Navigating tomorrow's challenges with AI
Compliance and due diligence are two of the most crucial processes that an organization must undertake. They can also be highly complex. If they aren’t properly or completely conducted, it can expose an organization to numerous risks, particularly fraud from outside parties.
AI tools can assist with compliance and due diligence by automating and streamlining the processes, helping organizations’ risk and fraud professionals better manage these risks. It can sort through complexities and help organizations keep current on ongoing changes in legal requirements across jurisdictions. But there’s a caveat: Not engaging in the responsible use of AI can counteract effective compliance and due diligence.
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Today’s compliance and due diligence
Navigating tomorrow’s challenges with AI
Setting yourself up for success
Today’s compliance and due diligence
Regulatory compliance involves activities an organization undertakes to conform to government regulations or other rules that pertain to that organization’s industry or legal status. Not being in compliance can result in costly fines as well as reputational damage.
Due diligence involves the investigation one party undertakes before entering into a legal agreement with another party. The two parties are typically businesses or customers, and each party wants to verify the legal existence of the other. The process typically involves background checks, document verification, and monitoring of their business activities. A specific version is vendor due diligence.
History and importance
Compliance programs began in the early 20th century when the federal government enacted regulations to protect consumers from tainted food and medications. Public concerns over environmental contamination in the 1960s and 1970s gave rise to the Environmental Protection Agency and the Clear Air and Clean Water Acts.
In the financial services sector, the U.S. Securities Act of 1933 introduced the concept of “reasonable investigation,” a form of due diligence that made sellers of securities liable for misrepresentations or omissions of key information. The concept of reasonable investigation would expand to other financial transactions. More recent financial regulations include the 2002 Sarbanes-Oxley Act enacted after the Enron and WorldCom accounting scandals and the Dodd-Frank rules established after the 2009-2009 financial crisis. There also are federal legal requirements regarding know-your customer and anti-money-laundering due diligence.
With the rise of the internet and its central role in commerce and information storage, governments worldwide have instituted regulations regarding cybersecurity and data protection. AI regulations are in their infancy, but they are growing. In other words, despite its positive impact, AI still needs regulation. In the U.S., the Colorado AI Act, which will become effective in February 2026, will regulate the development and deployment of AI systems that make crucial decisions regarding employment or access to financial, legal, government, or healthcare services. In Europe, the 2024 EU AI Act became the world’s first comprehensive regulatory framework for this technology.
What makes rigorous compliance and due diligence crucial for risk and fraud professional practice is the protection they provide both to organizations and to stakeholders.
Main challenges
Compliance management missteps are all too easy to make. Organizations of all sizes and in numerous industries are having to navigate an ever-changing and often bewildering regulatory landscape. Rules and regulations vary from state to state and from country to country.
The main due diligence challenges are similar. Thorough due diligence requires gathering and summarizing massive quantities of information. If the data is incomplete or inaccurate, the organization is at risk of fraud by a customer, vendor, or other business partner.
Yet another challenge organizations must manage: To do a thorough job of either compliance or due diligence can require large outlays of resources and time.
Navigating tomorrow’s challenges with AI
With these challenges continuing to grow and to evolve, risk and fraud professionals need digital technology that can keep pace. This points to the promise of professional-grade AI. Due to its ability to “learn,” AI tools can help with risk analysis and evolve along with regulatory changes and the proliferation of data.
AI use cases for compliance
AI-powered compliance can automate real-time monitoring of new regulations and changes to existing rules efficiently, with fewer needed resources. It can also deliver insights into the regulatory issues that vendors and partners need to comply with, thus helping protect an organization from third-party risk.
- Machine learning algorithms can continuously scan internal communications, transactions, and documentation to identify potential compliance violations in real-time, flagging issues before they escalate into regulatory breaches. This proactive approach allows compliance teams to focus on addressing high-risk areas rather than manually reviewing vast amounts of data, significantly reducing the likelihood of costly violations and penalties.
