SYSTEMS AND METHODS FOR EXECUTING SMART PAYMENTS

Systems and methods for executing smart payments are disclosed. According to an embodiment, a method for executing smart payments may include: (1) receiving, by a computer program and from a borrower, a bulk payment for a plurality of loans; (2) retrieving, by the computer program, information on each of the loans, wherein the information comprises a payment amount due for the loan; (3) determining, by the computer program and using a machine learning model that is trained with prior payments to the loans by the borrower, a payment allocation of the bulk payment for each of the loans; and (4) providing, by the computer program, the payment allocation to a loan system for each of the loans, wherein the loan system for each of the loans executes a payment to the loan for the payment allocation.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Embodiments relate to systems and methods for executing smart payments.

2. Description of the Related Art

Borrowers often have several loans with a lender, such as a financial institution. So, when the borrower makes a payment, unless the borrower provides express instructions, the lender must determine how the funds are to be applied. This can lead to funds being misapplied.

SUMMARY OF THE INVENTION

Systems and methods for executing smart payments are disclosed. According to an embodiment, a method for executing smart payments may include: (1) receiving, by a computer program and from a borrower, a bulk payment for a plurality of loans; (2) retrieving, by the computer program, information on each of the loans, wherein the information may include a payment amount due for the loan; (3) determining, by the computer program and using a machine learning model that is trained with prior payments to the loans by the borrower, a payment allocation of the bulk payment for each of the loans; and (4) providing, by the computer program, the payment allocation to a loan system for each of the loans, wherein the loan system for each of the loans executes a payment to the loan for the payment allocation.

In one embodiment, the plurality of loans are with a plurality of lenders.

In one embodiment, the machine learning model may be further trained with messages from the borrower.

In one embodiment, the information further may include a loan type.

In one embodiment, the method may also include: determining, by the computer program, that manual review of the payment allocation is required; receiving, by the computer program, manual allocations of the bulk payment for each of the loans; and assigning, by the computer program, the manual allocations as the payment allocations. The method may also include training, by the computer program, the machine learning model with the manual allocations.

According to another embodiment, a system may include: a borrower electronic device associated with a borrower; a borrower information database comprising information on each of a plurality of loans for the borrower comprising a payment amount due for each of the plurality of loans; a machine learning model that is trained with prior payments by the borrower; a loan system; and an electronic device executing a computer program that may be configured to receive a bulk payment for a plurality of loans from the borrower electronic device, retrieve the information on each of the loans from the borrower information database, determine, using the machine learning model, a payment allocation of the bulk payment for each of the loans, and provide the payment allocation to the loan system for each of the loans, wherein the loan system for each of the loans may be configured to execute a payment to the loan for the payment allocation.

In one embodiment, the plurality of loans are with a plurality of lenders.

In one embodiment, the machine learning model may be further trained with messages from the borrower.

In one embodiment, the information further may include a loan type.

In one embodiment, the computer program may be further configured to determine that manual review of the payment allocation is required, receive manual allocations of the bulk payment for each of the loans, and assign the manual allocations as the payment allocations. The computer program may be further configured to train the machine learning model with the manual allocations.

A non-transitory computer readable storage medium, may include instructions stored thereon, which when read and executed by one or more computer processors, cause the one or more computer processors to perform steps comprising: receiving, from a borrower, a bulk payment for a plurality of loans; retrieving information on each of the loans, wherein the information may include a payment amount due for the loan; determining, using a machine learning model that is trained with prior payments by the borrower, a payment allocation of the bulk payment for each of the loans; and providing the payment allocation to a loan system for each of the loans, wherein the loan system for each of the loans executes a payment to the loan for the payment allocation.

In one embodiment, the plurality of loans are with a plurality of lenders.

In one embodiment, the machine learning model may be further trained with messages from the borrower.

In one embodiment, the information further may include a loan type.

In one embodiment, the non-transitory computer readable storage medium may also include instructions stored thereon, which when read and executed by one or more computer processors, cause the one or more computer processors to perform steps comprising: determining that manual review of the payment allocation is required; receiving manual allocations of the bulk payment for each of the loans; and assigning the manual allocations as the payment allocations. The non-transitory computer readable storage medium may also include instructions stored thereon, which when read and executed by one or more computer processors, cause the one or more computer processors to train the machine learning model with the manual allocations.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Embodiments relate to systems and methods for executing smart payments.

When a bulk payment from a borrower with multiple loans is received, embodiments may intelligently calculate the payment due for each loan or transaction type and make the payments from the bulk payment. For example, embodiments may use a trained machine learning engine to identify the appropriate payments. If the payments need review, embodiments may notify an operations team for review and approval. Embodiments may create reports following payment.

Referring to FIG. 1, a system for executing smart payments is disclosed according to an embodiment. System 100 may include electronic device 110 that may execute smart payments computer program 115. Electronic device 110 may include any suitable electronic device, including servers (e.g., physical and/or cloud-based), computers (e.g., workstations, desktops, laptops, tablets, etc.), smart devices (e.g., smart phones, smart watches, etc.), Internet of Things (IoT) appliances, etc.

In one embodiment, electronic device 110 may be provided or hosted by a financial institution from which a borrower may have loans. In another embodiment, electronic device 110 may be provided or hosted by a financial technology (FinTech) services entity, an aggregator, etc. that may provide services for a plurality of financial institutions with which the borrower may have loans, lines of credit (e.g., credit cards), etc.

The loans may include business loans, personal loans, lines of credit (e.g., credit cards), mortgages, etc.

Smart payments computer program 115 may receive a bulk payment from a borrower that may have a plurality of loans and/or transactions, and may retrieve information on the borrower's loans and/or transactions, such as any amounts due, from borrower information database 120. It may apply machine learning model 122 to determine the allocation of the bulk payment to the borrower's loans and/or transactions.

