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 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 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 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 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 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 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 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 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: In a computer-implemented method of facilitating detection of document-related fraud, fraudulent document detection rules may be generated or updated by training a machine learning program using image data corresponding to physical documents, and fraud determinations corresponding to the documents. The documents and fraudulent document detection rules may correspond to a first type of document. Image data corresponding to an image of one of the physical documents may be received, where the physical document corresponds to the first type of document. By applying the fraudulent document detection rules to the image data, it may be determined that the physical document is, or may be, fraudulent. An indication of whether the physical document is, or may be, fraudulent may be displayed to one or more people via one or more respective computing device user interfaces.

Abstract: A system and computer-implemented method of continuously updating information about one or more of a customer approved for a mortgage and a real estate property identified as mortgage ready. The method includes monitoring information accessed from a memory storage location corresponding to a customer identification number, the information used to determine the customer is approved for a mortgage, and receiving new information about the customer, the new information used to determine the customer is approved for a mortgage. The method also includes updating, at a memory coupled to the one or more processors, the memory storage location to include the new information. The method still further includes recalculating the amount in which the customer is approved for a mortgage based upon the new information received.

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: 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: In a computer-implemented method of using customer data to determine that geolocation-based fraud alerts are false positives, it may be determined that an electronic fraud alert is a geolocation-based alert generated based upon an unexpected or abnormal transaction location. In response, customer data may be obtained from two or more sources via radio frequency links. It may then be determined that the customer data from the sources indicates that a customer is traveling. In response, it may be determined that a customer location indicated by the customer data corresponds to the transaction location. In response to determining that the customer location corresponds to the transaction location, the electronic fraud alert may be marked as a false positive, and the electronic fraud alert may be prevented from being transmitted to a mobile device of the customer, in order to reduce an amount of false positives that are transmitted to customers.

Abstract: In a computer-implemented method of facilitating detection of document-related fraud, fraudulent document detection rules may be generated or updated by training a machine learning program using image data corresponding to physical documents, and fraud determinations corresponding to the documents. The documents and fraudulent document detection rules may correspond to a first type of document. Image data corresponding to an image of one of the physical documents may be received, where the physical document corresponds to the first type of document. By applying the fraudulent document detection rules to the image data, it may be determined that the physical document is, or may be, fraudulent. An indication of whether the physical document is, or may be, fraudulent may be displayed to one or more people via one or more respective computing device user interfaces.

Abstract: A heuristic engine includes capabilities to collect an unstructured data set, including document image data and correlate the image data with known valid data to detect document fraud. Notifications of fraud detection may include particular signature images, or characteristics of a document image. By feeding back indications of document fraud with correlations to past instances of fraud into the heuristic algorithm, the algorithm may learn and improve in fraud detection accuracy.

Abstract: A system and computer-implemented method of continuously updating information about one or more of a customer approved for a mortgage and a real estate property identified as mortgage ready. The method includes monitoring information accessed from a memory storage location corresponding to a customer identification number, the information used to determine the customer is approved for a mortgage, and receiving new information about the customer, the new information used to determine the customer is approved for a mortgage. The method also includes updating, at a memory coupled to the one or more processors, the memory storage location to include the new information. The method still further includes recalculating the amount in which the customer is approved for a mortgage based upon the new information received.

Abstract: A system and computer-implemented method for approving a dynamic mortgage application using a blockchain. In one aspect, the method may include determining a customer is approved for a mortgage (“mortgage ready”) and determining a real estate property is mortgage ready. The method may include comparing a calculated amount in which the customer is approved for a mortgage loan with a calculated appraisal value of the real estate property, and approving the mortgage application of the customer when the calculated amount the customer is approved for the mortgage loan is equal to, or exceeds, the calculated appraisal value of the real estate property, reducing a processing time and closing time of the mortgage.

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.