Multi-factor authentication using location data

A method of multi-factor authentication using location data, comprising:

• obtaining, by a device, first location data associated with one or more exchanges based on obtaining records that are associated with one or more transactions related to an account, wherein the first location data is associated with a first level of granularity;
• obtaining, by the device and from an entity location database, information that includes second location data associated with the one or more exchanges, wherein the second location data is associated with a second level of granularity that is higher than the first level of granularity and includes information of a physical location associated with one or more entities;
• pre-process entries of the entity location database based on searching using at least a merchant identifier, city, state, and zip code indicated by the records, to identify candidate entries likely associated with the one or more exchanges, thereby reducing computing overhead;
• training, by the device and using the pre-processed entries, a machine learning model to determine a score indicating a likelihood that an exchange of the one or more exchanges corresponds to a particular second location data;
• obtaining, by the device and using the trained machine learning model, the score;
• determining, by the device and based on the score satisfying a threshold, that the exchange is associated with the particular second location data;
• receiving, by the device, from another device, and over a network between the device and the other device, an access attempt associated with a request for access to the account, that includes one or more credentials associated with a first layer of security to the account, wherein the first layer of security includes biometric information obtained via the other device;
• authenticating, by the device, the access attempt based on the one or more credentials based on the first layer of security;
• detecting, by the device, an authentication event associated with granting access to the account, wherein the authentication event occurs after authenticating the access attempt is authenticated, for performing additional operations associated with a second layer of security related to the account;
• providing, by the device and based on detecting the authentication event, one or more authentication queries that are based on the particular second location data,
• wherein the one or more authentication queries are provided to the device in a prompt displayed on the device;
• granting or denying access to the account based on one or more responses to the one or more authentication queries;
• re-training the machine learning model based on feedback information that is provided to the machine learning model via a feedback loop,
• wherein the feedback information is an output of the machine learning model including an indication of whether a predicted match between an entry in the entity location database and the exchange is correct;
• and
• utilizing the re-trained machine learning model in subsequent score determinations.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• obtain records associated with one or more exchanges that are associated with an account, wherein the records include first location data, of a first level of granularity, associated with the one or more exchanges;
• obtain, from an entity location database, information that includes second location data associated with the one or more exchanges based on the records, wherein the second location data includes address information associated with respective entity locations at which the one or more exchanges occurred;
• pre-process entries of the entity location database based on searching using at least a merchant identifier, city, state, and zip code indicated by the records, to identify candidate entries likely associated with the one or more exchanges, thereby reducing computing overheard;
• train, using the pre-processed entries, a machine learning model to determine a score indicating a likelihood that an exchange of the one or more exchanges is related to a particular second location data;
• obtain, using the trained machine learning model, the score;
• determine that the exchange is associated with a particular entity location based on the score satisfying a threshold;
• receive, from another device and over a network between the device and the other device, an access attempt associated with a request for access to the account, that includes one or more credentials associated with a first layer of security to the account, wherein the first layer of security includes biometric information obtained via the other device;
• authenticate the access attempt based on the one or more credentials based on the first layer of security;
• detect an authentication event associated with an access attempt to the account, wherein the authentication event occurs after authenticating the access attempt is authenticated, for performing additional operations associated with a second layer of security related to the account;
• provide, based on detecting the authentication event, one or more authentication queries that are based on the particular entity location,
• wherein the one or more authentication queries are provided to the other device in a prompt displayed on the other device;
• grant or deny access to the account based on one or more responses to the one or more authentication queries;
• re-train the machine learning model based on feedback information that is provided to the machine learning model via a feedback loop,
• wherein the feedback information is an output of the machine learning model including an indication of whether a predicted match between an entry in the entity location database and the exchange is correct;
• and
• utilize the re-trained machine learning model in subsequent score determinations.