American Express Travel Related Services Company, Inc.

Bobby Chetal

Anshul Jain

Sharad Kumar Agrawal

Arun Dutta

Ayush Jain

Rahul Ghosh

Rahul Sharma

Krishnaprasad Narayanan

Yakshi Shode

Ashni Jauhary

Kartikeya Singh

Vivek Singh

Isha Kakkar

Ashi Sawhney

Mayank Pruthi

Varun Agarwal

Madhu Setia

Annamalai Veerappan

Vishal Jain

Priyesha Jain

Richa Bishnoi

Swetha P Srinivasan

Preetika Madan

Systems and methods are disclosed for generating interactive user interfaces for evaluating transactions of an organization. For example, a system can be configured to receive transactions for an organization and determine a first subset of the transactions during a trip at a first location that is different from a home location of a consumer. A respective transaction associated with a second location that occurred during the trip is identified. The respective transaction is assigned to the first subset of the of transactions for the trip. A second subset of the transactions of a spending type is determined. A parameter value associated with the spending type is identified. A parameter adjustment is determined. The system is configured to cause a display of a spending type user interface on a client device and receive a selection of a parameter component.

Insights system

At least some embodiments are directed to an insights inference system that produces multiple insights associated with an entity. The insights inference system generates a decision tree machine learning model, assigning a first insight to a parent node of a decision tree machine learning model and assigning at least one second insight to child nodes of the decision tree machine learning model. Each child node is associated with a sequence number and a rank number. The sequence number and the rank number are indicative of a significance associated with the at least one second insight. The insight inference system responds to queries by traversing the decision tree machine learning model to compute at least one response insight based on the sequence number and the rank number associated with each child node and outputs the at least one response insight to a client terminal.

Computer-based systems having computer engines and data structures configured for machine learning data insight prediction and methods of use thereof

At least some embodiments are directed to an exemplary computer-based electronic activity tracking system that detects activity patterns receiving data values that represent at least one electronic activity. The exemplary electronic activity tracking system includes a detector of unsecure electronic activities that identifies electronic activity patterns performed by a user or non-person entity. The detector of unsecure electronic activities utilizes unsupervised machine learning techniques to detect the electronic activity patterns. The detected electronic activity patterns correspond to unsecure or malicious electronic activities. The electronic activity tracking system outputs notifications indicative of identified unsecure or malicious activity patterns and identifies entities associated with such unsecure or malicious activity patterns. The exemplary electronic activity tracking system implements a graphical user interface operated from a client computing device. The graphical user interface enables a user of the client computing device to perform actions upon the detection of the unsecure or malicious activity patterns.

Computer based system for detecting electronic activity patterns and methods of use thereof

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Gurgaon, India

Bobby Chetal