Due diligence is an important aspect of any business transaction, whether it is a merger, acquisition, partnership, or an investment. Organisations must scrutinise contracts, financial, and operational data to identify risks before a transaction is completed.
However, traditional due diligence is a tedious and time-consuming process. Artificial intelligence is revolutionising the traditional due diligence process by allowing faster document analysis, better risk detection, and more in-depth insights.
What is AI Due Diligence?
The term AI due diligence refers to the application of artificial intelligence technologies such as machine learning and natural language processing for the purpose of automating the analysis of documents and data in business transactions.
Traditionally, due diligence involves the examination of thousands of documents. AI-based due diligence uses AI technologies such as machine learning and natural language processing for the analysis of large numbers of contracts and other business documents. AI-based due diligence helps in the automated analysis of data and documents.
During the traditional due diligence process, the examination of a small percentage of the total number of documents is carried out due to time constraints. In the traditional due diligence process, only 5-10% of the total number of documents is typically reviewed.
This limitation is overcome by AI due diligence because it allows 100% coverage of documents. With this, organisations are able to get a comprehensive understanding of their obligations, liabilities, and potential deal risks.
Some of the most commonly used cases for AI due diligence include:
- Mergers and acquisitions
- Private equity investments
- Evaluation of partnerships
- Review of contracts
- Regulatory investigations.
By using AI due diligence, legal and financial experts are able to spend more time interpreting and strategising instead of wasting it on reviewing documents and retrieving important information from them.
Why Traditional Due Diligence Falls Short
Traditional due diligence processes were created with the assumption that the volumes of information were much lower. Today, businesses are creating enormous volumes of contracts, data records, and regulatory documents.
With the growth of information, several flaws have been discovered with the traditional process of due diligence.
Time-Consuming Reviews
The traditional process of reviewing contracts, agreements, and financial reports is a time-consuming task that involves lawyers, analysts, and consultants.
Each document has to be reviewed individually, which is a major drawback of the traditional process.
For a large-scale deal involving thousands of contracts, the traditional process of due diligence may take several weeks or even months.
Limited Document Coverage
Due to the time constraint, the traditional process of due diligence often involves reviewing a sample of the documents.
Instead of reviewing the entire set of contracts, agreements, and records, the traditional process of due diligence involves reviewing a selected few, which is believed to represent the entire set of documents.
Though this may save time, it is a risky approach, as several key aspects may have been left out in the documents that were not reviewed.
Risk of Human Error
The risk of human error is a major problem that may occur when the analysis is done by humans, as they may get fatigued when dealing with a large number of documents.
Furthermore, different people may interpret the same clause of the agreement in different ways, leading to discrepancies.
High Operational Costs
Another problem with the traditional method is that it is very expensive, as many people, including lawyers and financial experts, may be needed.
The many experts, including lawyers, may spend a lot of time reviewing the documents, hence the high cost of the traditional method.
These weaknesses have contributed to the development of AI technologies that can handle large amounts of data efficiently.
How AI Due Diligence Works
AI-powered due diligence tools utilise machine learning technology and natural language processing to evaluate documents in an automated fashion. These tools perform several functions that facilitate the process of due diligence.
Data Ingestion and Organisation
AI-powered tools are capable of ingesting thousands of documents from the virtual data room or document repository. After ingesting the documents, the tool organises the data in an automated fashion.
The tool may be able to recognise the type of documents, such as contracts, invoices, or compliance documents, and may also be able to recognise duplicate documents, missing documents, or organise the documents in categories.
This automated organisation significantly reduces the time spent preparing and structuring data for analysis.
Intelligent Data Extraction
Natural language processing allows AI tools to extract important data from complex documents. To exemplify this, we can list the following data that an AI tool can extract from a contract automatically:
- payment obligations
- termination clauses
- indemnity clauses
- liability caps
- service-level agreements
- renewal terms
The use of natural language processing allows teams to search and filter data quickly.
Cross-Contract Analysis
Possibly the most powerful capability of AI due diligence is the ability to examine thousands of documents at the same time.
For instance, AI may compare the terms of contracts across an entire portfolio of contracts, searching for things like:
- contrasting pricing structures
- contrasting liability limits
- inconsistent termination provisions
- contrasting service obligations
These types of patterns may not become immediately apparent when reviewing contracts individually, especially if they are done manually.
Predictive Analytics
Some of the AI-based platforms may also have the facility of predictive analytics. Predictive analytics can be used to estimate the risks and trends that are likely to occur in the future.
Some of the risks or trends that can be identified by using predictive analytics are as follows:
- Customer churn risks
- Vendor performance risks
- Regulatory risks
- Bidder engagement risks
This gives organisations an idea of the outcomes before the deal is done.
Key Benefits of AI Due Diligence
There are various advantages associated with AI due diligence, which make it efficient and accurate in the investigation of deals.
Faster Deal Timelines
AI has greatly reduced the time taken in the assessment of deals. While it used to take weeks for teams to go through the information, AI has reduced it to a matter of hours or days.
This has helped various organisations in completing deals in a timely manner, especially in a competitive transaction environment.
