Deposit availability schedule

A system, comprising:

• a memory;
• and
• at least one processor coupled to the memory and configured to:
• activate, on a client mobile device, a mobile financial application, wherein the client mobile device is configured to instantiate a user interface (UI);
• receive a customer request, based on interactions with the UI, to deposit a financial instrument;
• based on the customer request, activating a camera on the client mobile device, wherein the camera provides access to a field of view;
• capture, by the camera and based on customer interactions with the UI, one or more images of a financial instrument;
• store, in a computer memory on the client mobile device, the one or more images;
• extract in real-time, by an optical character recognition (OCR) program resident on the client mobile device, a funding amount from one or more portions of the financial instrument;
• communicate, by the client mobile device, the funding amount to a remote machine-learning engine with a deposit availability machine-learning model, wherein the deposit availability machine-learning model is initially trained by other customer historic data, including at least previous financial transactions;
• generate, by the deposit availability machine-learning model and based on the funding amount and a user profile, a customized deposit availability schedule displayable on the UI;
• prior to transmitting the one or more images to a remote deposit server for the remote deposit of the financial instrument, terminating remote processing of the remote deposit when a state of the remote deposit indicates a customer did not confirm a desire to deposit the financial instrument;
• and
• communicating, to the machine-learning engine and based on the terminating remote processing, the state of the remote deposit of the financial instrument as feedback to re-train the deposit availability machine-learning model.

A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, causes the at least one computing device to perform operations comprising:

• activating, on a client mobile device, a mobile financial application, wherein the client mobile device is configured to instantiate a user interface (UI);
• receiving a customer request, based on interactions with the UI, to deposit a financial instrument;
• based on the customer request, activating a camera on the client mobile device, wherein the camera provides access to a field of view;
• capturing, by the camera and based on customer interactions with the UI, one or more images of a financial instrument;
• storing, in a computer memory on the client mobile device, the one or more images;
• extracting in real-time, by an optical character recognition (OCR) program resident on the client mobile device, a funding amount from one or more portions of the financial instrument;
• communicating, by the client mobile device, the funding amount to a remote machine-learning engine with a deposit availability machine-learning model, wherein the deposit availability machine-learning model is initially trained by other customer historic data, including at least previous financial transactions;
• generating, by the deposit availability machine-learning model and based on the funding amount and a user profile, a customized deposit availability schedule displayable on the UI;
• prior to transmitting the one or more images to a remote deposit server for the remote deposit of the financial instrument, terminating remote processing of the remote deposit when a state of the remote deposit indicates a customer did not confirm a desire to deposit the financial instrument;
• and
• communicating, to the machine-learning engine and based on the terminating remote processing, the state of the remote deposit of the financial instrument as feedback to re-train the deposit availability machine-learning model.