In one embodiment, if smart payments computer program 115 is provided by an aggregator, smart payments computer program 115 may interface with borrower information 120 from a plurality of financial institutions with which the borrower has loans.

Smart payments computer program 115 may also apply any business rules 124 provided by the financial institution(s). For example, business rules 124 may specify an order of payment, how overpayments are allocated, etc.

Once the allocation is determined, smart payments computer program 115 may provide the payment allocations to loan systems 140, which may execute the payments. Each financial institution may have its own loan system 140.

If an exception or conflict is identified, smart payments computer program 115 may generate a notification and send it to reviewer electronic device 130. A reviewer may provide input on payment to reviewer electronic device 130, which may be returned to smart payments computer program 115 for execution.

Any feedback received from the reviewer, or the borrower via, for example, borrower electronic device 135, may be used to update machine learning model 122.

Referring to FIG. 2, a method for executing smart payments is disclosed according to an embodiment.

In step 205, a computer program, such as a smart payments computer program, executed on an electronic device may receive a bulk payment from a borrower. The bulk payment may be for a plurality of loans and/or transactions that the borrower may have with one or more lenders.

In step 210, the computer program may retrieve information on the borrowers loans and/or transactions, such as the type of loans/transactions and the payment amount due for each loan/transaction.

In one embodiment, the computer program may retrieve information from a plurality of lenders.

in step 215, the computer program may determine the amount to allocate to each loan/transaction using a machine learning model. The machine learning model may be trained with prior payments to the loans by the borrower, messages (e.g., emails, texts, phone communications, etc.) from the borrower, reports, manual data, etc. The machine learning model may return the loans and/or transactions types with payment amounts for each loan/transaction.

In step 220, the computer program may determine whether manual review is needed to identify the payment amounts. If it is, in step 225, the computer program may generate a notification for a reviewer to manual review or provide the payment allocations.

In step 230, the reviewer may return approval of the allocation or may provide a manual allocation. The computer program may then assign the approved or manual allocations to the payment allocations.

If manual review is not needed, or the reviewer's input has been received, in step 230, the computer program may provide the payment allocation to one or more loan servicing platforms, which may execute the payment. In one embodiment, if the payments are being made to a plurality of lenders, the computer program may provide the allocations to a plurality of payment systems.

In step 235, the computer program may receive feedback on the payment allocations, and in step 240, may update the machine learning model with the feedback.

FIG. 3 depicts an exemplary computing system for implementing aspects of the present disclosure. FIG. 3 depicts exemplary computing device 300. Computing device 300 may represent the system components described herein. Computing device 300 may include processor 305 that may be coupled to memory 310. Memory 310 may include volatile memory. Processor 305 may execute computer-executable program code stored in memory 310, such as software programs 315. Software programs 315 may include one or more of the logical steps disclosed herein as a programmatic instruction, which may be executed by processor 305. Memory 310 may also include data repository 320, which may be nonvolatile memory for data persistence. Processor 305 and memory 310 may be coupled by bus 330. Bus 330 may also be coupled to one or more network interface connectors 340, such as wired network interface 342 or wireless network interface 344. Computing device 300 may also have user interface components, such as a screen for displaying graphical user interfaces and receiving input from the user, a mouse, a keyboard and/or other input/output components (not shown).

Hereinafter, general aspects of implementation of the systems and methods of embodiments will be described.

In one embodiment, the processing machine may be a cloud-based processing machine, a physical processing machine, or combinations thereof.

As noted above, the processing machine used to implement embodiments may be a general-purpose computer. However, the processing machine described above may also utilize any of a wide variety of other technologies including a special purpose computer, a computer system including, for example, a microcomputer, mini-computer or mainframe, a programmed microprocessor, a micro-controller, a peripheral integrated circuit element, a CSIC (Customer Specific Integrated Circuit) or ASIC (Application Specific Integrated Circuit) or other integrated circuit, a logic circuit, a digital signal processor, a programmable logic device such as a FPGA (Field-Programmable Gate Array), PLD (Programmable Logic Device), PLA (Programmable Logic Array), or PAL (Programmable Array Logic), or any other device or arrangement of devices that is capable of implementing the steps of the processes disclosed herein.

The processing machine used to implement embodiments may utilize a suitable operating system.

To explain further, processing, as described above, is performed by various components and various memories. However, it is appreciated that the processing performed by two distinct components as described above, in accordance with a further embodiment, may be performed by a single component. Further, the processing performed by one distinct component as described above may be performed by two distinct components.

In a similar manner, the memory storage performed by two distinct memory portions as described above, in accordance with a further embodiment, may be performed by a single memory portion. Further, the memory storage performed by one distinct memory portion as described above may be performed by two memory portions.

Any suitable programming language may be used in accordance with the various embodiments. Also, the instructions and/or data used in the practice of embodiments may utilize any compression or encryption technique or algorithm, as may be desired. An encryption module might be used to encrypt data. Further, files or other data may be decrypted using a suitable decryption module, for example.

It will be readily understood by those persons skilled in the art that embodiments are susceptible to broad utility and application. Many embodiments and adaptations of the present invention other than those herein described, as well as many variations, modifications and equivalent arrangements, will be apparent from or reasonably suggested by the foregoing description thereof, without departing from the substance or scope. Accordingly, while the embodiments of the present invention have been described here in detail in relation to its exemplary embodiments, it is to be understood that this disclosure is only illustrative and exemplary of the present invention and is made to provide an enabling disclosure of the invention. Accordingly, the foregoing disclosure is not intended to be construed or to limit the present invention or otherwise to exclude any other such embodiments, adaptations, variations, modifications or equivalent arrangements.