Cost Efficiency
AI has helped in reducing the overall cost associated with due diligence, especially in terms of document assessment. This has helped teams in reducing the time taken in document assessment, thus enabling them to focus on strategic activities.
This has helped in reducing the overall cost in the long run.
Higher Accuracy
AI systems analyse all documents uniformly without any fatigue or distractions. Each document is analysed according to the same set of rules, ensuring all documents in the data set are analysed uniformly.
This minimises the probability of any risk being ignored or any obligation being neglected.
Deeper Risk Identification
AI systems are capable of identifying many more risks than traditional methods of document review. This is because AI systems are capable of analysing all documents in the document repository.
In many instances, AI system results can reveal several times as many key issues as traditional methods.
Better Decision-Making
The structured information available as a result of AI document analysis enables better decision-making for top management. Rather than digging through thousands of pages of documents, top management can refer to key reports highlighting key issues and deal-breakers. This enables better strategic decision-making.
AI Due Diligence Checklist: Key Areas to Investigate
In scrutinising an organisation that employs an AI technology or utilising an AI system for due diligence, certain key areas should be considered.
Data Governance and Training Data
Organisations should assess where the AI system’s training data is coming from and if it is lawfully obtained. Inadequate data management and utilisation of data could cause an AI system to be less dependable.
Some of the key questions that should be answered include:
Is the data adequately licensed?
Does the data contain accurate and unbiased information?
Algorithm Performance and Scalability
It is essential to understand whether the AI system is a complete product or just a prototype or demo version. The organisation should examine whether the AI system performs effectively or whether it is scalable.
Regulatory Compliance
The regulatory compliance of AI systems is essential with respect to data protection. The regulatory requirements that need to be taken into consideration may include data privacy, AI governance, etc., depending on the industry and location.
Intellectual Property Ownership
The ownership of the intellectual property of the models used in the AI system is another factor that needs to be addressed. The intellectual property rights should be clear to avoid any complications.
Security and Data Protection
The due diligence teams should also look into how sensitive information is handled when being processed by AI. Good security measures, encryption techniques, and access control are vital when dealing with sensitive business information.
Human Oversight
There should be clear processes in place when using AI that involve human review. Even when using AI, human professionals should be able to make sense of the findings within the larger business context.
Traditional vs AI-Powered Due Diligence
The traditional due diligence process is largely dependent on document review. Even though this has been a conventional practice for decades, it is not able to keep pace with large volumes of data that are being generated today.
The AI-powered due diligence brings a new dimension to this traditional practice by introducing a more scalable and proactive way of doing things.
Unlike traditional methods, where review is limited to a small sample size, AI has the ability to review all documents in a set. It has the power to analyse large volumes of data quickly, recognise patterns in large sets of documents, and even identify risks.
Challenges of AI Due Diligence
Although AI has many advantages, it is not without its own set of challenges.
AI, for instance, cannot replace human expertise in legal and financial matters.
Another challenge associated with AI is the quality of data used in the tools. The quality of scanned data, for example, can affect the accuracy of the results.
Organisations should also be wary of “AI washing,” a situation in which companies claim that their tools are AI-powered, but in reality, they are not.
Finally, data security and privacy are critical factors in analysing corporate data.
Best Practices for Implementing AI Due Diligence
There are several best practices that organisations that implement AI due diligence should consider.
Firstly, AI is a collaborative tool that should complement professional judgment, not replace it. The combination of AI and professional judgment is the most reliable.
Secondly, the entire data set should be analysed, if possible. The ability of AI to process large repositories of documents eliminates the need for sampling.
Thirdly, the AI model should be tailored according to the objectives of the particular transactions, as well as the risk framework of the transactions.
Lastly, transparency is key, with the AI system showing the reason why certain documents or clauses are highlighted as possible risks.
How Legistify Supports AI-Powered Due Diligence
The effectiveness of AI-powered due diligence is increased by the availability of tools that can quickly scan large volumes of contracts and retrieve key information. Legistify is a platform that assists the due diligence process by integrating contract management with AI-powered analytics.
Legistify helps teams review contracts at scale by automatically extracting key information, including payment conditions, liability conditions, termination conditions, renewal conditions, etc. This helps organisations gain better insights into their contracts, which is essential during the due diligence process.
Using Legistify, deal teams gain better visibility into their contracts, which helps them identify possible risks during the due diligence investigation process.
The Future of AI in Due Diligence
As businesses continue to create more and more data, the role of AI in due diligence will grow significantly.
Improvements in natural language processing and machine learning will allow for more in-depth analysis of data related to the legal, financial, and operational health of companies.
Additionally, AI tools will become more integrated with contract lifecycle management tools, enterprise data systems, and analytics tools.
This will allow companies to monitor risks continuously rather than relying on one-time due diligence.
In the future, AI will allow companies to make more effective decisions related to opportunities by identifying potential risks and making transaction decisions more quickly.
Conclusion
AI is changing the way we conduct due diligence with faster analysis, deeper risk detection, and broader insights. By using AI-powered tools and human expertise, we can be more efficient in our deal investigations.
