Methods and apparatus for constructing machine learning models to process user data and provide advance access to payments
Devices, systems, and methods herein relate to generating a machine learning model for providing access to earned income. In some embodiments, an apparatus includes a memory and a processor. The processor may be configured to receive calibration datasets including (1) historical time data indicating time worked by a set of users and (2) historical earnings data indicating earnings made by the set of users. The processor may be configured to construct, based on the calibration datasets, a model for identifying anomalous data. The processor may be configured to receive, from a compute device associated with an entity, raw data for a current time period. The processor may be configured to process the raw data by removing anomalous data to produce processed data. The processor may be configured to determine an available balance for each user from the set of users based on the processed data.
1 . A method, comprising:
receiving sets of raw data for a current pay period of an entity, each set of raw data being associated with a different time period of a plurality of time periods in the current pay period and including (1) time data indicating time worked by a set of users affiliated with the entity during that time period and (2) earnings data indicating earnings made by the set of users during that time period;
identifying anomalous data in that set of raw data, the anomalous data including at least one of time data errors or earnings data errors;
processing that set of raw data by removing the anomalous data identified in that set of raw data to produce processed data without at least one of the time data errors or the earning data errors;
determining, for the time period associated with that set of raw data, a net earnings amount for each user from the set of users based on the processed data;
updating an available balance of each user from the set of users based at least on the net earnings amount;
receiving, from a user device from the set of user devices, a request to transfer at least a portion of the available balance; and
causing, in response to receiving the request to transfer, a transfer of at least the portion of the available balance to a predetermined user account associated with the user.
2 . The method of claim 1 , further comprising determining, for the time period associated with that set of raw data, a net-to-gross ratio for each user from the set of users based on the processed data.
3 . The method of claim 2 , further comprising updating an available balance of each user from the set of users based on the net-to-gross ratio.
4 . The method of claim 1 , wherein identifying the anomalous data in that set of raw data is based on one or more of historical net earnings data, historical gross earnings data, and historical net-to-gross ratio data.
5 . The method of claim 2 , further comprising, for the time period associated with that set of raw data, a gross earnings amount for each user from the set of users based on the processed data; and
adjusting the gross earnings amount based on the net-to-gross ratio and a level of risk associated with for each user from the set of users.
6 . The method of claim 1 , further comprising sending information indicative of the available balance for each user from the set of users to a set of user devices such that the set of user devices displays the available balance for each user from the set of users.
7 . The method of claim 6 , wherein sending the information indicative of the available balance for each user from the set of users causes the set of user devices to display the available balance for each user from the set of users and an amount of funds to transfer up to the available balance for each user from the set of users.
8 . The method of claim 6 , wherein sending the information indicative of the available balance for each user from the set of users causes the set of user devices to display one or more accounts for the transfer.
9 . The method of claim 6 , wherein sending the information indicative of the available balance for each user from the set of users causes the set of user devices to display one or more of available balance calculations, transfer data, account data, fee data, pay period data, payday data, balance, time worked, estimated transfer completion data, or payment history.
10 . The method of claim 1 , wherein the predetermined user account includes one or more of a bank account, a payroll card account, a debit card account, a savings account, a charge card account, a pay card account, a payroll card account, and a prepaid card account.
11 . The method of claim 1 , wherein causing the transfer of at least the portion of the available balance is from a financial account associated with a financial institution to the predetermined user accounts.
12 . The method of claim 1 , wherein the available balance of each user from the set of users is an available balance of a wage account associated with that user, the method further comprising determining a negative balance being in the wage account of that user.
13 . The method of claim 12 , wherein the available balance of each user from the set of users is an available balance of a wage account associated with that user, the method further comprising terminating the transfer of future payment amounts to the predetermined user account associated in response to a negative balance being in the wage account of that user.
14 . The method of claim 1 , wherein the available balance is from an account associated with the entity to the predetermined user accounts.
15 . The method of claim 14 , wherein the entity includes one or more of an employer, a company, a corporation, an enterprise, an organization, a franchise, or a provider.
16 . The method of claim 1 , wherein the transfer of at least the portion of the available balance is facilitated by one or more of a debit card network, a payment vendor, or an automated clearing house (ACH).
17 . An apparatus, comprising:
a memory; and
a processor operatively coupled to the memory, the processor configured to:
receive sets of raw data for a current pay period of an entity, each set of raw data being associated with a different time period of a plurality of time periods in the current pay period and including (1) time data indicating time worked by a set of users affiliated with the entity during that time period and (2) earnings data indicating earnings made by the set of users during that time period;
identify anomalous data in that set of raw data, the anomalous data including at least one of time data errors or earnings data errors;
process that set of raw data by removing the anomalous data identified in that set of raw data to produce processed data without at least one of the time data errors or the earning data errors;
determine, for the time period associated with that set of raw data, a net earnings amount for each user from the set of users based on the processed data;
update an available balance of each user from the set of users based on the net earnings amount;
receive, from a user device from the set of user devices, a request to transfer at least a portion of the available balance; and
cause, in response to receiving the request to transfer, a transfer of at least the portion of the available balance to a predetermined user account associated with the user.
18 . The apparatus of claim 17 , the processor further configured to determine, for the time period associated with that set of raw data, a net-to-gross ratio for each user from the set of users based on the processed data.
19 . The apparatus of claim 17 , the processor further configured to update an available balance of each user from the set of users based on the net-to-gross ratio.
20 . The apparatus of claim 17 , the processor further configured to identify the anomalous data in that set of raw data based on one or more of historical net earnings data, historical gross earnings data, and historical net-to-gross ratio data.