_{PART TWO}
AI AS A DRIVER OF EFFICIENCY

in partnership with GAN Integrity

AI as a Driver of Efficiency

In a recent McKinsey survey, 78 percent of respondents say their organizations use AI in at least one business function, up from 72 percent in early 2024 and 55 percent a year earlier. In many use cases, this includes automating tasks such as data analysis, screening, and policy management support. In areas such as supply chain and third-party risk management, survey respondents report cost savings as high as 20% more.

The reality is that the technology delivers massive efficiency benefits. Manual teams cannot systematically review tens of thousands of suppliers in real time. Nor can they identify emerging red flags such as negative news or changes in beneficial ownership faster than a machine. This creates use cases such as:

Automated Due Diligence: AI-driven background checks can scan large volumes of adverse media and watchlists to identify potential risks.
Contextual Risk Assessments: AI creating bespoke risk assessments and pre-populated questionnaires.

Continuous Monitoring: Machine learning models can assign dynamic risk scores, alerting compliance when significant changes occur.
Pattern Detection: Identify anomalies in invoicing or contractual relationships that may signal fraud or corruption.

E&C teams are no exception to this, already forming strategies for how to leverage AI to increase program efficiency, and also developing AI policies to provide guardrails for the use of this nascent technology as well as to manage the risks it presents itself.

The allure of AI also applies to other areas of risk management, which in various Roundtables and Working Groups hosted by the Business Ethics Leadership Alliance point out that programs are looking for scalable solutions that unify pre-existing hodgepodges of systems and technologies, drive efficiency, and perhaps unify efforts in ethics and compliance with work in other departments across the enterprise.

Creative Use Cases for AI in TPRM

Automated Due Diligence
Contextual Risk Assessments
Continuous Monitoring
Pattern Detection

In which ways are you considering or implementing AI in TPRM?

Automated Due Diligence
Contextual Risk Assessments
Continuous Monitoring
Pattern Detection
Automated Document Review
AI-Driven Contract Analysis
Decision Support
Automated Triage & Risk Scoring
AI-Driven Ownership & Relationship Mapping

TERMS DEFINED

Automated Due Diligence: AI-driven background checks scan large volumes of adverse media and watchlists to identify potential risks.

Contextual Risk Assessments: AI creating bespoke risk assessments and pre-populated questionnaires.

Continuous Monitoring: Machine learning models assign dynamic risk scores, alerting compliance when significant changes occur.

Pattern Detection: Identify anomalies in invoicing or contractual relationships that may signal fraud or corruption.

Automated Document Review: AI scans contracts, financials, and compliance reports for red flags and anomalies

AI-Driven Contract Analysis: Machine learning to detect risky clauses, deviations from standard terms, or hidden obligations

Decision Support: AI provides risk experts with machine-learning-based recommendations. (e.g. to approve or deny a third party.)

Automated Triage & Risk Scoring: AI-based classification and prioritization of third parties, helping teams focus on the highest-risk entities first

AI-Driven Ownership & Relationship Mapping: AI supported graph analytics to uncover complex ownership structures, beneficial owners (UBOs), and hidden relationships.

AI AS A DRIVER OF EFFICIENCY

Prioritizing AI Use Cases

AI should be deployed with purpose - solving real compliance challenges rather than adopting technology for its own sake. To maximize effectiveness, companies should:

Identify Compliance Pain Points: Clearly define the specific regulatory, ethical, or operational issues AI will address before deploying solutions.
Solve Practical Problems: Focus AI efforts on enhancing compliance efficiency and accuracy, not adding unnecessary complexity.
Enhance, Don’t Replace: AI should support human decision-making, not replace it. Effective AI complements expertise with enhanced data processing capabilities.

Success depends heavily on building a clear, well-defined use case for AI.

High-Impact vs. Feasible:

Start by applying AI to straightforward, high-impact tasks like adverse media checks and sanctions screening - data-intensive processes that are ideal for automation. Once these foundational capabilities are established, gradually expand into more sophisticated applications such as predictive risk modeling and transactional analysis. The success of AI deployments relies heavily on data quality. Incomplete, outdated, or inaccurate data will undermine AI’s ability to generate reliable insights.

Implementation Roadmap:

Break down your AI integrations into discrete segments with their own time horizons and KPIs.

Phase 1: Integrate AI with existing TPRM tools for screening and due diligence.
Phase 2: Expand to continuous risk monitoring and pattern detection.
Phase 3: Orchestrate a holistic approach that merges data from procurement, finance, E&C, and InfoSec.

A phased approach allows companies to demonstrate early wins, refine their strategy, and scale AI solutions effectively.

AI AS A DRIVER OF EFFICIENCY

The Power of AI in Streamlining Due Diligence

AI offers powerful efficiencies in due diligence, allowing organizations to accelerate screening processes, reduce false positives, and enhance overall accuracy. By automating labor-intensive tasks, AI enables compliance teams to focus on high-risk findings instead of wading through irrelevant data.

AI-Enabled Screening Efficiencies

For example Integrity Essential™ - GAN Integrity’s AI-powered risk screening solution, has been found to:

Reduce false positives by 77%. AI screening and consolidated results, reduce irrelevant alerts, cutting down manual review efforts and increasing accuracy.
Support 50% faster onboarding: Automated screening processes accelerate due diligence, enabling more rapid vendor onboarding.

AI Deepsearch for Due Diligence

For example Integrity Enrich™ - GAN Integrity’s AI-powered deep-search due diligence solution, has been found to reduce the volume of results requiring manual assessment by approximately 93%.

To put this in context, if the system initially flags 1,000 potentially relevant items, Integrity Enrich can narrow that list down to just 67 for human review.

With an average review time of 30 seconds per item, this process saves approximately 7.75 hours - transforming a full workday of manual review into just 35 minutes.

In these scenarios, AI streamlines efficiency, allowing teams to focus their expertise on critical risks rather than sifting through noise.

CASE IN POINT:

Digging Deeper, Faster

In addition to improving efficiency, Integrity Enrich™ surfaces hidden risks that traditional open-source searches often miss.

For instance one Fortune 50 company monitoring 20,000 third parties using AI deep-search capabilities, consistently uncovers risk signals that conventional risk intelligence databases fail to detect. This proactive approach helps them stay ahead of emerging threats, highlighting AI’s ability to deliver better and faster risk detection.