Fidelity Information Services, LLC

Worldpay Limited

Rajiv Ramanjani

Harsh Sharma

Yogendra Singh Katheria

Seerajudeen Sheik Ahamed

Milin Kapoor

Rohit V Raut

Yogendra Katheria

Shefali Garg

Bhanu Sirisha Pothukuchi

Rahul Pramod Nandanwar

Computer-implemented systems and methods for analyzing applications include, obtaining user data records from a server, constructing an instruction template, the instruction template includes main streams, the instruction template adding the user data records as user parameters corresponding to the main streams, transmitting the user data records to a file transfer connection, inputting the instruction template and a first command into the file transfer connection, the file transfer connection executes the first command, inputting the file transfer connection and a second command into the script file, the script file executes the second command, opening each of the main streams through the pre-defined driver program by using variable records to retrieve a plurality of in-streams of each of the main streams, aggregating the main streams and the in-streams associated with the user parameters resulting a final output, transmitting the final output to the server.

High speed mainframe application tool

Systems and methods are disclosed for generating a prediction on chargeback representment based on probability data and/or results from a plurality of machine learning models. The method includes receiving data associated with at least one disputed transaction for at least one user, wherein the received data includes user-specific information and/or merchant-specific information. The received data is processed to calculate a probability of success in a chargeback representment for the at least one disputed transaction. A prediction is calculated based, at least in part, on the probability of success, one or more results from a plurality of machine learning models, or a combination thereof. A presentation is generated of at least one recommendation on the chargeback representment based, at least in part, on the prediction in a user interface of at least one device associated with the at least one user.

Systems and methods for predictive analysis of electronic transaction representment data using machine learning

Systems and methods are provided for implementing automated user interface testing with integrated machine learning models. Systems and methods for detecting and preemptively correcting flow path errors are disclosed. Systems and methods for minimizing user input and optimizing testing efficiency are disclosed. A result dashboard is disclosed in which testing results and errors are displayed and a user may interact with interactive testing reports.

Automated user interface testing with machine learning

Methods and systems include sending recording data of a call to a first server and a second server, wherein the recording data includes a first voice of a first participant of the call and a second voice of a second participant of the call; receiving, from the first server, a first emotion score representing a degree of a first emotion associated with the first voice, and a second emotion score representing a degree of a second emotion associated with the first voice; receiving, from the second server, a first sentiment score, a second sentiment score, and a third sentiment score; determining a quality score and classification data for the recording data based on the first emotion score, the second emotion score, the first sentiment score, the second sentiment score, and the third sentiment score; and outputting the quality score and the classification data for visualization of the recording data.

Systems and methods for classification and rating of calls based on voice and text analysis

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Bengaluru, India

Rajiv Ramanjani