Predicting capital needs

1. A system comprising:

one or more processors configured by executable instructions to perform operations comprising:

receiving, by the one or more processors, 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;

accessing, by the one or more processors, a data structure including historical account data that indicates variations in a balance of the first account over time;

training a first predictive model with training data extracted at least from the data structure to generate a first trained predictive model, the training data including account data of a plurality of other users, the first trained predictive model trained with the training data to determine account balances over which funds in user accounts are predicted to be surplus;

receiving, by the one or more processors, via a user interface associated with the user, an indication of a risk level to associate with the first account;

determining, by the one or more processors, based on the indicated risk level, and further based on inputting the transaction data and the historical account data into the first trained predictive model, a balance over which funds in the first account are predicted to be surplus;

determining, by the one or more processors, based on a difference between a current balance of the first account and the balance determined for the first account based on the indicated risk level and the first trained predictive model, that the first account has surplus funds; and

sending, by the one or more processors, based at least in part on the surplus funds, an instruction to cause at least one of:

a periodic transfer of the surplus funds from the first account into a second account; or

a transfer of a portion of funds associated with one or more transactions into the second account.

2. The system as recited in claim 1 , wherein the second account is separate from the first account, and wherein the second account is associated with a second yield that is greater than a first yield associated with the first account.

3. The system as recited in claim 1 , wherein a payment service system is associated with the one or more processors, the operations further comprising receiving the transaction data via the payment service system, wherein the first trained predictive model is trained using historical transaction data and historical account data of at least one other user that uses the payment service system.

4. The system as recited in claim 1 , wherein a portion of funds associated with a first transaction is transferred into the second account, and wherein the portion of funds transferred corresponds at least in part to a difference between a transaction amount for the first transaction and a rounded up amount.

5. The system as recited in claim 4 , wherein there are a plurality of second accounts associated with the user, the operations further comprising selecting one of the second accounts to receive the portion of funds based on transaction information associated with the first transaction.

6. The system as recited in claim 1 , the operations further comprising:

determining, based on inputting additional transaction data and updated historical account data for the user into a second trained predictive model, a first amount of time until the balance in the first account is predicted to reach a threshold minimum amount; and

sending, to a device associated with the user, prior to expiration of the first amount of time, an offer to extend financing to the user.

7. The system as recited in claim 6 , wherein the second trained predictive model is trained using historical transaction data and historical account data of at least one other user that uses a payment service system associated with the one or more processors.

8. A method comprising:

receiving, by one or more processors, 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;

accessing, by the one or more processors, a data structure including historical account data that indicates variations in a balance of the first account over time;

training a first predictive model with training data extracted at least from the data structure to generate a first trained predictive model, the training data including account data of a plurality of other users, the first trained predictive model trained with the training data to determine account balances over which funds in user accounts are predicted to be surplus;

receiving, by the one or more processors, via a user interface associated with the user, an indication of a risk level to associate with the first account;

determining, by the one or more processors, based on the indicated risk level, and further based on inputting the transaction data and the historical account data into the first trained predictive model, a balance over which funds in the first account are predicted to be surplus;

determining, by the one or more processors, based on a difference between a current balance of the first account and the balance determined for the first account based on the indicated risk level and the first trained predictive model, that the first account has surplus funds; and

sending, by the one or more processors, based at least in part on the surplus funds, an instruction to cause at least one of:

a periodic transfer of the surplus funds from the first account into a second account; or

a transfer of a portion of funds associated with one or more transactions into the second account.

9. The method as recited in claim 8 , wherein the second account is separate from the first account, and wherein the second account is associated with a second yield that is greater than a first yield associated with the first account.

10. The method as recited in claim 8 , wherein a payment service system is associated with the one or more processors, the method further comprising receiving the transaction data via the payment service system, wherein the first trained predictive model is trained using historical transaction data and historical account-balance data of at least one other user that uses the payment service system.

11. The method as recited in claim 8 , wherein a portion of funds associated with a first transaction is transferred into the second account, and wherein the portion of funds transferred corresponds at least in part to a difference between a transaction amount for the first transaction and a rounded up amount.

12. The method as recited in claim 11 , wherein there are a plurality of second accounts associated with the user, the method further comprising selecting one of the second accounts to receive the portion of funds based on transaction information associated with the first transaction.

13. The method as recited in claim 8 , further comprising:

determining, based on inputting additional transaction data and updated historical account data for the user into a second trained predictive model, a first amount of time until the balance in the first account is predicted to reach a threshold minimum amount; and

sending, to a device associated with the user, prior to expiration of the first amount of time, an offer to extend financing to the user.

14. The method as recited in claim 13 , wherein the second trained predictive model is trained using historical transaction data and historical account data of at least one other user that uses a payment service system associated with the one or more processors.

15. One or more non-transitory computer readable media storing instructions executable by one or more processors to configure the one or more processors to perform operations comprising:

receiving 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;

accessing a data structure including historical account data that indicates variations in a balance of the first account over time;

training a first predictive model with training data extracted at least from the data structure to generate a first trained predictive model, the training data including account data of a plurality of other users, the first trained predictive model trained with the training data to determine account balances over which funds in user accounts are predicted to be surplus;

receiving, via a user interface associated with the user, an indication of a risk level to associate with the first account;

determining, based on the indicated risk level and further based on inputting the transaction data and the historical account data into the first trained predictive model, a balance over which funds in the first account are predicted to be surplus;

determining, based on a difference between a current balance of the first account and the balance determined for the first account based on the indicated risk level and the first trained predictive model, that the first account has surplus funds; and

sending based at least in part on the surplus funds, an instruction to cause at least one of:

a periodic transfer of the surplus funds from the first account into a second account; or

a transfer of a portion of funds associated with one or more transactions into the second account.

16. The one or more non-transitory computer readable media as recited in claim 15 , wherein the second account is separate from the first account, and wherein the second account is associated with a second yield that is greater than a first yield associated with the first account.

17. The one or more non-transitory computer readable media as recited in claim 15 , wherein a payment service system is associated with the one or more processors, the operations further comprising receiving the transaction data via the payment service system, wherein the first trained predictive model is trained using historical transaction data and historical account data of at least one other user that uses the payment service system.

18. The one or more non-transitory computer readable media as recited in claim 15 , wherein a portion of funds associated with a first transaction is transferred into the second account, and wherein the portion of funds transferred corresponds at least in part to a difference between a transaction amount for the first transaction and a rounded up amount.

19. The one or more non-transitory computer readable media as recited in claim 18 , wherein there are a plurality of second accounts associated with the user, the operations further comprising selecting one of the second accounts to receive the portion of funds based on transaction information associated with the first transaction.

20. The one or more non-transitory computer readable media as recited in claim 15 , the operations further comprising:

determining, based on inputting additional transaction data and updated historical account data for the user into a second trained predictive model, a first amount of time until the balance in the first account is predicted to reach a threshold minimum amount; and

sending, to a device associated with the user, prior to expiration of the first amount of time, an offer to extend financing to the user.