MODEL FOR ANALZYING AUTHENTICATION ATTEMPTS
An example computer system for analyzing authentication attempts comprises one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to: receive authentication attempt data from a client device attempting to access a user account; input the authentication attempt data into a voice recognition model; receive a confidence score indicating a likelihood the audio data of the authentication attempt data matches original training data; determine whether the authentication attempt data is authenticated based on the confidence score; input the authentication attempt data into a monitor model; receive flagged authentication attempt failure data from the monitor model; input the flagged authentication attempt failure data into a failure analysis model; receive a classification for the authentication attempt data; determine a response based on the classification.
1 . A computer system for analyzing authentication attempts, the computer system comprising:
one or more processors; and
non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to:
receive authentication attempt data from a client device attempting to access a user account, the authentication attempt data including audio data;
input the authentication attempt data into a voice recognition model;
receive a confidence score indicating a likelihood the audio data of the authentication attempt data matches original training data;
determine whether the authentication attempt data is authenticated based on the confidence score;
responsive to the authentication attempt data failing to authenticate, input the authentication attempt data into a monitor model;
receive flagged authentication attempt failure data from the monitor model;
input the flagged authentication attempt failure data into a failure analysis model, wherein the failure analysis model is configured to classify the flagged authentication attempt failure data into classifications that indicate a reason the authentication attempt data failed;
receive a classification for the authentication attempt data;
determine a response based on the classification.
2 . The computer system of claim 1 , wherein the failure analysis model is configured to identify an error, the error including a predicted output differing from an actual output by an amount that exceeds a predetermined threshold.
3 . The computer system of claim 1 , wherein the response is to contact a local bank to require additional authentication.
4 . The computer system of claim 1 , wherein the response is to request additional audio training data.
5 . The computer system of claim 1 , wherein the failure analysis model determines a likelihood score of the classification indicating the reason the authentication attempt data failed.
6 . The computer system of claim 1 , wherein the failure analysis model is a nonlinear autoregressive neural network model.
7 . The computer system of claim 1 , wherein the monitor model is configured to:
determine whether authentication attempt failures associated with the user account exceed a monitor threshold; and
determine a frequency of authentication failure attempts, a recency of receiving training data for the user account, a period of time since a last successful authentication, or metadata data of the attempts.
8 . The computer system of claim 1 , wherein the classification is a fraudulent authentication attempt or a poor training data sample.
9 . The computer system of claim 1 , wherein the computer system is further caused to:
responsive to the authentication attempt data successfully authenticating, provide access to the user account, wherein the user account is registered on a transaction network.
10 . The computer system of claim 9 , wherein the computer system is further caused to:
provide rewards to the user account.
11 . A method for analyzing authentication attempts, the method comprising:
receiving authentication attempt data from a client device attempting to access a user account, the authentication attempt data including audio data;
input the authentication attempt data into a voice recognition model;
receive a confidence score indicating a likelihood the audio data of the authentication attempt data matches original training data;
determining whether the authentication attempt data is authenticated based on the confidence score;
responsive to the authentication attempt data failing to authenticate, inputting the authentication attempt data into a monitor model;
receiving flagged authentication attempt failure data from the monitor model;
inputting the flagged authentication attempt failure data into a failure analysis model, wherein the failure analysis model is configured to classify the flagged authentication attempt failure data into classifications that indicate a reason for the authentication attempt data failed;
receiving a classification for the authentication attempt data;
determining a response based on the classification.
12 . The method of claim 11 , wherein the failure analysis model performs identifying an error, the error including a predicted output differing from an actual output by an amount that exceeds a predetermined threshold.
13 . The method of claim 11 , wherein the response is to contact a local bank to require additional authentication.
14 . The method of claim 11 , wherein the response is to request additional audio training data.
15 . The method of claim 11 , wherein the failure analysis model performs determining a likelihood score of the classification indicating the reason the authentication attempt data failed.
16 . The method of claim 11 , wherein the failure analysis model is a nonlinear autoregressive neural network model.
17 . The method of claim 11 , wherein the monitor model is configured to perform:
determining whether authentication attempt failures associated with the user account exceed a monitor threshold;
determining a frequency of authentication failure attempts, a recency of receiving training data for the user account, a period of time since a last successful authentication, or metadata data of the authentication attempt data.
18 . The method of claim 11 , wherein the classification is a fraudulent authentication attempt or a poor training data sample.
19 . The method of claim 11 , further comprising:
responsive to the authentication attempt data successfully authenticating, provide access to the user account, wherein the user account is registered on a transaction network.
20 . A non-transitory computer readable medium having instructions stored thereon, the instructions causing one or more processors to perform:
receiving authentication attempt data from a client device attempting to access a user account;
input the authentication attempt data into an authentication model;
receive a confidence score indicating a likelihood the authentication attempt data matches original training data;
determining whether the authentication attempt data is authenticated based on the confidence score;
responsive to the authentication attempt data failing to authenticate, inputting the authentication attempt data into a monitor model;
receiving flagged authentication attempt failure data from the monitor model;
inputting the flagged authentication attempt failure data into a failure analysis model, wherein the failure analysis model is configured to classify the flagged authentication attempt failure data into classifications that indicate a reason the authentication attempt data failed;
receiving a classification for the authentication attempt data; and
determining a response based on the classification.