It has been said that the trouble with M&A due diligence (DD) is not that companies fail to do it, but that they fail to do it well. Properly executed, the enhanced DD process required before an acquisition is best understood as an investigation rather than a compliance DD exercise. It’s no coincidence that experienced fraud investigators are often engaged to manage M&A DD projects – otherwise how would a researcher know what to look for?
At Integrus, our enhanced DD team has extensive experience in fraud investigations and business intelligence. Well aware of the stringent requirements and high stakes surrounding the acquisition of a company, we have built a sophisticated AI driven research engine that makes the DD process more robust.
Here are 5 ways that our AI-driven model, which powers our IntegReport offering, can contribute to the pre-M&A DD process:
1. Yes, we can get you a final (AI driven) report by tomorrow
By the time an organization is ready to commit to a pre-M&A DD budget, counsel often already wants a full analysis of the target company’s public profile. While it can be implausible for a human analyst to thoroughly research and review thousands of resources in days or even weeks, our AI driven model can review tens of thousands of documents overnight. Rather than replace the work of a human analyst, AI driven models can rapidly turn around a final AI driven report, so that major issues come to light immediately and a high level view of the target’s public domain profile is achieved right away.
2. Enhance human source interview outcomes
Successful pre-M&A DD human source interviews are predicated on a robust preparation process. However, that preparation process cannot begin until after a project has commenced, meaning there is often a lag between when a project starts and when effective human source interviews begin. Our AI driven model can again assist by quickly and thoroughly reviewing all project data at the outset, often including thousands of potentially relevant public and internet records, and help analysts quickly obtain the context they need to conduct effective human source interviews.
3. Robust coverage of peripheral entities, related parties, and/or supply chains
Generally, pre-M&A DD focuses on several key entities and individuals – such as key holding and operational entities as well as major shareholders – with others receiving limited coverage. A target company can have dozens of subsidiaries and affiliates. Lack of capacity and budget has always been a pain point in pre-M&A DD, ultimately resulting in inconsistent coverage of lower priority entities. The same can be said for supply chains – increasing focus on forced labor and other ESG related risks have led many organizations to consider a basic screening exercise for high-risk suppliers of acquired entities. However, having a human analyst conduct diligence on hundreds or even thousands of suppliers is cost prohibitive. Our AI driven model can be used cost effectively when it is not feasible to engage a human analyst to review large sets of messily structured data.
4. Drastically reduce the risk of human error
Humans make mistakes that, when combined with poor project management, can result in the failure to detect a given risk. In a DD context, these can happen for several reasons, such as when:
Researchers are over-utilized and working on too many projects to give each one sufficient focus
Researchers are new or inexperienced
Researchers are exhausted, burned out, or otherwise not at their best
Using an AI driven model – fast, thorough, and tireless – alongside human analysis, therefore, is not just a matter of having an additional perspective or saving time. It also significantly contributes to making a risk research process more robust.
5. Monitor public profile throughout a deal timeline and/or post transaction
Deals get delayed for all sorts of reasons. When they do, the results of a DD report can quickly become dated. AI driven models can efficiently monitor public profiles for changes – including the broader internet – for as long as necessary.