There’s no shortage of eagerness surrounding agentic AI adoption in corporate settings, even though there is still a level of wariness.
Almost 40% of professionals surveyed said their organizations either use or are planning to use agentic AI tools, and another 29% say their organizations are considering its use, according to the recent AI in Professional Services Report from the Thomson Reuters Institute. The report also predicted wide-scale growth in the coming years, as 77% of professionals said they expect agentic AI to be central to their workflow by 2030.
The path of agentic AI adoption is similar to the one trod by generative AI (GenAI) over the past couple of years. Now, more than 80% of professionals say their organizations either use or are planning to use GenAI tools, according to the report.
Yet, for either of these innovations, the key question remains. Are these tools being adopted in a way that best suits the users’ needs? In some workplaces, AI agents are being implemented “faster than leaders can redesign processes, assign decision rights, or rethink workforce models,” according to a global survey of executives by the MIT Sloan Management Review and Boston Consulting Group.
Corporate investigators should particularly have a cautious outlook, since the central issues are not profitability or new market enhancements but compliance, risk management, fraud defense, and data integrity concerns.
The field of risk and fraud investigation is connected to a world of data that is readily available and accessible to investigators — but also accessible to illicit actors looking to commit fraud or launder money. The 2026 INTERPOL Global Financial Fraud Threat Assessment reports that agentic AI is being used by fraud actors worldwide and is over four times more profitable than the digital methods previously used. In fact, a great deal of criminal innovation has taken place with this technology, interweaving scams, extortion, and impersonation to further their methods of deception.
Companies look to respond
Pressure is mounting on businesses in fraud-rich verticals to find ways to respond effectively to the expanding volume of tech-driven threats, both out of institutional necessity and for the protection of clients and customers. Parallel to the problems facing data management in other fields, fraud investigators now face data overload rather than data scarcity. Newer investigative and analysis tools, using both generative and agentic AI, can offer investigators a way forward by enhancing the pace of data gathering, organization, and evaluation while maintaining defensibility and oversight.
The immediate challenge is practical. Investigators need guidance in choosing and implementing the newest investigative tools, which have recently advanced in versatility, the breadth of potential data-gathering and assembly abilities, and the number and scope of added tools to handle administrative tasks. Newer tools can efficiently sift through, analyze, and assemble data for investigators, as well as produce an accurate and well-documented draft of an investigative report.
Further, an institution’s regulatory requirements and practical data management can benefit from the availability of organized, preserved links both to the origin of each piece of data for more transparent decision-making and to a record of previous natural language queries used with the tool that investigators can store, share, and refine over time.
This whitepaper draws on real-world evaluations from investigative professionals in the banking sector who have been early adopters of agentic AI solutions for their investigative work. In a recent webinar, two of these investigators described the ways that these tools can speed up investigations, from search and query to producing summaries and reports, which give back to investigators the time to focus on deeper evaluation and analysis.
The evolving role of agentic AI in financial investigations
Risk management needs more tools that work at scale. For corporate investigations, even state-of-the-art technology has struggled over the last few years to keep up with the growing volume and complexity of threats and many new methods of fraud. In the webinar, Amy Fardella, a Senior Vice President of BSA Operations at First National Bank of Texas, explained how AI-driven speed is outpacing institutions’ current resources. “Speed is everything these days,” Fardella said. “Besides destroying evidence, people will create evidence.”
In digital-aided investigations, she has seen things shifting quickly. “We do all things anti-money laundering investigations, and I think we've seen more of it driven by fraud and scams,” she explains, adding that they’re seeing more cases featuring fraud methods such as impersonation, romance and investment scams, and business e-mail compromise.
Indeed, many investigators are now seeing how speed, volume, and method versatility in digitally driven fraud can contribute to the overload problem they face. They also know that good tools with real-time updates and maximal analytic speed are needed to augment investigators’ skill sets and give them the tools to fight this AI-driven wave of fraud.
Because some aspects of these crimes involve nuance, astute human judgement is crucial; and that means investigators need to be placed at the center of an advanced data provision and analysis system — and quickly.
The advantages of using agentic AI tools in investigations
There are numerous benefits for investigators in using AI-driven tools and solutions to improve the speed, analytical capabilities, and value of their work, including:
Human-in-the-loop: Ensuring accountability and context
Many professionals in both the public and corporate investigation fields are rightly worried about the preservation of human analytical judgement after an agency or organization adopts an agentic AI tool. The importance of oversight is not just for accountability and defensibility purposes, but to exercise critical scrutiny of the assembled data and associated output.
Humans draw on not just critical analytic skills, but also tacit, everyday knowledge of human relationships, activities, and contexts. For instance, a human investigator understands that there may be more than one reason why data about individuals, businesses, and their activities looks the way it does. In the webinar, Susan Woods, Senior Ethics and Compliance Investigator for a global infrastructure company, Balfour Beatty Investments, and an early adopter of CLEAR Investigate, notes the unique characteristics of data in the real world.
“You know, data on paper means one thing, data in reality — once you put that context around it — means something really different,” Woods explains. “Now our conversations are more truly about: Is this really a risk? Is it a singular risk? Is it a risk for one person? Is it systemic? Are there other risks attached to it?”
