Bottomline Technologies Limited

Bottomline Technologies (De) Inc.

Leonardo B Gil

Sandhya S Pillalamarri

Peter Cousins

Caitlin Reardon

Nikki Mintrasak

Amanda Boston

Erik Vaughn Mitchell

Marshall Joseph Tracy

Jeffrey Todd Dixon

Alexey Skosyrskiy

Brian Smith Mclaughlin

Brian Smith McLaughlin

Durgaprasad Nagalla

Venkatesh Mohanraj

Display screen with an animated graphical user interface

Display screen with a graphical user interface

This document describes a non-transitory computer readable media programmed to enrich an entered record submitted to be matched with a dataset record stored on a data storage device. The enrichment is done by supplementing data in the entered record with customer data from a dataset. The media is further programmed to search through a plurality of dataset records in the dataset for the entered record. The search is programmed to first determine if the entered record unambiguously matches one of the dataset records or if the entered record unambiguously does not match one of the dataset records. If the entered record does not unambiguously match one of the dataset records, score match characteristics using a Fellegi-Sunter algorithm, save the score as a highest score if the score is above the highest score less a threshold, and save a location of one of the dataset records as a matching record if the score is above a previous highest score. Next, tune a Fellegi-Sunter algorithm parameter with the data from the entered record and data from one of the dataset records; and when the dataset records have been checked, return the matching record.

Process for automatically matching datasets

A method for using machine learning techniques to analyze past decisions made by administrators concerning account opening requests and to recommend whether an account opening request should be allowed or denied. Further, the machine learning techniques determine various other products that the customer may be interested in and prioritizes the choices of options that the machine learning algorithm determines appropriate for the customer.

Automated account opening decisioning using machine learning

Customized automated account opening decisioning using machine learning

According to an aspect, a computing device includes a processor; a computer readable memory; a display screen; a touch sensitive panel overlying the display screen; and computing device application instructions coded in the computer readable memory and executed by the processor to: display a user-selectable photograph on the display screen, the user-selectable photograph including a group of fiducials, generate captured pattern data, the captured pattern data representing coordinate values on the touch sensitive panel where touched by a user, and provide for authentication of the user based on a comparison of the captured pattern data and respective locations of the group of fiducials included in the user-selectable photograph.

Application security system and method

A system for authenticating the user of a computing device comprises an authorized user directory. Each record is uniquely associated with an authorized user and includes at least a computing device ID value that is a globally unique value assigned to the authorized user's computing device, a group of unique depictions such as photographs, an identification of a key depiction. Portions of each image form fiducials recognizable by the user. The record further includes trace pattern verification data representing continuous trace strokes between pairs of the fiducials within the key depiction. To authenticate, the group of images are displayed to the user. The user must first select the key image and secondly trace continuous trace strokes between the pairs of fiducials to match the trace pattern verification data.

Mobile application security system and method

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Derry, NH, United States of America

Leonardo B Gil