Adjustment of card configurations for flight interruptions

An apparatus comprising:

• one or more processors;
• and
• one or more memories storing instructions that, when executed by the one or more processors, cause the apparatus to:
• receive past flight interruption data indicating a plurality of past flight interruptions;
• receive user transaction data indicating card transactions, which were performed during past flight interruptions, associated with a plurality of users;
• train, based on the past flight interruption data and the user transaction data, a machine learning model to predict an amount of available credit to be applied, during flight interruptions, to a card associated with a user, wherein the trained machine learning model is configured to generate a predicted amount of available credit based on input historical records for the user;
• determine, based on the flight interruption data and the user transaction data, one or more historical records for the user of the plurality of users, wherein each record of the one or more historical records for the user indicates a past flight interruption and one or more performed card transactions associated with the past flight interruption, wherein the one or more performed card transactions were performed via the card associated with the user;
• detect a current flight interruption associated with the user;
• using the machine learning model and based on the detected current flight interruption, determine, based on the machine learning model and the detected current flight interruption and the one or more historical records for the user, a predicted amount of available credit to be accessible during the current flight interruption to the card associated with the user;
• update, based on the predicted amount of available credit, a credit limit of the card associated with the user;
• and
• send, to a user device associated with the user, a notification of the updated credit limit.

A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform steps comprising:

• receiving past flight interruption data indicating a plurality of past flight interruptions;
• receiving user transaction data indicating card transactions, which were performed during past flight interruptions, associated with a plurality of users;
• training, based on the past flight interruption data and the user transaction data, a machine learning model to predict a set of transaction types to be applied, during flight interruptions, to a card associated with a user wherein the trained machine learning model is configured to generate a predicted set of transaction types based on input historical records for the user;
• determining, based on the flight interruption data and the user transaction data, one or more historical records for the user of the plurality of users, wherein each record of the one or more historical records for the user indicates a past flight interruption and one or more performed card transactions associated with the past flight interruption, wherein the one or more performed card transactions were performed via the card associated with the user;
• detecting a current flight interruption associated with the user;
• determining, using the machine learning model and based on the detected current flight interruption and the one or more historical records for the user, a predicted set of transaction types to be applied during the current flight interruption to the card associated with the user;
• and
• updating, based on the predicted set of transaction types, a set of authorized transaction types of the card associated with the user.