Abstract: A system and computer-implemented method of identifying multiple mortgage ready properties. The method includes receiving, via one or more processors, information from a customer approved for a mortgage about preferences for a real estate property and accessing, at a memory coupled to one or more processors, a memory storage location to retrieve a plurality of mortgage ready real estate properties meeting one or more of the customer preferences information. The method further includes transmitting, via the one or more processors, the plurality of real estate properties meeting one or more of the customer preferences information to the customer.

Abstract: A computer-implemented method of continuously updating information about a customer approved for a mortgage, the customer associated with a customer identification number. In one aspect, the method may include monitoring information accessed from a blockchain corresponding to the customer identification number, the information used to determine the customer is approved for a mortgage. In addition, the method may further include receiving new information about the customer, the new information used to determine the customer is approved for a mortgage. Further, the method may include updating a block of the blockchain to include the new information and recalculating the amount in which the customer is approved for a mortgage based upon the new information received.

Abstract: A method of using browsing activity to identify fraudulent online or virtual applications includes receiving a virtual application over one or more radio frequency links, determining an applicant name on the virtual application, determining an IP address of a source computer from which the virtual application originated, determining an online browsing or search history associated with the IP address, determining whether the online browsing or search history indicates recent Internet searches for the applicant name, and, in response to determining that the online browsing or search history does indicate recent Internet searches for the applicant name, flagging the virtual application as fraudulent and generating an electronic alert indicating that the virtual application is fraudulent to facilitate identifying fraudulent virtual applications for goods or services.

Abstract: A method of detecting whether electronic fraud alerts are false positives includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that determines whether to generate an electronic fraud alert for the financial transaction based upon the data, and, when an electronic fraud alert is generated, inputting the data into a machine learning program trained to identify one or more facts indicated by the data. The method may also include determining whether the identified facts can be verified by customer data and, in response to determining that the facts can be verified, retrieving or receiving first customer data. The method may further include verifying that the electronic fraud alert is not a false positive based upon analysis of the first customer data, and transmitting the verified electronic fraud alert to a mobile device of the customer to alert the customer to fraudulent activity.

Abstract: A heuristic money laundering detection engine includes capabilities to collect an unstructured data set, such as a transaction record, and detect indications of money laundering activity. By detecting money laundering activity and feeding back indications of money laundering transactions, the heuristic algorithm may continue to learn and improve detection accuracy. Such indications may include correlations to sets of transaction activity among a number of financial accounts and past indications of money laundering activity. Indications of money laundering may allow generation of audit reports for reporting to regulatory authorities.

Abstract: A method of detecting whether electronic fraud alerts are false positives includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that determines whether to generate an electronic fraud alert for the financial transaction based upon the data, and, when an electronic fraud alert is generated, inputting the data into a machine learning program trained to identify one or more facts indicated by the data. The method may also include determining whether the identified facts can be verified by customer data and, in response to determining that the facts can be verified, retrieving or receiving first customer data. The method may further include verifying that the electronic fraud alert is not a false positive based upon analysis of the first customer data, and transmitting the verified electronic fraud alert to a mobile device of the customer to alert the customer to fraudulent activity.

Abstract: A method of reducing a future amount of electronic fraud alerts includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that generates an electronic fraud alert, transmitting the alert to a mobile device of a customer, and receiving from the mobile device customer feedback indicating that the alert was a false positive or otherwise erroneous. The method also includes inputting the data detailing the financial transaction into a machine learning program trained to (i) determine a reason why the false positive was generated, and (ii) then modify the rules-based engine to account for the reason why the false positive was generated, and to no longer generate electronic fraud alerts based upon (a) fact patterns similar to fact patterns of the financial transaction, or (b) data similar to the data detailing the financial transaction, to facilitate reducing an amount of future false positive fraud alerts.

Abstract: In a method of preventing fraudulent online financial transactions, a request to authorize an online, financial transaction may be received, where the transaction is associated with a debit or credit card account. A computing device at which information associated with the debit or credit card account was entered for the transaction may be identified, and a first geographic location at which the computing device resides may be determined. Based upon geolocation data indicating one or more geographic locations of the authorized cardholder, it may be determined that the authorized cardholder was at a second geographic location at a time of the transaction. If the second geographic location does not correspond to the first geographic location, the financial transaction may be prevented from being executed.

Abstract: A computer-implemented method of continuously updating information about a customer approved for a mortgage, the customer associated with a customer identification number. In one aspect, the method may include monitoring information accessed from a blockchain corresponding to the customer identification number, the information used to determine the customer is approved for a mortgage. In addition, the method may further include receiving new information about the customer, the new information used to determine the customer is approved for a mortgage. Further, the method may include updating a block of the blockchain to include the new information and recalculating the amount in which the customer is approved for a mortgage based upon the new information received.

Abstract: A method of using browsing activity to identify fraudulent online or virtual applications includes receiving a virtual application over one or more radio frequency links, determining an applicant name on the virtual application, determining an IP address of a source computer from which the virtual application originated, determining an online browsing or search history associated with the IP address, determining whether the online browsing or search history indicates recent Internet searches for the applicant name, and, in response to determining that the online browsing or search history does indicate recent Internet searches for the applicant name, flagging the virtual application as fraudulent and generating an electronic alert indicating that the virtual application is fraudulent to facilitate identifying fraudulent virtual applications for goods or services.

