Abstract: A method for providing smart auto rental estimatics to a user includes training a machine learning model to predict repair information, receiving a report, analyzing the report to identify a coverage, determining a vendor, receiving a chosen vendor, determining a branch, receiving a chosen branch, analyzing the report and a volume data using the trained machine learning model to determine loss information, calculating out-of-pockets, and transmitting the out-of-pockets to the user.

Abstract: Methods, systems, and apparatus for automatically obtaining tax documents. The system includes a taxpayer client device and a server configured to automatically identify tax return items associated with the taxpayer by analyzing a prior year tax return. The server is configured to generate a list of tax return items including the automatically identified tax return items. The server is configured to receive, from the taxpayer client device, authentication data for a third party database. The server is configured to access the third party database using the authentication data. The server is configured to receive a document from the third party database and automatically determine whether the document corresponds to a tax return item from the list of tax return items. The server is configured to automatically store the document when the document corresponds to one of the tax return items from the list of tax return items.

Abstract: A method and corresponding system to determine an optimized credit limit assignment using reinforcement learning techniques in order to maximize a reward function for a given bank. A reinforcement learning module is configured to use a set of user profiles and an associated set of risk profiles to determine an initial credit limit assignment. Based on this initial credit limit assignment, an updated set of user profiles and an associated set of updated risk profiles are generated. The reinforcement learning module can use these updated sets of user profiles and associated risk profile as inputs to determine an optimized credit limit assignment that maximizes the reward function for the given bank.

Abstract: Systems, apparatuses, and methods for verifying physical presence of a mobile device as part of an electronic transaction. Tags may be generated and displayed on one or more display devices. Those tags, which may be time-limited, may be read by a mobile device to verify physical presence of the mobile device as a prerequisite to initiating, continuing with, or completing an electronic transaction, and/or as a decision point in how the electronic transaction is to be conducted.

Abstract: Embodiments provide methods, and server systems for enhancing online payment transaction experience. A method includes receiving, by a server system associated with a payment network, a tokenization request based on selecting a payment card of a plurality of payment cards of a user from a payment application running on a user device for processing an online payment transaction using the selected payment card at a merchant payment interface on the user device. The tokenization request includes a card information of the selected payment card. The method includes facilitating generation of a digital token. The digital token includes a tokenized card information of the selected payment card. The method includes provisioning the digital token in a floating widget at the merchant payment interface on the user device. The floating widget enables the user to enter the digital token at the merchant payment interface for processing the online payment transaction.

Abstract: Disclosed are systems and methods for supporting a vehicle lending services platform. Specifically, machine learning is applied to identify and accumulate data from electronic services. The machine learning is further used to identify risk associated with a potential lend of a vehicle. The identified risk is presented as part of a browser plugin graphical user interface (GUI) and/or inline with portions of a portal GUI of the vehicle lending services platform.

Abstract: A computer-implemented method of updating an auto insurance policy is provided. The method may include (1) determining that a customer's mobile device has a Telematics Application (“App”) installed on it, the Telematics App configured to (i) receive telematics data associated with another vehicle via a wireless communication broadcast; (ii) determine a travel event from analysis of the telematics data received, and (iii) generate a corrective action based upon the telematics data received or travel event determined that alleviates the risk of vehicle collision. The method may also include (2) monitoring, with the customer's permission, an amount or percentage of usage of the Telematics App on the customer's mobile device while the customer is driving in an insured vehicle; and (3) adjusting an insurance policy premium or discount based upon usage of the Telematics App to facilitate rewarding risk-averse drivers and encourage usage of risk mitigation or prevention technology.

Abstract: A method of exercising effective influence over future occurrences using knowledge synthesis is described. Techniques include influencing methods that yield actions, once a proposed outcome has been assumed. This is different from methods, typically referred to as “predictive” or “prescriptive” that use analytics to model future results based upon existing data and predict most likely outcome. One or more methods of analysis of historical data, in a hierarchical manner, determine events which led to an observed outcome. The outcome-based algorithms use, as input, a future event or state and generate attributes that are necessary precursors. By creating these attributes, the future can be affected. Where necessary, synthetic contributors of such attributes are also created and made to act in ways consistent with generating the assumed outcome. These contributors might be called upon respectively, to post favorable opinions, to report balmy weather, or to describe sales to a certain population demographic.

Abstract: There is provided a method for detecting fraudulent use of monetary credentials during online transactions, comprising: receiving by a computing system, from a client terminal via a network, a request to obtain a digital voucher having a certain monetary equivalent using monetary credentials of a user, determining, a risk of malicious use of the monetary credentials according to an analysis of data denoting the context of the monetary credentials, providing, to the client terminal, a digital unusable voucher, wherein the digital unusable voucher is invalid for performing an online transaction, wherein the digital unusable voucher is structurally similar to a valid voucher that is valid for performing the online transaction, and identifying malicious use of the monetary credentials when within an elapsed predefined interval of time, no appeal in response to invalidity of the digital usable voucher during an attempt to perform the online transaction is received from the user.

