Machine learning for authentication based on device proximity

A system for authenticating a transaction, the system comprising:

• a processor;
• and
• a memory storing instructions that, when executed by the processor, cause the processor to:
• pair a card to a mobile electronic device and a wearable device, the card having at least one of a near-field communication (NFC) or radio frequency identification (RFID) capability;
• store data associated with the pairing in a profile of a user associated with the card, the profile including data associated with the card for performing a transaction at a point-of-sale (POS) terminal;
• train a machine learning (ML) model including:
• obtaining first received signal strength indicator (RSSI) data from the card, while the mobile electronic device and the wearable device are at calibrated distances, and
• calculating a first estimated proximity radius encompassing the card, the mobile electronic device, and the wearable device, utilizing the first RSSI data;
• classify the first estimated proximity radius to be within a threshold;
• subsequently, receive from the POS terminal, a request to authorize a transaction based on the data associated with the card, the data being provided by the user via the mobile electronic device while performing the transaction at the POS terminal without the card;
• obtain second RSSI data from the mobile electronic device and the wearable device;
• calculate a second estimated proximity radius encompassing the mobile electronic device and the wearable device based on the second RSSI data;
• using the trained machine learning model, determine that the second estimated proximity radius is within the threshold;
• and
• authorize, to the POS terminal, the transaction.

A non-transitory computer-readable storage medium storing instructions for authentication that, when executed by a processor, cause the processor to:

• pair a card to a mobile electronic device and a wearable device, the card having at least one of a near-field communication (NFC) or radio frequency identification (RFID) capability;
• store data associated with the pairing in a profile of a user associated with the card, the profile including data associated with the card for performing a transaction at a point-of-sale (POS) terminal;
• train a machine learning (ML) model by obtaining first received signal strength indicator (RSSI) data from the card, while the mobile electronic device and the wearable device are at calibrated distances, calculating a first estimated proximity radius encompassing the card, the mobile electronic device, and the wearable device, utilizing the first RSSI data;
• classify the first estimated proximity radius to be within a threshold;
• subsequently, receive from the POS terminal, a request to authorize a transaction based on the data associated with the card, the data being provided by the user via the mobile electronic device while performing the transaction at the POS terminal without the card;
• obtain second RSSI data from the mobile electronic device and the wearable device;
• calculate a second estimated proximity radius encompassing the mobile electronic device and the wearable device utilizing the second RSSI data;
• using the trained machine learning model, determine that the second estimated proximity radius is within the threshold;
• and
• authorize, to the POS terminal, the transaction.