| CPC G06Q 20/405 (2013.01) [G06Q 20/10 (2013.01); G06Q 20/389 (2013.01)] | 20 Claims |
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1. A method for automatically detecting data discrepancy during data processing by utilizing a system comprising one or more processors, one or more memories and a smart data discrepancy detection module (SDDDM) for automatically detect discrepancy between preset data and input data during data processing of card transactions involving gratuities, the method comprising steps performed by the system:
wherein the SDDDM includes a setting module, a storing module, an implementing module, a receiving module, a calling module, a comparing module, an authorizing module, a transmitting module, a creating module, a training module, and a communication module, wherein each module being called via corresponding Application Programming Interface (API);
establishing a communication link between an application of a client device of a user and a database via a communication interface by calling the communication module via a corresponding API of the communication module, wherein the database stores user profile data of the user, user history data of the user in connection with a card transaction data, and wherein the card transaction data includes a final data, wherein the final data includes a first part data associated with rendering a service by a merchant to the user and a second part data that represents gratuity data for the rendered service;
receiving, from the application of the client device via a corresponding API of the receiving module, a user request comprising a first user input for preauthorizing the second part data of the final data;
setting a desired threshold data value based on receiving the first user input from the application for preauthorizing the second part data of the final data by calling the setting module via a corresponding API of the setting module;
storing the desired threshold data onto the database by calling the storing module via a corresponding API of the storing module;
implementing a data processing algorithm by calling the implementing module via a corresponding API of the implementing module;
receiving, from a merchant computing device of the merchant via the corresponding API of the receiving module, a first merchant input data to process the final data associated with the card transaction, wherein the first merchant input data comprises the first part data and a first-second part data;
calling a first application programming interface (API) to fetch the desired threshold data from the database by utilizing the calling module, wherein calling the first API further comprises retrieving the desired threshold data from the database;
comparing the first-second part data of the final data with the desired threshold data by calling the comparing module via a corresponding API of the comparing module;
determining whether the first-second part data is equal, below or above the desired threshold data;
automatically transmitting an alert signal to the application to receive a second user input to authorize or deny the card transaction in response to determining that the first-second part data is above the desired threshold data by calling the transmitting module via a corresponding API of the transmitting module;
receiving, from the application via the corresponding API of the receiving module, the second user input data for authorizing the card transaction in response to transmitting the alert signal indicating that the first-second part data is above the desired threshold data;
storing the second user input data onto the database indicating that the user authorized the first-second part data for the card transaction with the merchant by calling the storing module via the corresponding API of the storing module;
creating a machine learning model based on the user profile data and the user history data in connection with the card transaction data by calling the creating module via a corresponding API of the creating module;
training the machine learning model with the second user input data indicating that the user authorized the first-second part data by calling the training module via a corresponding API of the training module;
receiving, from the merchant computing device via the corresponding API of the receiving module, a second merchant input data of a second card transaction to process a second final data, wherein the second final data includes a second-first part data and a second-second part data associated with the second card transaction involving the merchant by calling the receiving module via the corresponding API of the receiving module;
implementing the trained machine learning model by calling the implementing module via the corresponding API of the implementing module;
automatically authorizing, based on the trained machine learning model, the second card transaction by calling the authorizing module via a corresponding API of the authorizing module, wherein the second-second part data is above the desired threshold data; and
retraining the machine learning model with transaction history data associated with the second card transaction by calling the training module via the corresponding API of the training module.
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