Abstract: A heuristic money laundering detection engine includes capabilities to collect an unstructured data set, such as a transaction record, and detect indications of money laundering activity. By detecting money laundering activity and feeding back indications of money laundering transactions, the heuristic algorithm may continue to learn and improve detection accuracy. Such indications may include correlations to sets of transaction activity among a number of financial accounts and past indications of money laundering activity. Indications of money laundering may allow generation of audit reports for reporting to regulatory authorities.

Abstract: In a method of preventing fraudulent online financial transactions, a request to authorize an online, financial transaction may be received, where the transaction is associated with a debit or credit card account. A computing device at which information associated with the debit or credit card account was entered for the transaction may be identified, and a first geographic location at which the computing device resides may be determined. Based upon geolocation data indicating one or more geographic locations of the authorized cardholder, it may be determined that the authorized cardholder was at a second geographic location at a time of the transaction. If the second geographic location does not correspond to the first geographic location, the financial transaction may be prevented from being executed.

Abstract: A method of detecting whether electronic fraud alerts are false positives includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that determines whether to generate an electronic fraud alert for the financial transaction based upon the data, and, when an electronic fraud alert is generated, inputting the data into a machine learning program trained to identify one or more facts indicated by the data. The method may also include determining whether the identified facts can be verified by customer data and, in response to determining that the facts can be verified, retrieving or receiving first customer data. The method may further include verifying that the electronic fraud alert is not a false positive based upon analysis of the first customer data, and transmitting the verified electronic fraud alert to a mobile device of the customer to alert the customer to fraudulent activity.

Abstract: A method of reducing a future amount of electronic fraud alerts includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that generates an electronic fraud alert, transmitting the alert to a mobile device of a customer, and receiving from the mobile device customer feedback indicating that the alert was a false positive or otherwise erroneous. The method also includes inputting the data detailing the financial transaction into a machine learning program trained to (i) determine a reason why the false positive was generated, and (ii) then modify the rules-based engine to account for the reason why the false positive was generated, and to no longer generate electronic fraud alerts based upon (a) fact patterns similar to fact patterns of the financial transaction, or (b) data similar to the data detailing the financial transaction, to facilitate reducing an amount of future false positive fraud alerts.

Abstract: Systems and methods are described for Distification of 3D imagery. A computing device may obtain a three dimensional (3D) image that defines a 3D point cloud used to generate a two dimensional (2D) image matrix. The 2D image matrix may include 2D matrix point(s), where each 2D matrix point can be associated with a horizontal coordinate and a vertical coordinate. The computing device can generate an output feature vector that includes at least one 2D matrix point of the 2D image matrix, and a 3D point in the 3D point cloud of the 3D image. The 3D point in the 3D point cloud is mapped to a coordinate pair comprised of the horizontal coordinate and the vertical coordinate of the at least one 2D matrix point of the 2D image matrix point. The output feature vector is input into a predictive model.

Abstract: A system and computer-implemented method for approving a dynamic mortgage application. In one aspect, the method may include determining a customer is approved for a mortgage and determining a real estate property is mortgage ready, including calculating an appraisal value for the real estate property based upon the information about the real property retrieved from a computer file or memory location/address (such as by using a machine learning or artificial intelligence algorithm). The method may still further include comparing the calculated amount the customer is approved for a mortgage loan with the calculated appraisal value of the real estate property; and approving the mortgage application of the customer for the real estate property when the calculated amount the customer is approved for the mortgage loan meets, or exceeds, the calculated appraisal value of the real estate property.

Abstract: A heuristic engine includes capabilities to collect an unstructured data set and detect instances of transaction fraud in a financial account. By providing a heuristic algorithm with unstructured transaction sets and indications of particular instances of transactions that correlate with past fraudulent activity allows prevention of future occurrences of fraud. Such heuristic algorithms may learn from past indications of fraudulent activity and improve accuracy of detection of future fraud detections.

Abstract: A method of reducing a future amount of electronic fraud alerts includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that generates an electronic fraud alert, transmitting the alert to a mobile device of a customer, and receiving from the mobile device customer feedback indicating that the alert was a false positive or otherwise erroneous. The method also includes inputting the data detailing the financial transaction into a machine learning program trained to (i) determine a reason why the false positive was generated, and (ii) then modify the rules-based engine to account for the reason why the false positive was generated, and to no longer generate electronic fraud alerts based upon (a) fact patterns similar to fact patterns of the financial transaction, or (b) data similar to the data detailing the financial transaction, to facilitate reducing an amount of future false positive fraud alerts.

Abstract: A heuristic engine includes capabilities to collect an unstructured data set and a current business context to calculate a credit worthiness score. Providing a heuristic algorithm, executing within the engine, with the data set and the context may allow determination of predicted future contexts and recommend subsequent actions, such as assessing a credit risk of a customer transaction and reducing the risk of customer transactions by processing the available data. Such heuristic algorithms may learn from past data transactions and appropriate correlations with events and available data.

Abstract: A heuristic engine includes capabilities to collect an unstructured data set and detect instances of transaction fraud in a financial account. By providing a heuristic algorithm with unstructured transaction sets and indications of particular instances of transactions that correlate with past fraudulent activity allows prevention of future occurrences of fraud. Such heuristic algorithms may learn from past indications of fraudulent activity and improve accuracy of detection of future fraud detections.