Abstract: In order to protect a trading party from predatory trading strategies employed by some market participants, especially during certain periods when quotes for a particular security are experiencing rapid changes or transitions, embodiments of the present invention facilitate and support various new type of trading orders whose booking and execution behaviors are dynamically varied in response to environmental market conditions and/or predefined rules. For example, the orders may be allowed to trade at more aggressive price levels if the market is relatively stable, and the orders can only trade at less aggressive price levels or be automatically suspended or even cancelled when the market is unstable.

Abstract: A method, computer system, and computer program product are provided for assessing a credit risk of a set of companies. A computer system creates a training data set from distance-to-default values for a first set of companies. The computer system builds a set of predictive models based on the training data set, linking the observed distance-to-default to market capitalization and total liabilities. The computer system forecasts estimated new distance-to-default values for a second set of companies, based on their current distance-to-default (obtained from the Merton approach), and a future change in market capitalization and/or change in total liabilities, according to the set of predictive models.

Abstract: A system may receive a first resource allocation request (associated with a first company) from a first entity. The system may determine that the first request is not associated with a risk relationship and arrange for it to be processed via a claim servicing entity. Information about the first request may then be routed to a request handler device for processing and a communication with a first graphical indication may be generated. The system may also receive a second resource allocation request (associated with the first company) from a second entity and determine that it is to be processed via a risk. The system may then route information about the second request to a request handler device for processing and generate a communication with a second graphical indication. Note that the request handler device may be the same device that received the information about the first request.

Abstract: There are provided systems and methods for automatic adjustment of limits based on machine learning forecasting. An entity, such as company or other entity, may purchase items utilizing a payment instrument or card provided to the company by a credit provider system or entity. In order to provide proper underwriting for credit extensions, such as balances and limits of extendable credit, the credit provider system may utilize a forecasting machine learning (ML) model trained to predict a future global balance of funds or a likelihood of repayment of the extended credit limit. This may be based on information retrievable balances from a banking system and a staleness of this data. When the data is stale and has not been updated, the forecasted balance may have a wider range, and thus risk factors may designate less risky and lower limits.

Abstract: Systems and methods for entity risk management are disclosed. A system for entity risk management may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: establishing a connection between the system and a data source, the data source being remote from the system and associated with a first entity; receiving first document data from the data source; normalizing the first document data; classifying the normalized document data; extracting model input data from the classified document data; applying a machine learning model trained to predict risk levels using second document data to the extracted model input data to predict a risk level associated with the first entity; generating analysis data based on the predicted risk level; and based on the analysis data, transmitting an alert to a management device communicably connected to the system.

Abstract: Systems and methods of providing access to earned but unpaid income are provided. An available balance of earned but unpaid income is computed. A login request and device information may be received from a remote device and a login procedure is invoked based on an assessed security level of the login device type. On authenticating the remote device successfully, the available balance of earned but unpaid income is recomputed. A list of financial services is displayed to the user, and a request may be received from the remote device for access to earned but unpaid income specifying cash access, an amount and a deduction schedule. Terms and conditions of a transaction, including a fee are displayed and a signal indicating acceptance is received. The request is fulfilled, cash dispensed, and a deduction file associated with an employer of the user is updated.

Abstract: Using position location information to pre-populate and verify information on a credit application is disclosed. A mobile application for a retailer is downloaded to a mobile device. The mobile application on the mobile device obtains authorization to access location information on the mobile device. Location information for the mobile device is received at a computer system and from the mobile device, the location information generated by a positioning system on the mobile device, the location information comprising: historic location information stored in a memory of the mobile device and real-time location information. The location information is used to prefill at least a portion of a credit application.

Abstract: Systems and apparatus for processing a trade order include a computer configured for receiving market data for a financial asset, receiving pricing parameters and receiving proposed order quantity and price data. The computer is further configured for constructing proposed trades based on the proposed order quantity and price data, and for calculating a theoretical price for the financial asset based on the market data, pricing parameters as well as the proposed order price data. In addition, the computer is configured for comparing the constructed trades with the theoretical price, and displaying market data indicators relative to the theoretical price indicators based on the comparison.

Abstract: The invention relates to a computer-implemented system and method for authenticating a customer of a business. The method may include the steps of: receiving from the customer at least one item of identifying information; confirming the identity of the customer; generating a token that represents an active session in which the customer interacts with one or more systems or personnel at the business; conducting a first set of communications between the customer and a first system of the business; inquiring with the customer as to whether the customer desires to transfer the active session to a second system at the business or to a third party system; and transferring the active session using the token to the second system or the third party system. The method may also comprise the steps of retrieving background information on the customer to assist a representative of the business in assisting the customer.

Abstract: A device receives recording data, for a recording of a user associated with an account, that captures the user describing a transaction. The device processes the recording data to identify one or more characteristics of an individual that described the transaction in the recording. The device determines, based on the one or more characteristics of the individual, whether the individual that described the transaction in the recording is the user associated with the account. The device causes the recording data to be stored in association with transaction data that identifies a list of transactions that are associated with the account of the user, wherein causing the recording data to be stored in association with the transaction data allows the recording to be made accessible to the user via an interface of an application used to manage the account.

Abstract: 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.