Systems and methods for artificial intelligence-based ATM service
Systems and methods for servicing an ATM may include maintaining, by one or more processors, a machine learning model trained to determine one or more actions corresponding to automated teller machines (ATMs), receiving, by the one or more processors, an error code corresponding to an ATM, determining, by the one or more processors, a service history associated with the ATM, applying, by the one or more processors as an input, data corresponding to the service history and the error code to the machine learning model, to determine one or more actions for responding to the error code corresponding to the ATM, and providing, by the one or more processors, the one or more actions for rendering on a user interface, to facilitate servicing the ATM.
1 . A method comprising:
maintaining, by one or more processors, a machine learning model trained to determine one or more actions corresponding to automated teller machines (ATMs);
predicting or receiving, by the one or more processors, an error code corresponding to an ATM;
receiving, by the one or more processors, via an audio device of the ATM, an audio input from a user regarding an error of the ATM;
determining, by the one or more processors, a service history associated with the ATM;
comparing, by the one or more processors, the error code and one or more problems regarding the ATM not resulting in an error code to identify one or more trends between the error code and the one or more problems;
determining, based on the comparison, a predicted issue affecting the ATM;
applying, by the one or more processors as an input, data corresponding to the service history, the audio input, the predicted issue, and the error code to the machine learning model, to determine one or more actions for responding to the error code corresponding to the ATM; and
providing, by the one or more processors, the one or more actions for rendering on a user interface, to facilitate servicing the ATM.
2 . The method of claim 1 , further comprising training, by the one or more processors, during a training phase, the machine learning model using a plurality of training sets corresponding to ATM servicing.
3 . The method of claim 2 , wherein the plurality of training sets comprise a first training set, a second training set, and a third training set, the first training set comprising data corresponding to a plurality of error codes corresponding to ATMs, the second training set comprising data corresponding to a plurality of service history sets for respective ATMs, and the third training set comprising data indicative of actions for servicing the respective ATMs.
4 . The method of claim 1 , wherein receiving the error code comprises receiving, by the one or more processors, during a session between an operator at a branch including the ATM and a service center, the error code corresponding to the ATM.
5 . The method of claim 4 , wherein providing the one or more actions for rendering on the user interface comprises transmitting, by the one or more processors, data corresponding to the one or more actions for rendering on a device at at least one of the branch or the service center.
6 . The method of claim 5 , wherein the machine learning model comprises a first machine learning model, the method further comprising:
generating, by the one or more processors, via a second machine learning model, a content item indicating the one or more actions for rendering at the device.
7 . The method of claim 6 , wherein the first machine learning model comprises at least one of a regression model, a classification algorithm, or reinforcement learning, and wherein the second machine learning model comprises at least one of a generative adversarial network or a variational autoencoder.
8 . The method of claim 1 , further comprising performing, by the one or more processors, at least some of the one or more actions for responding to the error code.
9 . The method of claim 8 , wherein the at least some of the one or more actions comprise at least one of updating a service ticket, escalating the service ticket, or dispatching a service provider to the ATM.
10 . A system comprising:
a processing circuit comprising one or more processors and memory storing instructions that, when executed by the one or more processors, cause the processing circuit to:
maintain a machine learning model trained to determine one or more actions corresponding to automated teller machines (ATMs);
predict or receive an error code corresponding to an ATM;
receive, via an audio device of the ATM, an audio input from a user regarding an error of the ATM;
determine a service history associated with the ATM;
compare the error code and one or more problems regarding the ATM not resulting in an error code to identify one or more trends between the error code and the one or more problems;
determine, based on the comparison, a predicted issue affecting the ATM;
apply, as an input, data corresponding to the service history, the audio input, the predicted issue, and the error code to the machine learning model, to determine one or more actions for responding to the error code corresponding to the ATM; and
provide the one or more actions for rendering on a user interface, to facilitate servicing the ATM.
11 . The system of claim 10 , wherein the instructions further cause the processing circuit to train, during a training phase, the machine learning model using a plurality of training sets corresponding to ATM servicing.
12 . The system of claim 11 , wherein the plurality of training sets comprise a first training set, a second training set, and a third training set, the first training set comprising data corresponding to a plurality of error codes corresponding to ATMs, the second training set comprising data corresponding to a plurality of service history sets for respective ATMs, and the third training set comprising data indicative of actions for servicing the respective ATMs.
13 . The system of claim 10 , wherein the instructions that cause the processing circuit to receive an error code further cause the processing circuit to receive, during a session between an operator at a branch including the ATM and a service center, the error code corresponding to the ATM.
14 . The system of claim 13 , wherein the instructions that cause the processing circuit to provide the one or more actions for rendering on the user interface further cause the processing circuit to transmit data corresponding to the one or more actions for rendering on a device at at least one of the branch or the service center.
15 . The system of claim 14 , wherein the machine learning model comprises a first machine learning model, and wherein the instructions further cause the processing circuit to:
generate, via a second machine learning model, a content item indicating the one or more actions for rendering at the device.
16 . The system of claim 15 , wherein the first machine learning model comprises at least one of a regression model, a classification algorithm, or reinforcement learning, and wherein the second machine learning model comprises at least one of a generative adversarial network or a variational autoencoder.
17 . The system of claim 10 , wherein the instructions further cause the processing circuit to perform at least some of the one or more actions for responding to the error code.
18 . The system of claim 17 , wherein the at least some of the one or more actions comprise at least one of updating a service ticket, escalating the service ticket, or dispatching a service provider to the ATM.
19 . A non-transitory computer readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to:
maintain a machine learning model trained to determine one or more actions corresponding to automated teller machines (ATMs);
predict or receive an error code corresponding to an ATM;
receive, via an audio device of the ATM, an audio input from a user regarding an error of the ATM;
determine a service history associated with the ATM;
compare the error code and one or more problems regarding the ATM not resulting in an error code to identify one or more trends between the error code and the one or more problems;
determine, based on the comparison, a predicted issue affecting the ATM;
apply, as an input, data corresponding to the service history, the audio input, the predicted issue, and the error code to the machine learning model, to determine one or more actions for responding to the error code corresponding to the ATM; and
provide the one or more actions for rendering on a user interface, to facilitate servicing the ATM.
20 . The non-transitory computer readable medium of claim 19 , wherein the instructions further cause the one or more processors to train, during a training phase, the machine learning model using a plurality of training sets corresponding to ATM servicing.