In today’s data-driven world, adopting artificial intelligence (AI) is no longer just an option, but a necessity. Mergers and acquisitions are no exception — generative AI is used for 16% of M&A deal processes today and is expected to reach 80% over the next three years.
In most cases, deal-makers use artificial intelligence for M&A due diligence. Among the tools are machine learning (ML), natural language processing (NLP), large language models (LMMs), and predictive analytics.
The article aims to explain the role of AI tools in the due diligence process in detail, describe their benefits, and provide solutions for challenges that arise during their implementation. This will be helpful for advisories, law firms, investment banks, private equity firms, and financial consultants.
The role of AI in M&A due diligence
AI for M&A due diligence refers to the use of advanced technologies that can analyze and process large datasets efficiently and accurately during the M&A deal process. In particular, AI integration enhances traditional due diligence, making it more comprehensive and less time-consuming.
Key AI-driven technologies used in M&A due diligence include machine learning, natural language processing, and data analytics.
Machine learning (ML). ML algorithms identify patterns and predict future trends by analyzing historical data. In the due diligence process, ML helps with:
- Risk assessment. Predicting potential risks and highlighting areas for deeper investigation.
- Financial analysis. Reviewing financial statements, identifying anomalies, and forecasting future performance.
- Compliance checks. Ensuring legal and regulatory compliance by analyzing historical data for potential issues.
Natural language processing (NLP). NLP enables AI to interpret human language. In M&A due diligence, this is crucial for:
- Document review. Automating the review and summarization of contracts, emails, and reports.
- Sentiment analysis. Assessing market and employee sentiment towards the target company.
- Contract analysis. Identifying critical clauses and risks in legal documents.
Data analytics. Advanced analytics uncovers patterns and insights from data sets, assisting in:
- Market analysis. Understanding market trends, competitive positioning, and growth opportunities.
- Operational analysis. Assessing operational efficiencies and identifying strengths and weaknesses.
- Customer data analysis. Analyzing customer behavior and retention to evaluate market position.
Specific functions of AI in due diligence
Here are the key areas and specific functions of AI in M&A due diligence:
AI automates gathering relevant data from multiple sources, such as financial records and legal documents. This reduces the manual effort, speeds up the process, and ensures comprehensive data coverage.
Machine learning algorithms analyze historical financial data to identify trends and patterns, which improves the accuracy of financial forecasts. These algorithms can predict future revenue, profitability, and other financial metrics, helping to create more reliable financial models and valuations.
Natural language processing (NLP) automates the review of large volumes of documents, such as contracts, emails, and regulatory filings. It can extract key information, summarize content, and highlight critical terms and conditions, making the review process faster and more thorough.
AI M&A tools can review legal documents and compliance records to ensure that the target company adheres to relevant laws and regulations. They can identify potential legal risks and highlight areas where the company may be non-compliant, helping to avoid future legal issues.
Benefits of artificial intelligence for M&A due diligence
Let’s explore the key advantages of artificial intelligence for M&A highlighted by Bain & Company research:
- Reduced manual effort
AI-powered dealmaking eliminates grunt work and repetitive tasks, saving time in the due diligence process. By automating manual tasks, teams can focus on higher-value activities, such as analyzing insights and making strategic informed decisions, leading to more efficient and effective M&A transactions.
- Accelerated timelines
AI automates data collection and analysis, significantly accelerating M&A deals. It processes information quickly, enabling teams to gather and analyze data much faster than traditional methods. This speed gives a competitive advantage in M&A environments where timely strategic decision-making is vital.
- Reduced cost
AI technology reduces the need for extensive human labor, making the deal process more cost-effective. Additionally, AI’s ability to quickly identify risks and opportunities helps mitigate potential costly errors or oversights, further contributing to cost reduction.
- Improved focus
AI M&A enhances focus by freeing up team members from time-consuming manual tasks, allowing them to concentrate on more strategic and complex aspects of the deal, such as risk assessment, strategic fit, and value creation.
It’s also important to highlight the following advantages of AI usage:
- Greater accuracy
AI M&A minimizes human error, enhancing the accuracy of data analysis. Machine learning algorithms detect patterns and anomalies that might be missed by human analysts. This precision is especially beneficial in financial analysis, ensuring that forecasts and valuations are based on accurate data, and reducing the potential risk of errors.
- Improved risk identification
AI algorithms can identify potential risks by analyzing vast amounts of data and detecting anomalies or issues. For example, they can flag inconsistencies in financial statements, unusual transaction patterns, or compliance issues.
