Do more with less, and do it faster – that’s the expectation for compliance leaders in today’s turbulent operating environment.
But the regulatory environment is increasingly complex: ever-evolving FCPA guidelines, increased pressure on human rights, continued ESG concerns, sanctions, geopolitics, and national security issues. The list goes on. For global companies, differing regulations across jurisdictions just add to the complexity.
Compounding this, compliance leaders are expected to embrace AI – despite the well-documented pitfalls of using it in sensitive legal or other matters.
The good news is that AI is a big part of the solution and can help compliance leaders handle the complexity and fast-changing environment – especially when running a global due diligence program.
What AI-enabled due diligence actually looks like
When applied carefully and judiciously, AI tools can deliver exponential efficiency gains in:
- Rapidly trawling through vast amounts of structured and unstructured data
- Highlighting anomalies and risks to prompt human intervention
- Producing executive-ready reporting tailored to your compliance program
The key is getting the right balance between AI and human expertise.
Summarisation and report generation
AI can be a force multiplier for risk and compliance teams by doing the repetitive, large-scale sifting so analysts can focus on judgment and escalation.
Rather than producing data dumps full of false positives – the main criticisms of automated reports in recent decades – modern platforms distil information into concise, evidence-linked reports that an analyst can act on immediately.
For example, the new automated screening report tool in our due diligence platform analyses vast amounts of data, sifts through it and presents a structured, organised report that can be as brief or as detailed as needed.
Quality in, quality out
The quality of the report is only as good as the data the AI draws from. When trawling the entire internet, it’s inevitable that AI will produce a report that reflects the variable (at best) quality of information available from an open web search.
Our approach is to ring-fence the data – feeding the AI agents with data from trusted, high-quality sources, so that the provenance is crystal clear and reliable. The most trusted sources for due diligence tend not to be available on the open internet, hence the poor quality of reports generated by the mainstream AI tools.
Wider coverage, quicker
Breadth of geographic coverage and speed of turnaround: two more constant challenges of a global due diligence program. AI addresses these too. Instead of being limited by time, language, or access to local records, AI-enabled platforms can screen across hundreds of jurisdictions and multiple languages at the same time, ingesting billions of digitised records alongside curated in-country sources.
The result: broader reach, faster decisions, and fewer missed risks, especially in markets where manual checks have historically been slow or costly.
Triage and prioritisation
Once the data has been retrieved, AI can make reporting more flexible and seamless than human analysts ever could.
AI tools can categorise findings by relevance and severity, surfacing red flags and deprioritising background noise. This allows analysts to focus attention where it matters most, enabling faster escalation and ultimately quicker, more defensible decisions.
Hallucinations: broadly a thing of the past
The AI landscape has moved on significantly even in recent months, smoothing out headline-grabbing issues that caused distrust of the tools – hallucinations being the most widely-discussed example.
The conventional wisdom that “AI makes stuff up” is now an out-of-date view. AI can still hallucinate, but instances are tremendously reduced. This is due to the sheer pace of innovation and improvement, as well as the fact that the large-scale commercial, high-powered models are incentivised not to fabricate facts.
Beyond the ever-improving quality of the LLMs, it matters greatly how and when they are used. We’ve developed our platform in an “AI-native” way where the LLMs are deployed for thousands of discreet tasks, based on the most appropriate application from technical and due-diligence perspectives. In the words of our engineers, “We don’t just offload all decision-making to an LLM.”
The technical explanation: we have a series of deterministic algorithmic decisions that are made before an LLM is invoked. This means the AI-enabled tools are being used in the correct way at the correct time, perfectly orchestrated with the knowledge provided by human experts to guide and facilitate the process.
Conclusion
AI has moved beyond being a theoretical talking point in due diligence; it is now a practical enabler. By bringing speed, structure, and scale to the process, it allows teams to expand their coverage across more jurisdictions, surface risks faster, and reduce the time lost to false positives or data overload. The result is not just efficiency, but the ability to deliver more robust, cost-effective due diligence programs that keep pace with today’s regulatory and reputational demands.