Patent ID: 11922380
Assignee: YAMAPAY INC.
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 5:
6. A Machine Learning (ML) based method for generating recommendations of a portable financial device for a payment transaction, the Machine Learning (ML) based method comprising:
establishing, by one or more hardware processors, a secure communication session with one or more external Application Programming Interfaces (APIs) by scanning a Quick Response (QR) code, during a payment transaction stage at a merchant's website, wherein data exchanged after establishing the secure communication session is encrypted;
fetching, by the one or more hardware processors, data representative of banking accounts associated with a customer from the one or more external APIs after establishing the secure communication session, wherein each banking account comprises one or more portable financial devices, and wherein the data representative of banking accounts associated with the customer comprise: real-time account balance information, past transaction records of the customer and monetary details of the one or more portable financial devices;
determining, by the one or more hardware processors, one or more transactional parameters associated with the payment transaction stage at the merchant's website in real-time based on a third-party website, wherein the one or more transactional parameters comprise: Personal Identifiable Information (PII) of one or more merchants including Merchant Category Codes (MCC) and Merchant Identification Number (MID) of the one or more merchants, transaction-based points associated with the one or more portable financial devices of the customer and one or more banking account conditions, and wherein the one or more banking account conditions comprises setting of credit limit of the one or more portable financial devices by the customer;
correlating, by the one or more hardware processors, the fetched data representative of the banking accounts with the determined one or more transactional parameters using a Machine Learning (ML) based transaction model;
categorizing, by the one or more hardware processors, a type of the one or more merchants based on the Merchant Category Codes (MCC) of the one or more merchants during the correlation of the fetched data representative of the banking accounts with the determined one or more transactional parameters;
determining, by the one or more hardware processors, maximum monetary gain to be provided to each of the one or more portable financial devices associated with the customer, based on the categorized type of the one or more merchants;
generating, by the one or more hardware processors, an optimal score for each of the one or more portable financial devices based on the determined maximum monetary gain provided to each of the one or more portable financial devices associated with the customer, using the Machine Learning (ML) based transaction model;
identifying, by the one or more hardware processors, best suitable portable financial device among the one or more portable financial devices with maximum optimal score;
generating, by the one or more hardware processors, recommendations of the identified best suitable portable financial device for completing the payment transaction stage at the merchant's website based on the identification;
outputting, by the one or more hardware processors, the generated recommendations of the portable financial device on a graphical user interface of one or more electronic devices associated with the customer;
identifying, by the one or more hardware processors, a second best suitable financial device among the one or more portable financial devices, having a second maximum optimal score and sufficient account balance corresponding to the payment transaction, when the best suitable portable financial device having insufficient balance corresponding to the payment transaction;
determining, by the one or more hardware processors, whether first transaction-based points associated with a third best suitable financial device are optimized than at least one of: second transaction-based points associated with the best suitable portable financial device and third transaction-based points associated with the second best suitable financial device, wherein the third best suitable financial device is not associated with the customer; and
generating, by the one or more hardware processors, recommendations of the third best suitable financial device on the graphical user interface of the one or more electronic devices associated with the customer for applying for the third best suitable financial device, upon determining that the first transaction-based points associated with the third best suitable financial device are optimized than at least one of: the second transaction based points associated with the best suitable portable financial device and the third transaction-based points associated with the second best suitable financial device.