In the realm of mergers and acquisitions (M&A), due diligence is a critical phase where a buyer’s acquisition team assesses potential risks and opportunities before finalizing the terms of an agreement to purchase its target company. The diligence process can be labor-intensive and time-consuming, involving extensive manual review of the target company’s governing documents, financial statements, indebtedness, contracts, and other pertinent data. To help make this process more approachable, dealmakers in the M&A realm are exploring how the integration of certain artificial intelligence (AI) driven tools can yield information that is more efficient, accurate, and insightful.
1. Accelerating Data Analysis
Due diligence often requires sifting through vast amounts of information. To reduce the time required to review each document manually, utilizing AI can help to more quickly identify patterns, anomalies, and trends in a target company’s documents. For example, natural language processing algorithms can analyze text-heavy documents to extract vital information and summarize important findings, which leaves the buyer’s acquisition team with more time to devote to other key pieces of the transaction.
2. Enhancing Risk Assessment
Analyzing the buyer’s risks of purchasing its target company’s business is crucial during the diligence process, and with the assistance of AI technologies, various risk factors can be identified earlier. Machine learning algorithms, for instance, are becoming more proficient in picking up financial inconsistencies, regulatory compliance issues, and potential legal liabilities. Spotting risks early in the diligence process better allows the acquisition team to address potential issues before they become significant problems, leading to more informed decision-making and stronger negotiation strategies.
3. Automating Routine Tasks
Automating routine tasks, such as data entry and document management, using AI can also free up valuable time for a buyer’s acquisition team to focus on more strategic activities while still getting accurate and reliable results free of potential human error. Specifically, popular robotic process automation tools are equipped to handle repetitive tasks, such as extracting data from financial statements or cross-referencing information across multiple sources.
4. Improving Financial Analysis
AI-driven financial analysis tools are particularly revered because these tools are performing advanced financial modeling and forecasting with greater precision and speed to help provide buyers and investors with a clearer picture of a target company’s financial health. Certain AI algorithms, in particular, can create detailed financial projections and valuations from an analysis of financial statements, historical performance data, and market trends.
5. Enhancing Data Security
Data security is becoming an increasing concern during due diligence, particularly in an age where sellers exchange their documents with buyers via the use of virtual data rooms. Machine learning algorithms can help alleviate these concerns by identifying unusual patterns of behavior and flagging potential security threats in real-time, ensuring that sensitive information remains protected throughout the diligence process.
Conclusion
The use of AI in M&A due diligence can alter the way dealmakers approach this crucial phase of the acquisition process, leading to more streamlined processes and better-informed decisions. As this technology continues to advance, companies and advisors that operate within the dealmaking space should consider exploring whether certain AI tools can be integrated into its existing M&A due diligence procedures.
