The Dun Bradstreet Corporation

Sandia Corporation

Nipa Basu

Xin Yuan

Alla Kramskaia

Brian Scott Crigler

Kathleen Wachholz

Jianjing Ling

Anjana Bhattacharyya

Yuan Niu

Yufan Liu

MeiHsiu Lin

Richard J Pryor

Eoin Lane

Jingtao Jonathan Yan

Paul Douglas Ballew

Meihsiu Lin

An identification and assessment system that flags business entities that are current or future Third Party Intermediaries and provides compliance risk ratings In at least one embodiment, the system is configured for TPI identification to enable companies to optimize compliance efforts, for example, effective anti-bribery and anti-corruption compliance. The system is configured to identify TPI likelihood and General Compliance Risk Rating to enable businesses to setup anti-bribery and anti-corruption (ABAC) strategies to focus on those high-risk TPIs, perform due diligence, and mitigate compliance risk.

System and method for global third party intermediary identification system with anti-bribery and anti-corruption risk assessment

Embodiments are directed to a trusted platform powered by a client-side company application that implements a Business Organizing Principle ('BOP') for a company and distributed immutable ledger, which provides the company with secure transactions and transaction data. The application's Business Organizing Principle identifies the business components for a company (e.g., HR, marketing, suppliers, etc.) which the application identifies as core, medium-core, and non-core. The application can then suggest and link the business to other businesses on the network based on matching BOP components. The platform's scoring engine uses the ledger's trusted transactions to generate a BOP network score and 'local score' to augment traditional thereby creating a quickly developed trusted ecosystem for horizontal B2B integration between not just businesses, but business components.

Trusted platform and integrated BOP applications for networking BOP components

A Total Loss Predictor Score assesses the risk of Total Loss of new credit applications by leveraging the most powerful insights from business databases. The Total Loss Predictor Score built on segmentation and algorithms that use the commercial information from business databases, powered by analytics, delivers a unique solution for credit risk management to help business creditors with origination decisions.

Apparatus and method for total loss prediction

There is provided a method that includes (a) receiving a unique identifier of a first entity, (b) constructing a family tree of entities that includes the first entity, (c) calculating a decision-making power score for each entity in the family tree in accordance with a rule, thus yielding decision-making power scores, (d) identifying a decision-making entity in the family tree, based on the decision-making power scores, thus yielding an identification of the decision-making entity, and (e) controlling a messaging system to direct a correspondence to a recipient, based on the identification of the decision-making entity. There is also provided a system that performs the method, and a storage device for storing instructions for a processor to perform the method.

Data communications system and method that maximize efficient usage of communications resources

An economy whose activity is to be predicted comprises a plurality of decision makers. Decision makers include, for example, households, government, industry, and banks. The decision makers are represented by agents, where an agent can represent one or more decision makers. Each agent has decision rules that determine the agent's actions. Each agent can affect the economy by affecting variable conditions characteristic of the economy or the internal state of other agents. Agents can communicate actions through messages. On a multiprocessor computer, the agents can be assigned to processing elements.

Method of predicting a change in an economy

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New York, NY, United States of America

Nipa Basu