Systems and methods for generating assessments based on construction site images are provided. For example, image data captured from a construction site using at least one image sensor may be obtained. Further, at least one electronic record associated with the construction site may be obtained. The image data and the at least one electronic record may be analyzed to generate at least one assessment related to the construction site. For example, the image data may be analyzed to identify at least one discrepancy between the at least one electronic record and the construction site, and the identified at least one discrepancy may be used in the generation of the at least one assessment.

A system and methods are provided for using order descriptor identifiers in relation to orders being used in trading strategies. According to one example method, when a hedge order is submitted upon detecting a fill of another order, the hedge order includes one or more order descriptor identifiers conveying a purpose of the hedge order to a user. The order descriptor identifiers can be used to search for desired orders and perform more effective order management and post trade analysis.

A method for using machine learning techniques to analyze past decisions made by administrators concerning account opening requests and to recommend whether an account opening request should be allowed or denied. Further, the machine learning techniques determine various other products that the customer may be interested in and prioritizes the choices of options that the machine learning algorithm determines appropriate for the customer.

A method for using machine learning techniques to analyze past decisions made by administrators concerning account opening requests and to recommend whether an account opening request should be allowed or denied. Further, the machine learning techniques determine various other products that the customer may be interested in and prioritizes the choices of options that the machine learning algorithm determines appropriate for the customer.

A transaction method for a user using a first and a second terminal and connected to a server via a first and a second communication channel, respectively. The first terminal sends a transaction amount to the server. The server establishes a verification code whose length depends on the transaction amount, then sends a request to the first terminal and the verification code to the second terminal. The user returns the request filled in with a copied code to the server using the first terminal. The server compares the verification code with the copied code and sends a transaction validation or invalidation message to the first terminal depending on the comparison.

A transaction method for a user using a first and a second terminal and connected to a server via a first and a second communication channel, respectively. The first terminal sends a transaction amount to the server. The server establishes a verification code whose length depends on the transaction amount, then sends a request to the first terminal and the verification code to the second terminal. The user returns the request filled in with a copied code to the server using the first terminal. The server compares the verification code with the copied code and sends a transaction validation or invalidation message to the first terminal depending on the comparison.

There are provided systems and methods for identifying transaction processing retry attempts based on machine learning models for transaction success. A service provider, such as an electronic transaction processor for digital transactions, may detect a failure of data processing for a transaction when processed with a separate data processing system, such as a card processing system for payment cards. In order to minimize cost and wasted resources for retrying transactions that are likely to further fail, a machine learning model may be implements that generates a predictive score for whether a failed transaction is likely to be successful if retried with the data processing system. The predictive score may be used to predict a probability of success, which may then be used with a cost function to determine a cost to retry the failed transaction and a cost to stop a retry of the failed transaction.

There are provided systems and methods for identifying transaction processing retry attempts based on machine learning models for transaction success. A service provider, such as an electronic transaction processor for digital transactions, may detect a failure of data processing for a transaction when processed with a separate data processing system, such as a card processing system for payment cards. In order to minimize cost and wasted resources for retrying transactions that are likely to further fail, a machine learning model may be implements that generates a predictive score for whether a failed transaction is likely to be successful if retried with the data processing system. The predictive score may be used to predict a probability of success, which may then be used with a cost function to determine a cost to retry the failed transaction and a cost to stop a retry of the failed transaction.

A virtual payment system for paying for goods, services and content ordered over an internetwork is disclosed. The virtual payment system includes a commerce gateway. Buyers and sellers becomes registered participants by applying for virtual payment buyer and seller accounts. Once an account is established with the commerce gateway, a digital certificate is stored on the registered participant's computer. A buyer can then order a product, i.e., goods, services or content from a seller and charge it to the virtual payment account. When the product is shipped, the seller notifies the commerce gateway, which applies the charges to the buyer's virtual payment account. The buyer can settle the charges using a prepaid account, a credit account, or by using reward points earned through use of the virtual payment account. A buyer may create sub-accounts.

A computer-implemented method for initiating online collections that includes determining a current status of an account held by an account holder, determining a real-time financial profile of the account holder using information on the banking institution computer systems and third party information providers and displaying a customized status message based on the account holder's financial profile. The method provides remediation options to the account holder.