third-party sources and may also use customer logic input provided to update the model used in making KYC determinations. The system of the present invention further updates and optimizes the identity verification databases and selectively deploys them in a production environment without requiring any human intervention, such that identity verification is incrementally improved over time as more identity data is provided to the system.

Abstract: Tools, strategies, and techniques are provided for evaluating the identities of different entities to protect business enterprises, consumers, and other entities from fraud by combining biometric activity data with facial recognition data for end users. Risks associated with various entities can be analyzed and assessed based on a combination of user liveliness check data, facial image data, social network data, and/or professional network data, among other data sources. In various embodiments, the risk assessment may include calculating an authorization score or authenticity score based on different portions or combinations of the collected and processed data.

Abstract: Tools, strategies, and techniques are provided for evaluating the identities of different entities to protect individual consumers, business enterprises, and other organizations from identity theft and fraud. Risks associated with various entities can be analyzed and assessed based on analysis of social network data, professional network data, or other networking connections, among other data sources. In various embodiments, the risk assessment may include calculating an authenticity score based on the collected network data.

Abstract: Tools, strategies, and techniques are provided for evaluating the identities of different entities to protect individual consumers, business enterprises, and other organizations from identity theft and fraud. Risks associated with various entities can be analyzed and assessed based on analysis of social network data, professional network data, or other networking connections, among other data sources. In various embodiments, the risk assessment may include calculating an authenticity score based on the collected network data.

Abstract: Tools, strategies, and techniques are provided for evaluating the identities of different entities to protect individual consumers, business enterprises, and other organizations from identity theft and fraud. Risks associated with various entities can be analyzed and assessed based on analysis of social network data, professional network data, or other networking connections, among other data sources. In various embodiments, the risk assessment may include calculating an authenticity score based on the collected network data.

Abstract: Tools, strategies, and techniques are provided for evaluating the identities of different entities to protect individual consumers, business enterprises, and other organizations from identity theft and fraud. Risks associated with various entities can be analyzed and assessed based on analysis of social network data, professional network data, or other networking connections, among other data sources. In various embodiments, the risk assessment may include calculating an authenticity score based on the collected network data.

Abstract: Tools, strategies, and techniques are provided for evaluating the identities of different entities to protect business enterprises, consumers, and other entities from fraud by combining biometric activity data with facial recognition data for end users. Risks associated with various entities can be analyzed and assessed based on a combination of user liveliness check data, facial image data, social network data, and/or professional network data, among other data sources. In various embodiments, the risk assessment may include calculating an authorization score or authenticity score based on different portions or combinations of the collected and processed data.

Abstract: Tools, strategies, and techniques are provided for evaluating the identities of different entities to protect individual consumers, business enterprises, and other organizations from identity theft and fraud. Risks associated with various entities can be analyzed and assessed based on analysis of social network data, professional network data, or other networking connections, among other data sources. In various embodiments, the risk assessment may include calculating an authenticity score based on the collected network data.