Wells Fargo Bank, N.a.

Accenture Global Services Gmbh

Vinoth Venkataraman

Naveen Gururaja Yeri

Manpreet Singh

Ashutosh Verma

Divakar Vijayan

Nihar Swain

Shitiz Gupta

Ramesh Kempanna Babu

Arnab Chakraborty

Vinita V Nair

Kaushik Sanyal

Vikas Kumar

Rajan Shingari

Mayank Kant

Ramesh Babu

Saket Bhardawaj

Juan Morlanes Montesinos

Fernando Rex Lopez

Peter T Duncan

Kundan Lal Sareen

Jai Advani

Kumar Saurabh

Amab D Chakraborty

Arnab D Chakraborty

A voice communications computer system ('VCCS') receives a ring signal from a call device having unverified device identification data. The VCCS identifies an audible frequency component and an electronic frequency component of the ring signal. The VCCS identifies a device identification characteristic or a geographic location characteristic based on the audible or electronic frequency components, and identifies a stored identification characteristic associated with the device identification data. Based on a comparison of the stored identification characteristic with the device identification characteristic or geographic location characteristic, the VCCS generates fraud estimation data. In some cases, the VCCS generates call status data based on the fraud estimation data. The VCCS provides the fraud estimation data or the call status data to a user interface device, which is configured to display data or perform a call action for a call associated with the ring signal.

Ring and hardware characteristic identification techniques to identify call devices

A method may include determining first network data corresponding to a first time period, including alarm data and incident data collected by a set of network devices of a core network during the first time period. The method may include creating, based on the first network data, an incident prediction model. The method may include receiving second network data, associated with the core network, corresponding to a second time period. The second network data may include alarm data and incident data collected by a network device, of the set of network devices, during the second time period. The method may include generating, based on the second network data and the incident prediction model, an incident prediction that includes a prediction whether the network device will experience an incident during a third time period.

Core network analytics system

A device may obtain first information related to network devices of a network. The device may obtain second information related to the network devices and/or to one or more historic network service incidents. The one or more historic network service incidents may be related to network services provided in association with the network devices. The one or more historic network service incidents may include outages and/or degradations of one or more network services. The device may perform an analysis of the first information and the second information. The device may train a predictive model based on the analysis of the first information and the second information. The predictive model may predict a probability of a future network service incident based on the first information and/or the second information. The device may cause third information, related to the network devices, to be monitored based on the predictive model.

Network service incident prediction

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Bengaluru, India

Vinoth Venkataraman