We can argue that over the last decade, the increase in financial regulations and advances in digital technology have created an influx of data that continues to grow at a rapid speed. This influx has made it difficult for even the most experienced due diligence auditors to keep up the pace as they now must spend more time going through substantial quantities of irrelevant and inapplicable data.
However, all is not lost. Advancements in Artificial Intelligence (AI) have begun to change the regulatory environment making the due diligence process more efficient by automating some of the steps and relieving the task of data collection from due diligence teams thus allowing them to focus on other more critical tasks.
With the help of AI and machine learning, due diligence teams can now sort through massive datasets more quickly and efficiently saving both time and costs while improving the quality of the entire process.
In this article, we will discuss three ways that AI functionalities are improving the due diligence process.
What is due diligence?
Due diligence is the investigation of a potential partner or investment carried out by the buyer before they commit to that relationship or investment. The goal of due diligence is to verify the accuracy of the information given to them by the opposing party. In other words, if they were truthful about their identity or the value of the investment.
What Does the Due Diligence Process Entail?
To understand how AI improves due diligence, you must first familiarise yourself with the process. The due diligence process is made up of two steps: collecting the necessary information and composing the data into one easy to comprehend package.
To complete the first step, most companies, use a due diligence checklist which is a document that lists all the crucial information that the auditor must collect as part of the due diligence process. The due diligence checklist also has the vital role of helping auditors to keep a record of their progress, so they remain on track and prevent themselves from leaving out any crucial information.
The due diligence checklist often includes the following items:
- Material Assets
- Compliance and Regulatory Matters
- Corporate Structure & General Matters
- Employees and Management
To collect all the required information, the auditor will use multiple public and non-public sources such as corporate registries, government agencies, and even Google. Then they will narrow down the data by identifying how relevant it is to the subject and the due diligence case.
By applying this step, the auditor ensures that the information collected refers to the correct person or company and not to someone else that goes by the same name. Once they make sure that they are on the right track, they will also assess how applicable the content is to risk assessment.
While the researcher is collecting and assessing the data, they will also simultaneously assemble the information so that it makes sense together. Part of this process is deciding how the data can fit within due diligence, what connections exist between the results and how the data can be ‘cleaned’ so that it creates one completed and comprehensible report.
How Does AI Help in the Due Diligence Process?
Now that we have familiarised ourselves with due diligence, we can continue by discussing three Artificial Intelligence functionalities that help make the due diligence process more efficient.
Result Clustering and Subject Identity Resolution
One of the hardest challenges of due diligence is deciding which data is applicable to the individual or company in question. This issue often arises when the focus of the audit has a common name, making it time-consuming to identify which documents are applicable and which are not.
However, thanks to advancements made in AI, machine learning is now capable of result clustering, which is the ability to deep dive into massive sources of data and automatically identify and categorise information into groups of related data.
Result clustering resolves the issue caused by having multiple entities with the same name by taking all the identifying facts that are specific to that entity and comparing them with any data that was previously known about that person or business.
The result is a similarity score that tells the program which information shares similar features and therefore, should be kept for further grouping (clustering) and categorisation.
With the use of result clustering, AI technologies make the due diligence process more efficient in several ways. They save time as they only need a few seconds to complete the process, they remove human error and effectively resolve the above problem by automatically identifying and narrowing down the data that applies only to the research subject.
Learning to Rank
As we mentioned before, during due diligence, auditors will utilise both public and private sources to collect the information they need. However, the algorithms used by these sources will often not rank data in a way that serves due diligence purposes.
For example, researchers will often use the Google search engine to verify basic information such as a company’s location and phone number. Algorithms that are used by search engines such as Google’s will rank information using broader and unelated factors such as how mobile-friendly the page is or searcher’s past search patterns.
Artificial intelligence programs can solve this problem with their Learning to Rank (LTR) functionality. Once LTR algorithms are trained on the types of results that auditors find the most useful they can re-evaluate and re-rank the results to be more applicable to their due diligence needs.
Due to its LTR capabilities, AI not only saves time, but it also simplifies the due diligence process by assigning a higher ranking to the most topic-relevant information. Which means that researchers can find what they are looking for quickly and easily.
One of the most crucial breakthroughs that AI has made in recent years is its progress in Natural Language Processing (NLP). NLP allows AI programs to better read, analyse and understand the human language used in digital content such as blog posts and news articles.
Because of the advancements in NLP, AI software can be trained to recognise emerging patterns within bodies of text more quickly and easily.
By using supervised learning techniques and labelled training data researchers can teach AI programs what data is crucial to the due diligence process and how they would sort and categorise that data.
This process where the AI uncovers the patterns and filters and organises them into common categories based on what is essential for each due diligence case is called Result Classification. And while humans can take days and even months to do this, it only takes a few seconds for AI programs to complete.
With result classification, researchers can concentrate on the content that is relevant to them as they can quickly disqualify any data that is highly irrelevant to the due diligence process. At the same time, because the AI model will highlight all material that remains uncategorised or falls into multiple categories, researchers can take that data and repurpose it for further improvement and accuracy.
For more information on how Cedar Rose can help your business with the due diligence process, give us a call today on +357 25 346630 or email email@example.com.