How Is AI Relevant For Due Diligence?

 

There is a constant apprehension that artificial intelligence will automate professional fields and create mass redundancies sweeping through the legal sectors since last few years ago. While those fears are still unfounded. AI technology is beginning to change the due diligence process.

Today, artificial intelligence. (AI) in due diligence process has become an integral part of the industry. Now it is time to separate its hype from reality and observe how top tier lawyers are employ AI-based tools for day-to-day processes. It can also be used to examine the challenges and benefits of using such software and have a look at the future of the due diligence process.

The adoption of AI for the legal industry has not eliminated the need for human insight. It helps law practitioners unleash tremendous potential by automating repetitive tasks and allowing them to spend more time on other higher-value tasks.

Due Diligence in the New Era

Machine learning, a sub-division of artificial intelligence (AI) is advancing to change the very nature of regulatory due diligence and the due diligence team’s capabilities. Advancements enable the teams to sort through vast information faster, relieve skilled labor, save time, cut costs, and improve due diligence quality and data coverage.

AI is full of potential to improve due diligence programs’ efficiency and effectiveness in the coming years. To better understand this potential, we also need to realize the limits of this technology.

Structuring Due Diligence Using AI

To understand the use of AI on due diligence, it’s helpful to review the general due diligence research process, which is split into two main phases:

1.       1. Information discovery

2.       2. Information synthesis

During information discovery, the researcher uncovers information using various sources, such as Google, litigation repositories, and corporate registries. They further qualify knowledge by determining how applicable it is for the subject and the relevance of AI to the due diligence. For instance, the researcher spends time to make sure the findings aren’t referring to another person with the same name as the due diligence subject to satisfy the first qualification. Simultaneously, suppose the information concerns the correct subject. In that case, the researcher determines whether the content is relevant to the purpose, typically determining if the information is relevant to a risk assessment or not.

Throughout this process, researchers even need to conduct information synthesis. During this process, the researcher sorts the information gathered during the discovery stage and makes sense. The researcher decides how the data fits into the case, makes connections between findings, and distills information that fits into the context. Essentially, the researcher compresses data in a more digestible form for the consumer in the research report. Information discovery is also an iterative cycle for synthesizing critical information; this may lead the researcher to think of new investigation angles, leading to a new iteration of information discovery.

Clustering of Results

A common and time-consuming obstacle that a due diligence researcher faces are determining what information is and is not attached to a proper interest subject. For instance, when specialists research an issue with a common name, it is time-consuming to match information with the correct individual without dealing with mistaken identity cases positively. Machine learning now uses a process called result clustering to automatically determine whether the information pertains to the subject of interest, resolving the subject’s identity, as more and more information, is parsed and linked to the actual issue.

Notably, the clustering process takes seconds, saving the researcher’s hours of sifting through results one by one. It also lower downs the likelihood of human error accompanying cognitive fatigue using limited search result previews to determine the applicability of the work to the subject of interest, which shows only a fraction of the product’s available information.

Learning to Rank

Learning to Rank (LTR) algorithms sort through results and re-rank them based on due diligence researchers’ factors after being trained on examples of products that researchers care about. For instance, in a hypothetical Company X, LTR algorithms may show the researcher X company’s court cases fir rather than having the subject’s most popular blog posts flooding the search pages. For this, AI can help to push research-relevant content on top of results. Using this, researchers can quickly review important information and avoid being weighed down by more consumer-focused content.

Result Classification

Several classification models can filter and organize relevant content by labeling training data and supervising learning techniques. Here the program learns from the activities of a human. These techniques train a model to uncover generalized patterns in textual examples labeled against one or more categories. A model rapidly parses through thousands of unseen examples and quantitatively predicts the results falling into different types after being introduced.

Result classification enables due diligence researchers to focus only and only on the results falling into the categories they’re most interested in. It feeds researchers’ results that stand out as ambiguous or can fall into overlapping categories. This way, researchers can disqualify highly irrelevant content and review works that remain ambiguous to the model.

Emerging AI capabilities can free up compliance and research experts through due diligence and high-level risk management activities. However, organizations still have dedicated due diligence teams and risk assessment strategies that drive effective risk management processes. To maximize AI’s benefits, it’s essential to understand what technology precisely fits your program and thereby choose the right provider.