That’s why agentic AI tools need to be looked at as an extension of the investigator’s expertise — particularly in discernment and understanding of impact — and not a replacement for human judgement.
Efficiency gains: Dramatic reduction in investigation timelines
Expert human investigators are also key in interviewing and field observations. Yet even here, a system’s GenAI and agentic AI capabilities can lighten the more routine aspects of the work. Fardella’s experience working with AI tools is that “an interview recording becomes a transcript, which becomes an interview report and executive summary instantly after the meeting.”
This task reduction in the report and presentation phase is another benefit of newer digital investigation systems. AI can take the investigator’s queries and resulting analysis and produce summaries and investigative-report drafts. Due diligence reports, fraud analysis reports, summaries, timelines, and interim interview reports can now be completed in a fraction of the time it used to take.
“I used to spend, you know, at least half my time doing the admin work that came with the fun investigative work,” Woods says. “And that's been dramatically reduced now.”
Further, each document can be produced by conventional or specified formats, and are accompanied by a citation reference section, for a comprehensive and easy-to-review audit trail.
“Basically, it summarizes everything it did for you,” Fardella explains. “And so that way, instead of piecing together screenshots and web links, investigators can just document with the concise, professional wording they have already gotten from the CLEAR Investigate tool.”
Enhanced analytical capabilities: Making connections investigators don’t see
Agentic AI tools can help investigators uncover unknown connections, unusual payment patterns, and customer risk summaries. Part of this enhanced uncovering stems from not needing very specific prompts ahead of time to find connections. Instead, natural language queries are often enough to pull together potential links. “Suddenly, I realized that I could say, is there a connection between persons A, B, and C? And I'd get it all right there at once: One well-written report completely sourced back to the original document,” Fardella says, adding that previously she’d have to pull a report on A, pull report on B, pull report on C, and then see how they interact.
AI tools can offer suggestion queries or prompts — often to run specific searches — each time investigators get initial responses. These suggestions can be evaluated and incorporated into reusable prompts, a process Fardella says she found eased her workflow.
“So, I thought then being able to ask further questions like ‘Why is this risky?’, which seems so general — yet the tool is able to keep up with that same train of thought, like I don't need to start a new chat,” she explains, adding that the AI tool understood the query context, that she was still looking at the same people.
Overcoming internal hurdles: Building stakeholder confidence
Addressing data privacy, information handling, and improving AI transparency, which has been a problem in public-facing GenAI use, are key to building stakeholder confidence in the implementation of newer, agentic AI-based tools. Human resources and legal professionals will rightly have concerns about how the assembled information will be deployed and stored. Woods says this can lead to a host of questions. “Who has access to the information I'm putting into the system? How reliable is that information?” she asks. “And then again, knowing that a lot of our work is based on interviewing and human interaction, how is that going to be handled?”
To this end, agentic AI tools should meet emerging industry standards. For example, search results need to be clearly labeled by type and source, such as adverse media searches. There should be strict role-based access control, audit logging, preserved trails, and ideally government-grade data privacy controls.
Building trust — and reducing task redundancy — depends upon earned confidence among legal, compliance, and investigative teams that comes when users can demonstrate how the system is transparent in its methods.
Building the business case: The value of protection
Many scams and fraud attempts involve the manipulation of human relationships, and while quicker flagging and halting of fraud is the headline benefit, the real benefit to the customer relationship shouldn’t be disregarded. As daunting as the current threat environment is for the financial sector, it is potentially devastating for the institution’s retail customers and small businesses.
“I'm getting through those preliminary steps, which are so important to make sure that the allegation’s credible and that it can and should be investigated — but I’m also getting into the investigation so that the people involved will know that the company cares and [we] are responding quicker,” Fardella observes.
Indeed, the potential value and return on investment on these agentic AI investigation tools may go beyond significant time savings and into the potentially more valuable concept of protection and responsiveness. The company can protect customers from fraud with these tools while simultaneously being responsive to customers’ own concerns or suspicions much more quickly.
Fardella notes that an exemplary case for her team is an investigation that has a direct impact on a customer. The tool can help them avoid becoming a scam victim and losing money or successfully convincing someone that they're the victim of a scam. “Because a lot of times people don't believe that — they don't want to believe that,” she explains. “Those cases really stand out to me because it's not just the analytical aspect of working the case, it's the timely action that actually helps people.”
Taking the agentic AI plunge
Combined with GenAI tools, agentic AI solutions can provide users with a single dashboard of investigative and reporting software, freeing up time for those professionals who are overseeing risk and fraud investigations and allowing them to focus on their own expert interviews and real-world assessment for cases.
While agentic AI has aided the acceleration and sophistication of fraud and money laundering schemes, those same capabilities also can be brought to investigating and stopping new fraud schemes. These AI-driven tools can reduce the time spent on each phase of investigation — data gathering, relationship mapping, and data analysis, as well as report generation — and help users avoid the pitfalls of data overload and lack of source transparency.
This pairing — advanced agentic AI-driven solutions with skilled investigative expertise — will not only speed up case resolution, but provides the protection, assurance, and responsiveness that your company’s customers care most about.
Learn more about how CLEAR Investigate can leverage agentic AI solutions to transform your critical investigations.
