Block, Inc.

Square, Inc.

Theodore Kosev

Audrey Kim

Jacqueline Reses

Kelvin Chou

Andy Montgomery

Varun Kerof

Elliot Block

Linnea Perelli-Minetti

Caroline Hollis

Connor H Dunn

Emily McLinden

In some examples, a service provider provides a mobile application to a device of a user. The mobile application presents a user interface with a selectable control enabling the user to obtain a loan that is useable for a specific use at a merchant that accepts payments via the service provider. The service provider receives, via the mobile application, an indication of interaction with the selectable control and, based on determining that the user has accepted the loan offer, makes the loan funds available via a payment instrument accessible via the mobile application. Based on receiving transaction data for a current transaction for which the payment instrument is presented for payment, the loan funds are applied as payment for the transaction. After the funds have been applied, at least a portion of the loan is repaid via the mobile application at a particular frequency during a subsequent period of time.

Intelligent loan management via a mobile application

In some examples, a predictive model is trained to determine account balances over which funds in user accounts are predicted to be surplus. Data of a first account of a user is input into the trained predictive model to determine a balance over which a portion of funds in the first account are predicted to be surplus funds for a period of time. A user device of the user presents a user interface that indicates predicted expenses and predicted income for a plurality of periods of time including the period of time. The user interface further indicates the surplus funds for the period of time and includes an interactive element for indicating disposition of the surplus funds. Based on receiving, via the user interface, an indication of an action to perform with respect to the surplus funds, at least one instruction is sent for performing the indicated action.

Predicting capital needs

One or more processors receive, from a payroll application, payroll data specifying employment and payroll information associated with an employer. The payroll application is associated with a payment service. Transaction data is received specifying payments between one or more customers and the employer. A trained model is used to generate a risk score based at least in part on the payroll data and the transaction data, the model having been trained using past payroll data and past transaction data associated with a plurality of merchants. Based at least in part on determining that the risk score satisfies a threshold, an instruction is sent to cause the payroll application to present an interface element in a user interface of the payroll application, Selection of the interface element triggers a delay of an electronic withdrawal of funds from the employer account from a first date to a later date.

Electronic payroll funds transfer delay and failed transfer coverage

In some examples, a system may receive transaction data indicating payments into a first account over time and payments out of the first account over time, the first account associated with a user. In addition, the system may access a data structure including historical account data that indicates variations in a balance of the first account over time. The system may determine an indicated minimum balance for the first account based on inputting the transaction data and the historical account data into a first predictive model configured to predict a minimum balance for enabling predicted payments out of a user account. The system may determine, based on a difference between a current balance of the first account and the predicted minimum balance for the first account, that the first account has a surplus of funds. Based at least on the surplus, the system may send an instruction.

In some examples, a payment instrument is associated with a financing limit. A processor may receive, from a point of sale (POS) device associated with a merchant, an authorization request to authorize the payment instrument for a purchase of one or more first items having a purchase amount that is greater than the first amount. The processor may determine that the first item is a type of item in an inventory of a business of the user. Based at least on determining that a quantity of the type of item in the inventory is below a threshold quantity, changing the first amount to a second amount that is based on the purchase amount. Based at least on changing the first amount, sending to the POS device, an indication that the payment instrument is authorized for the second amount.

Intelligent modification of capital loan offerings at point-of-sale

In some examples, a computing device receives, from a point of sale (POS) device of a merchant, an authorization request for authorizing a payment instrument for a transaction for an item. The computing device sends an authorization approval to the POS device, and determines that a user is eligible for a loan for an amount and the loan is to be repaid at a particular frequency during a period of time. The computing device determines that an amount of the transaction is less than or equal to the amount, and causes an offer for the loan to be presented via a user interface of a payment application that is executable by a user device of the user. Based on acceptance of the loan, the computing device applies the loan funds to the transaction for the item, and the loan is repaid at the particular frequency.

Intelligent loan offering at a point of sale

The proposed system allows merchants to have more time to finalize payroll and to have access to capital for payroll processing. This system/process may include identifying eligible employer devices based on a risk score and transaction data received by the system. The system and process further includes providing an interface element via a user interface that activates a delay upon selection, and delaying initiation of an electronic payroll payment from a first date to a second date responsive to receiving selection of the interface element.

Techniques for intelligently modifying capital loan offerings at a point-of-sale (POS) are described. Server(s) can receive, from a first POS device associated with a first merchant, transaction data associated with transaction(s) between the first merchant and customer(s). The server(s) can determine that the first merchant is eligible for a capital loan of a first amount. The server(s) can receive, from a second POS device associated with a second merchant, an authorization request to authorize a payment instrument, having a limit based on the first amount, for a purchase having a purchase amount greater than the first amount, and the server(s) can determine to increase the first amount to a second amount. The server(s) can send, to the second POS device, an indication that the payment instrument is authorized for at least the second amount, thereby modifying the amount of the capital loan at the POS.

This disclosure describes, in part, techniques for generating predictive models based on past transaction data and/or future-event data to predict: (i) when an account of user is expected to fall below a minimum balance associated with that user, and/or (ii) when an account has surplus funds that may safely be moved to a higher-yield account for some amount of time. In response to determining a predicted time at which an account is expected to fall below the minimum balance, the techniques may generate an offer to extend capital to the user prior to the predicted time. In response to determining that a user has or will have surplus funds, the techniques may generate an offer to move some or all of these surplus funds into a separate account providing a higher yield than the primary account of the user.

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Seattle, WA, United States of America

Theodore Kosev