Trust, transparency, and technology: Implementing Agentic AI in corporate investigations
Whatever their industry, businesses know they can’t be too careful when it comes to scrutinizing customers, vendors, and transactions. Investigation is a key element of risk management, particularly when it involves identifying and preventing fraud.
This operational necessity also requires massive outlays of time and resources. The increasing complexity of financial transactions and the ever-evolving sophistication of fraudulent schemes have made it challenging for a business’s risk and fraud investigators to keep pace. Investigation teams can spend hours manually cross-referencing information across multiple databases, public records systems, and internal platforms. A single due diligence check might require searches in corporate registries, sanctions databases, litigation records, and adverse media — each requiring separate logins, different search syntaxes, and manual correlation of results.
Given this increasing complexity and the sheer volume of data they need to manage and sort through, businesses are exploring how they can apply AI to their investigations.
By now, companies have had at least some experience with generative AI (GenAI), particularly its most familiar form, ChatGPT. Many are already using GenAI to handle various necessary but repetitive and time-consuming tasks such as research, document writing, and data analysis. Now an emerging form of artificial intelligence called agentic AI is poised to make business investigations faster and more thorough.
This development is because of agentic AI’s distinctive and powerful capabilities. Unlike GenAI, agentic AI can operate like an autonomous agent that can execute multistep processes following predefined objectives with minimal human supervision. Agentic AI breaks down tasks, sequences actions, gathers all the pertinent information, takes appropriate actions, and executes workflows from start to finish. It can also self-correct and adjust outputs based on new self-discovered inputs.
When conducting an investigation using agentic AI, a company’s fraud and risk team starts by setting an objective as an initial prompt. For instance, conducting due diligence on a potential vendor or customer. The AI agent then generates a multistep plan to gather and synthesize pertinent data from multiple sources, including court records, corporate registries, sanctions lists, and news archives. Since the agent automates these procedures, it can deliver results much faster than manual methods.
Understandably, businesses need to be certain that the AI solution they implement accesses trustworthy data. The AI agent must also detail how it conducted its research and reached its conclusions. Put another way, a professional-grade AI tool must provide trails that offer transparency for both internal stakeholders and external auditors.
This e-book will examine how agentic AI’s core capabilities can enhance corporate risk and fraud investigations through several common use cases. It will also discuss how businesses can address internal concerns about incorporating AI into their investigative work.
Industry use case 1: Automated compliance monitoring and reporting
Depending on the industry in which it operates, a business needs to follow numerous government rules and regulations. They also must file reports detailing their compliance protocols. Financial institutions, for instance, are required to comply with federal Know Your Customer (KYC) and anti-money laundering (AML) regulations. Casinos also are required to establish anti-money laundering rules. Whatever the regulations a company needs to meet, being out of compliance can result in costly fines and a damaged reputation in the marketplace.
Maintaining compliance is not only crucial; it’s also highly complicated. There are numerous details to track, and the rules themselves can be complex — and often subject to change. Agentic AI workflows can automate processes for maintaining regulatory compliance. An AI agent can conduct continuous monitoring of transactions, communications, and business operations to identify potential instances of noncompliance. It also can keep tabs on changes in government rules to which an enterprise must conform.
Businesses can also conduct AI-powered investigations of potential threats and vulnerabilities that might expose them to the risk of noncompliance. For instance, AI agents can scan financial transactions for any suspicious activity so that financial institutions and companies with extensive operations overseas can maintain AML compliance. Agentic AI tools can generate and send alerts to the team assigned to monitor compliance. In addition, agentic AI can analyze data to proactively anticipate possible violations. The agent then can create audit-ready documentation of its findings.
In sum, agentic AI can significantly reduce manual compliance efforts, improve audit transparency, and detect potential breaches quickly — before they cause problems with regulators.
Industry use case 2: Investigative due diligence and identity resolution
Is a customer truly who he or she claims to be? Investigating and verifying a current or potential customer identity is essential for developing profitable business relationships — and preventing fraud or regulatory noncompliance.
Agentic AI facilitates these due diligence efforts by aggregating and analyzing data from a myriad of internal and external sources to build comprehensive risk profiles. These datasets can — and should — include not only government records and business registries but also adverse media, sanction lists, criminal and litigation history, and other concerns that require thorough investigation.
A related issue is third-party risk management. As with customers, a business needs to be certain that current and potential vendors, contractors, and partners don’t have any past or current red flags that might expose the company to any risks. Those risks are, in general, similar to those involving potential customers. An AI agent can help investigate a third party’s identity and reveal connections to high-risk entities or previous fraud cases.
There’s another risk associated with nearly all vendors — supply chain problems. With some Covid-era shortages still lingering and tariffs making many products more expensive, manufacturers and retailers need to be prepared for any supply disruptions. AI agents can monitor and assess potential supply chain risks before they cause havoc on production and sales.
When applied to due diligence investigations like these, agentic AI can deliver deeper insights, faster results, and enhanced risk-assessment accuracy.
Industry use case 3: Proactive fraud detection and incident response
This use case overlaps to some extent with the application of agentic AI for customer and vendor due diligence. That’s because customers and vendors themselves can be perpetrators of fraud.
One way that companies are implementing agentic AI in fraud investigations is through real-time pattern recognition and anomaly detection across large volumes of transactional and behavioral data. For instance, AI agents can identify activities that could signal potential fraud, such as money laundering, false invoices, or government benefits scams. These agents can then alert risk and fraud teams, then support rapid incident response with scenario testing and adaptive investigation plans.