Practical challenges and solutions
AI-powered due diligence also poses several challenges. Among those surveyed by Bain & Company, the biggest potential risks cited were:
Data inaccuracy
AI systems rely heavily on the quality of the data they process. Inaccurate or incomplete data can lead to incorrect analyses and flawed decision-making, which is particularly risky in M&A due diligence where precise evaluations are crucial.
Solution: Implement a comprehensive data management strategy with robust data validation and cleaning processes. Furthermore, conduct regular data audits to maintain data integrity and update AI models based on the latest, most accurate information.
Data privacy risk
The due diligence process often involves handling sensitive information about the target company, which can raise significant data privacy concerns, especially when dealing with personal data or proprietary information.
Solution: Adopt strict data privacy measures, including data anonymization techniques, compliance with relevant data protection regulations (e.g., GDPR, CCPA), and advanced access control measures to restrict access to sensitive data.
Cybersecurity risk
The adoption of due diligence AI increases the exposure to cybersecurity threats. Hackers could potentially exploit vulnerabilities to access sensitive data or disrupt the due diligence process.
Solution: Implement advanced cybersecurity measures such as encryption, multi-factor authentication, and intrusion detection systems. Regular security audits and penetration testing should be conducted to identify and address vulnerabilities.
High implementation costs
Implementing AI solutions can be expensive, requiring significant investment in technology, infrastructure, and skilled personnel. These high upfront costs can be a barrier for many organizations.
Solution: To mitigate high implementation costs, organizations can adopt a phased approach, starting with pilot projects to demonstrate AI’s value before scaling up. Additionally, leveraging cloud-based AI services can reduce infrastructure costs and provide scalability.
Integration with existing IT systems
Integrating AI tools with existing IT systems can be complex and challenging, often requiring significant changes to workflows and infrastructure. This integration process can be time-consuming and disruptive.
Solution: Adopt a modular integration approach, where AI components are gradually integrated into existing IT systems. Utilize APIs and middleware solutions to facilitate seamless communication between AI tools and legacy systems.
Consider reading our comprehensive article on the IT due diligence checklist to gain deeper insights into best practices and essential steps for effective due diligence.
Industry insights
Let’s explore real-life examples of companies using AI for M&A due diligence and other business processes.
JP Morgan COIN
JP Morgan developed COIN (Contract Intelligence), a machine learning-powered system, to automate the review of commercial loan agreements. COIN can analyze thousands of contracts in seconds, a task that previously required 360,000 hours of manual review by lawyers each year.
Its applications extend from commercial loan agreements to complex legal documents like credit-default swaps and custody agreements. This technology not only improves productivity and cost-effectiveness by freeing up lawyers for more strategic tasks but also enhances accuracy and compliance in contract management, significantly reducing human errors.
EY and Watson
Ernst & Young (EY) is transforming due diligence with EY Diligence Edge, integrating AI technology powered by IBM Watson. This platform consolidates and analyzes external data sources, including news, financial, and social media data, to provide M&A practitioners with strategic recommendations.
The platform features a “smart data room” powered by an M&A-specific AI model, enabling the analysis of hundreds or thousands of documents with exceptional depth and accuracy. This automation streamlines manual processes, allowing EY professionals to focus on value-added analysis and insights. Moreover, EY Diligence Edge presents findings through user-friendly charts and dashboards.
Deloitte Financial Advisory
Deloitte Financial Advisory implements AI to modernize Know Your Customer (KYC) and due diligence operations with the Diligence Insights (DI) Platform. This platform automates intelligence gathering, streamlining screening and verification processes. Using technologies like AI and Robotic Process Automation (RPA), it speeds up screening and sources data from public websites and proprietary databases.
The platform uses automation and cognitive technology to mimic human actions, discounting false positives, identifying negative news, and corroborating identities using facial recognition. Network analytics and visualization capabilities uncover potential conflicts of interest and hidden risk factors, offering a holistic risk profile view.
Key takeaways
- AI due diligence refers to the use of artificial intelligence (AI) technologies in the process of evaluating and analyzing companies during M&A or investment transactions. It involves leveraging AI algorithms to automate data collection, analysis, and risk assessment, allowing for faster and more accurate decision-making.
- Key functions of AI in due diligence include automated data collection, document review and analysis, and legal review.
- AI in acquisitions offers numerous advantages, including faster transaction speed, reduced manual effort, reduced cost, improved focus, greater accuracy, and improved risk identification.
- AI-powered due diligence also poses several challenges, including data inaccuracy, data privacy risk, cybersecurity risk, and high implementation costs.
Don’t hesitate to leverage AI for due diligence in M&A transactions. It enhances efficiency, accuracy, and insight, empowering you to make informed decisions swiftly and stay ahead in the competitive market.
