Aon Global Operations Se, Singapore Branch

Aon Global Operations Ltd (singapore Branch)

Aon Global Operations Plc, Singapore Branch

Oisin Lyons

Barry Sheridan

Eoin Hurrell

Piotr Milian

Trevor Doherty

In an illustrative embodiment, methods and systems for identifying prospective new clients based upon review of current clients include accessing a book of business of a user of a transactional platform to identify current clients, identifying, key terms relevant to each of the clients, automatically performing Internet searches, each search using different groupings of the key terms, automatically deriving from web sites of the search results information regarding a number of prospects, and presenting prospect information to the user. The key terms may be identified through performing web searches and web scraping of online information related to the current clients. The clients may be categorized based upon organizational attributes prior to searching. An initial set or sets of key terms may be filtered through performing a number of data analyses on the key terms.

Systems and methods for intelligent prospect identification using online resources and neural network processing to classify organizations based on published materials

In an illustrative embodiment, systems and methods for automatically identifying peer organizations of a subject organization within a transactional platform identify peer organizations of the subject organization by accessing characteristic features and transactional features of the subject organization, providing a portion of the features to similarity analysis models, and obtaining, through executing the models, predicted peer organizations. The predicted peers may be presented to a user arranged in priority order with some peer features of the respective organization and a control for selecting the respective peer. The user may select a subset of the predicted peers for use in comparison metrics.

System and methods for identification of peer entities

In an illustrative embodiment, systems and methods for generating data metrics and relationship analysis from an organization's electronic communications archives include pre-processing the electronic communications into a consistent, workable format, including filtering the data to remove irrelevant messages. Machine learning models may be applied to support automatic identification of relevant message content for data analytics. The systems and methods may link the electronic communications with transaction records of a transactional platform and analyze the communications traffic in view of transactional patterns and outcomes. Communications between parties may be analyzed to identify timings and patterns, plus correlations between electronic communication patterns and business outcomes.

Systems and methods for data mining of historic electronic communication exchanges to identify relationships, patterns, and correlations to deal outcomes

In an illustrative embodiment, systems and methods for performing cascading matching of data records from disparate data sources comprise identifying matches using at least one uniquely identifying data field and at least one additional data field shared by a first data set and a second data set. Potential matches may be resolved through calculating differences between one or more shared data fields of a matched data record of the first data set and both a first matched record and a second matched record of the second data set, and determining a best match through analyzing the calculated differences. Unmatched records may be iteratively matched using a different uniquely identifying data field and/or different at least one additional data field(s).

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Mountjoy, Ireland

Oisin Lyons