AI TPI Identification Engine for Anti-Bribery Compliance

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Solution Overview

Problem

Companies face challenges in identifying and managing compliance risks associated with third-party intermediaries (TPIs) due to manual and inefficient methods, which lead to missed high-risk TPIs and wasteful efforts on non-TPIs or low-risk entities, especially in anti-bribery and anti-corruption compliance.

Innovation Solution

A dual-engine system utilizing artificial intelligence and machine learning for TPI identification and General Compliance Risk Management, processing big data to provide rapid risk ratings and analytics, replacing manual self-reporting and subjective methods with objective assessments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If manual self-reporting methods are used to identify TPIs, then companies can collect information from third parties, but the method is slow, expensive, and misses unreported high-risk TPIs

Engineering Contradiction:

Improvecompliance risk informationVSAvoididentification time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical review processes with an automated computer-based system that uses machine learning algorithms and natural language processing to automatically analyze third-party data, extract compliance risk information, and identify TPIs without human intervention in the initial screening phase

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables third parties to self-report through automated digital questionnaires and data feeds, eliminating the need for manual data collection while allowing the system to automatically process and analyze the submitted information for compliance risk assessment

Inventive Principle:

Principle #25Self-service

2Reliability

If manual review of all third parties is performed, then complete coverage is achieved, but resources are wasted on non-TPIs and low-risk entities

Engineering Contradiction:

Improverisk assessment accuracyVSAvoidcompliance processing efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system applies different levels of analysis and scrutiny to different third parties based on their risk profiles, allocating intensive review resources only to high-risk TPIs while using automated lightweight assessment for low-risk entities, thereby optimizing resource distribution according to local risk characteristics

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system performs comprehensive automated screening on all third parties initially, then applies partial manual review only to the subset identified as high-risk, rather than performing full manual review on all entities, achieving reliable risk assessment with improved productivity

Inventive Principle:

Principle #16Partial or excessive action

3Productivity

If random sampling of third parties is used due to budget constraints, then some coverage is achieved, but most risky third parties are missed

Engineering Contradiction:

Improvecompliance cost efficiencyVSAvoidrisk identification accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system changes the parameter of risk assessment from subjective manual judgment to objective algorithmic scoring based on multiple data points and risk factors, enabling accurate identification of high-risk third parties without random sampling while maintaining cost efficiency through automated processing

Inventive Principle:

Principle #35Parameter changes

4Productivity

If dual-engine system with AI machine learning is implemented, then processing speed and accuracy are improved, but system complexity increases

Engineering Contradiction:

Improvedata processing speedVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system is segmented into two specialized engines: a TPI identification engine that detects third-party intermediaries and a GCRM engine that performs compliance risk assessment, allowing each component to be optimized independently and processed in parallel to manage overall system complexity

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11386435B2System and method for global third party intermediary identification system with anti-bribery and anti-corruption risk assessment

Publication Date: 2022.07.12 THE DUN & BRADSTREET CORP

AI summary

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.