- Natural language processing capabilities enable AI systems to stay current with ever-changing regulatory landscapes by automatically analyzing and interpreting new regulations, policy updates, and legal requirements across multiple jurisdictions. These systems can then translate complex regulatory language into actionable compliance protocols tailored to an organization’s specific operations and risk profile.
- With the rise of agentic AI, there could be radically new ways to use AI that could help you save research time. Unlike traditional AI, agentic AI makes decisions, uses various tools and APIs, and performs sequences of actions without continuous human guidance.
By providing personalized compliance guidance and automating routine reporting tasks, AI not only improves regulatory adherence but also transforms compliance from a cost center into a strategic function that supports informed decision-making and sustainable business practices.
AI use cases for due diligence
AI due diligence can enhance and automate traditional due diligence processes by analyzing vast amounts of data, identifying patterns, and providing insights that would be difficult or impossible for humans to achieve alone. AI-powered due diligence can streamline the evaluation of a potential business partner, customer, or vendor by uncovering hidden risks such as financial vulnerability, legal problems, sanctions, or adverse media reports.
- During mergers and acquisitions or vendor assessments, AI systems can rapidly scan thousands of contracts, financial statements, legal documents, and public records to identify potential risks, inconsistencies, or red flags that might otherwise remain hidden in traditional manual reviews. This comprehensive analysis provides decision-makers with deeper insights while dramatically reducing the time and resources required for thorough due diligence investigations.
- Advanced AI tools incorporating entity recognition and relationship mapping capabilities can uncover complex networks of connections between individuals, companies, and assets across global databases. These tools can automatically detect politically exposed persons, sanctioned entities, adverse media mentions, and beneficial ownership structures that might pose regulatory or reputational risks.
By continuously monitoring these relationships even after initial due diligence is complete, AI systems provide ongoing risk assessment that adapts to changing circumstances, ensuring organizations maintain appropriate oversight of their business relationships and investments throughout their lifecycle.
Setting yourself up for success
AI is proving to be a useful tool for risk management. But organizations must use it thoughtfully and responsibly. AI has powerful capabilities, but they require human expertise and management.
Ethical AI practices
AI compliance and AI due diligence best practices involve proactively addressing legal and ethical risks associated with AI systems. Key practices include:
- Clear AI policies and procedures
- Ensuring data privacy and cybersecurity
- Implementing robust risk management
- Fostering a culture of compliance within the organization
In addition, organizations can implement robust testing protocols to detect and mitigate algorithmic bias. One such protocol involves ongoing monitoring of AI outputs across different organizational departments.
Organizations can also tap existing frameworks as guidance for proper and effective AI use. For instance, the National Institute of Standards and Technology has developed a set of industry standards called the NIST AI Risk Management Framework (NIST AI RMF) for identifying, assessing, and mitigating AI-related risks.
The right approach, the right tools
Users should be aware that AI introduces risks of its own if you’re not prepared. AI systems may be vulnerable to threat actors seeking to disrupt their algorithms and large language models. And since AI can process so much data, there’s the risk that sensitive information may tempt determined cyber-criminals. That noted, organizations can use AI’s fraud detection capabilities which can be helpful in many industries to fight fire with fire.
AI compliance and AI due diligence are effective only when organizations use the right kinds of AI tools. A business’s risk and fraud teams need an AI tool that can access reliable information about outside parties and the potential risks associated with them. The tool should be able to provide a list of sources to ensure accountability and transparency.
You can expedite due diligence and business investigations with the Risk Analysis Summary report on CLEAR. Traditional business due diligence can be overwhelming with all the extraneous data available today. We take a different approach by analyzing the premium data in CLEAR with generative AI (GenAI). Your company or organization will receive concise and organized insights on businesses of interest, minimizing the need to deconstruct massive reports.
Risk Analysis Summary
Expedite vendor due diligence and business investigations with AI
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