Here's one way to see how agentic AI can make investigations more efficient and effective. At key points in an investigation, the agent can prompt the investigator to validate assumptions or provide additional context. If the investigator doesn’t have the answer to the agent’s queries, the agent can suggest alternative strategies to uncover further information. If the investigator does uncover new information, he or she can prompt the agent to incorporate that information into its research and analysis.
By identifying threats earlier, businesses can respond more nimbly to the danger and mitigate potential fraud — and thus reduce or prevent significant financial loss.
The future of corporate investigations
Agentic AI technology can boost operational efficiency in corporations and businesses in numerous ways. Risk and fraud investigations represent one of the most powerful use cases.
Still, many top executives within an organization may harbor serious doubts and worries about enhanced AI. Their concerns are reasonable. These are areas where companies need to be able to defend their actions and decisions thoroughly and clearly. Many business leaders also fear that AI implementation could create new security vulnerabilities or compliance gaps, particularly when dealing with sensitive investigation data, proprietary customer information, or regulated content.
Those who are convinced and want to make a persuasive case for adoption can offer some practical reasons and approaches. They can point to the use cases discussed earlier and demonstrate how they apply to their company’s operations.
Additionally, proponents of agentic AI can show how this technology can address their colleagues’ concerns in the following areas:
Security
AI agents require vast amounts of company data to be effective. That can make them tempting targets of cyber-attacks. Enterprises should already have strict security protocols to protect sensitive data that resides in their IT infrastructure. An AI agent designed for business use can identify any activities and vulnerabilities that might result in a breach. It can then generate a report that can help the company’s IT security team more effectively investigate the problem. This not only protects sensitive data. It keeps the organization in compliance with government data protection regulations.
Transparency
Corporate leaders who are AI skeptics worry about implementing "black box" AI systems that can’t explain or justify their conclusions. This concern is particularly acute in high-stakes investigations involving compliance, fraud detection, and regulatory reporting. AI proponents can point to the crucial importance of choosing a solution whose methodology is clear and easy for nontechnical people to understand. The technology should also provide the data sources they access, and those sources should be demonstrably reliable. All AI actions should have comprehensive audit trails for compliance purposes.
Accuracy
Though able to operate more autonomously than GenAI, agentic AI still requires human oversight to ensure that it operates within ethical boundaries. Proponents of AI incorporation observe that the business should establish clear and stringent policies regarding the technology’s use. These protocols include regular human monitoring and auditing of AI-directed activities and tasks to make certain the company is not allowing biases to negate the accuracy of the outputs that an AI tool generates.
Again, AI tools are always improving. Agentic AI continuously refines its models based on real-world outcomes and user interactions. Over time, its accuracy will become better. But that improvement comes only when those models and the data they use are reliable to begin with. One way a business can help ensure accuracy is to perform rigorous due diligence on the agentic AI solutions available on the market. An organization will want to know whether an AI tool is susceptible to hallucinations — that is, inaccuracies and unsupported outputs. In other words, a vendor should be able to prove that the data it uses is absolutely trustworthy.
Workflow integration
Another argument that proponents can make is that agentic AI is not intended to replace human expertise and discernment. It still requires knowledgeable employees to review what it delivers. An AI agent is a collaborator, an assistant and partner intended to amplify human capabilities and automate necessary but time-consuming tasks and projects from start to finish. This allows executives and employees to focus their time and knowledge on work that creates more value for their company.
Enterprises also should seek out agentic AI solutions that can both integrate with and modernize their legacy digital platforms. These agents can coordinate and manage multi-system workflows within existing IT infrastructure, a capability called intelligent orchestration. Using separate tools that can’t communicate with each other can disrupt workflows.
Adaptability to future needs
As agentic AI continues to improve, it will be able to deliver enhanced capabilities. For businesses, these will include the autonomy to handle complex, multistep tasks across various systems to solve larger, more interconnected problems. Agents will be able to set their own goals and execute plans to meet those objectives. They also will be capable of monitoring real-time data streams from Internet of Things (IoT) devices and other sources, analyzing those streams, and taking immediate actions to optimize operations or detect anomalies. All told, businesses can expect significant boosts in productivity and competitiveness through continuously increasing automation and optimization of workflows.
As companies expand the use of AI agents into their workflows, many executive and employee roles will shift to big-picture roles such as strategic oversight and planning. To further profit from agentic AI’s capabilities, companies will need to create new types of positions relating to AI governance and management.
Proponents can further strengthen their arguments by presenting examples of existing technology that fulfill these requirements and can anticipate and meet future needs. One such technology is CLEAR Investigate, the first AI-powered tool designed to transform investigations.
Though Thomson Reuters launched CLEAR Investigate in February 2026, this new solution is rooted in a half-century history of continuous technological innovation. That history began with the creation of Westlaw, one of the first online legal research services. Since then, Thomson Reuters has consistently developed digital products and services built upon the company’s reputation for trusted research tools and scrupulously reliable data. Major technology innovations that Thomson Reuters has introduced in the past few decades include:
- History Assistant, a large-scale natural language processor that analyzes case law documents (introduced in 1996)
- CARE, a technology that leverages machine learning to help classify legal, tax, and financial documents (2001)
- Westlaw Next, a legal research tool incorporating AI and machine learning (2010)
- Research, automation, and analytics tools that have extended Thomson Reuters AI technology’s applicability to other professional practices, including tax, compliance, and fraud and risk (2010-present)
- CoCounsel, a GenAI assistant for a wide range of professions (2024)
CLEAR Investigate builds upon the capabilities Thomson Reuters has developed throughout its history. CLEAR Investigate can reveal complex relationships and insights, including connections a company’s fraud and risk team may not have considered. CLEAR Investigate has been developed to meet compliance requirements, maintain data privacy, strengthen trust, and to simplify investigations to address a company’s ever-changing needs.
