The present disclosure provides a system for disseminating sensitive information. The system includes a software application which comprises a mobile application installed on a mobile device that enables a user to capture images, via a camera provided by the mobile device, of one or more documents which can then be subsequently uploaded to a remote database. The software application may also include a web-based application. The software application comprises a document checklist, document validation module, and an external communication module. After the electronic files are uploaded to the remote database the user may utilize the document validation module to cross-reference certain personal information is consistently disclosed within each of the one or more documents. The external communication module is configured to allow the user to select one or more electronic files from the remote database and electronically transmit the selected files to another party via an electronic facsimile (E-Fax).

To manage property records using a multi-layered hybrid distributed ledger architecture, identification information for a particular property is obtained and transmitted to at least one participant in a public distributed ledger network for a public distributed ledger layer. Ownership information for the same particular property is obtained and transmitted to at least one participant in a federated distributed ledger network for a federated distributed ledger layer. Transaction-related documents for the same particular property are obtained and transmitted to at least one participant in a private distributed ledger network for a private distributed ledger. The distributed ledger layers are different layers of a property distributed ledger each having a separate set of consensus rules for appending distributed ledger data to the respective layer. This allows for the immutable preservation of royalty terms and conditions for a property or asset using the distributed ledger.

This disclosure describes, in part, techniques for automating transfers of funds from a deposit account of a user to a savings account of the user based on historical transaction data associated with the deposit account. Using historical deposits and withdrawals from the deposit account, a prediction may be made regarding deposits to, and withdrawals from, the deposit account that will occur in a future period. Based on that that information and a balance to maintain in the deposit account for the duration of the future period, the amount of funds to transfer from the deposit account to the savings account may be determined. The techniques may further include transferring the funds to the savings account once determined.

A system for generating commercial real estate tax appeal documents is provided. Financial statements about a property are submitted in various formats to a form generation device, which may collect property data from a variety of external sources. Machine learning models are executed to identify errors or inconsistencies in the data and provide an estimated valuation for the commercial property. Based on the estimated value and comparable sales data, the platform generates tax appeals documentation for user review and submission.

Methods, systems, devices, and tangible non-transitory computer readable media for resource provisioning based on risk scores. The disclosed technology can include accessing resource request data including information associated with a request for a resource from an entity associated with an organization. Organizational data associated with the entity can be accessed. The organizational data can include information associated with risk factors and previous resource allocations of the entity. Based on performance of risk evaluation operations on the organizational data, a risk score associated with provisioning the resource to the entity can be determined. A resource provisioning amount can be determined based on the risk score. The resource provisioning amount can include an amount of the resource authorized to be provisioned to the entity. Furthermore, output including indications associated with the resource provisioning amount can be generated.

Systems as described herein generate an outstanding check alert. An alert generating server may receive transaction records associated with a plurality of checking accounts. The alert generating server may user a first machine learning classifier to determine a transaction pattern indicating a merchant has failed to process outstanding checks for a period of time. The alert generating server may receive sequential check information comprising at least one missing check number associated with a particular checking account. The alert generating server may user a second machine learning classifier to determine at least one outstanding check associated with the particular checking account. The alert generating server may send an alert indicating the at least one outstanding check to a user device.

A method can be used to predict risk using machine learning models having efficient feature learning. A risk prediction model can be applied to time-series data associated with a target entity to generate a risk indicator. The risk prediction model can include a feature learning model for generating features from the time-series data. The risk prediction model can also include a risk classification model for generating the risk indicator. The feature learning model can include filters and can be trained. Parameters of the risk prediction model can be adjusted to minimize a loss function associated with risk indicators. An updated risk prediction model can be generated by removing a filter from an original set of filters based on influencing scores of the original filters. The risk indicator can be transmitted to a computing device for use in controlling access of the target entity to a computing environment.

The SECURE MULTI-SERVER INTEREST RATE BASED INSTRUMENT TRADING SYSTEM AND METHODS OF INCREASING EFFICIENCY THEREOF AND ASSOCIATED INTEREST RATE BASED FINANCIAL INSTRUMENT (“IRFI”) provides efficient, secure instrument transactions. The IRFI provides increased trading system security and efficiency for an interest-rate based instrument exchange utilizing a server-based trading platform. The IRFI receives a user selection of a contract including associated characteristic parameters. The IRFI receives user-selected number of contracts to trade associated with a basis point for the contract. An artificial neural network, including at least one input layer, a hidden layer and an output layer, can determine a risk hedging amount based on the basis point for the contract and the selected number of contracts to cover a risk in an auction. The IRFI receives pricing data associated with an auction system, the pricing data including settlement yield and delivery price and determines a delivery amount for the contract.

Systems, methods and articles of manufacture for performing a comparison of tax results based on different sets of tax data, and generating an explanation as to why the tax results differ or do not differ due to the differences in the tax data. The system includes a computing device, a data store in communication with the computing device and a tax preparation software application executable by the computing device. The tax preparation software application has a tax calculation engine, a tax calculation graph, and a change analysis engine. The tax calculation engine is configured to perform a plurality of tax calculation operations based on the tax calculation graph. The change analysis engine is configured to determine whether tax results based on different tax data differ or do not differ. The system may also generate explanation(s) of the reasons that the tax results differ or do not differ due to the different tax data.

Methods and systems for monitoring use and determining risks associated with operation of a vehicle having one or more autonomous operation features are provided. According to certain aspects, operating data may be recorded during operation of the vehicle. This may include information regarding the vehicle, the vehicle environment, use of the autonomous operation features, and/or control decisions made by the features. The control decisions may include actions the feature would have taken to control the vehicle, but which were not taken because a vehicle operator was controlling the relevant aspect of vehicle operation at the time. The operating data may be recorded in a log, which may then be used to determine risk levels associated with vehicle operation based upon risk levels associated with the autonomous operation features. The risk levels may further be used to adjust an insurance policy associated with the vehicle.