Impact of AI in cross-border mergers and acquisitions transactions

The integration of Artificial Intelligence (AI) in cross-border Mergers and Acquisitions (M&A) is rapidly transforming the deal-making landscape. From identifying investment opportunities to conducting risk assessments and accelerating transaction timelines, AI is revolutionising how global companies approach M&A strategies. However, its adoption must be complemented by human expertise and robust regulatory oversight to mitigate risks such as data inaccuracies and security breaches.

AI is revolutionising M&A transactions by streamlining processes, improving accuracy, and enhancing strategic decision-making in the following aspects:

• Due diligence: One of its most significant contributions is in due diligence, where AI-powered tools rapidly review thousands of legal and financial documents, identifying key clauses, risks, and obligations with greater precision than manual review. By automating contract analysis and highlighting red flags such as regulatory non-compliance and pending litigations, AI enables dealmakers to conduct faster and more thorough risk assessments.
• Valuation and financial analysis: In valuation and financial analysis, AI enhances traditional models by analysing large datasets, historical financials, and market trends to provide accurate projections. Machine learning algorithms improve the reliability of earnings forecasts, synergy potential, and integration costs while offering deeper comparative insights across industries and geographies. This allows M&A teams to make data-driven decisions with better financial clarity.
• Assessment of success probability: AI-powered predictive analytics helps in assessing the success probability of deals by analysing past transactions, market behaviour, and potential integration challenges. It forecasts post-merger risks such as cultural mismatches and operational inefficiencies while identifying areas for cost savings and revenue growth. This enables companies to optimise deal structuring and mitigate risks before finalising the transaction.
• Target identification and deal sourcing: In target identification and deal sourcing, AI scans vast amounts of structured and unstructured data to find acquisition opportunities that align with an acquirer's strategic objectives. AI evaluates financial performance, competitive positioning, and industry trends to highlight promising targets. It can also predict distressed companies, high-growth startups, or undervalued assets, improving the efficiency and precision of deal sourcing.
• Negotiation and regulatory compliance: AI plays a crucial role in negotiation and regulatory compliance by analysing historical deal terms, pricing patterns, and counterparty behaviours to optimise negotiation strategies. It also tracks evolving global regulations, ensuring compliance with competition laws, tax requirements, and industry-specific mandates. AI automates regulatory filings, reducing the administrative burden and minimising the risk of errors in reporting.
• Seamless integration post-merger: Post-merger, AI facilitates seamless integration by identifying operational bottlenecks, redundancies, and potential cultural conflicts. AI tools analyse workforce sentiment to help align employee expectations, while machine learning models optimise supply chains and IT systems to enhance efficiency. Customer analytics powered by AI further assist in retaining market share and improving brand positioning post-merger.

For cross-border M&A, AI simplifies complex multi-jurisdictional transactions by monitoring foreign regulations, translating legal documents, and assessing geopolitical risks. By analysing political and economic conditions in target markets, AI helps companies navigate international expansions with greater confidence. Additionally, AI-driven sentiment analysis evaluates public perception and potential regulatory hurdles before deal execution.

Impact of India's data protection framework:

As AI-driven deal-making gains traction in cross-border M&A, regulatory frameworks in India will need to evolve to address the unique challenges posed by AI-powered transactions. India's Digital Personal Data Protection Act, 2023 (DPDP Act) is a landmark legislation aimed at protecting personal data while ensuring a balanced approach to data-driven innovation. The Act imposes obligations on entities processing personal data, including obtaining consent, ensuring data minimisation, and implementing security safeguards. For AI-driven M&A, this means companies leveraging AI tools for due diligence, customer data analysis, and compliance automation must adhere to stringent data protection norms. Additionally, the DPDP Act introduces cross-border data transfer regulations, which could impact AI models trained on sensitive financial and personal data. The government holds the power to restrict data transfers to certain jurisdictions, potentially affecting AI-based global deal sourcing and valuation models in M&A transactions. However, despite its comprehensive framework, the DPDP Act does not yet provide AI-specific regulations, such as guidelines on automated decision-making, AI accountability, or ethical AI development. Future amendments may introduce AI governance frameworks, including transparency obligations for AI systems.

Potential risks and challenges:

Despite AI's potential to transform M&A, there are certain inherent risks and limitations:

• Hallucinations: AI-generated errors, known as 'hallucinations,' have led to significant financial and legal miscalculations. In one instance, Microsoft's AI-powered Bing search engine provided incorrect financial comparisons between two retail giants, demonstrating AI's vulnerability to inaccuracies.
• Fictitious citations: Legal professionals have faced repercussions for relying on AI-generated legal documents containing fictitious citations. Such instances highlight the need for AI-generated outputs to be thoroughly vetted by human experts before implementation in critical M&A processes.
• Data security: Data security concerns remain a pressing issue. AI systems require access to vast amounts of confidential and sensitive data, raising risks of unintended data leaks. The European Union (EU) has responded by enacting the EU Artificial Intelligence Act, 2024, the first comprehensive AI regulation, set to take full effect in 2026. Similar regulatory frameworks are expected to emerge globally to ensure AI's responsible use in financial transactions.

The way forward:

The adoption of AI in M&A is still in its early stages, but it is rapidly gaining traction. According to a recent survey by Bain & Company of over 300 M&A professionals, the use of generative AI in deal-making has increased to 21% from 16% in 2023¹, with one in three professionals anticipating its adoption by year-end. As AI technology continues to evolve, its impact on global deal-making will become even more pronounced, particularly in cross-border transactions, where AI-driven insights are already reshaping how companies identify and evaluate acquisition targets.

AI is revolutionising the way companies, particularly in the IT sector, identify potential M&A targets. Global technology giants like IBM and Accenture are leveraging AI-driven insights to acquire Indian IT and SaaS firms, enhancing their technological and consulting capabilities.

AI has significantly accelerated deal sourcing, enabling companies to identify acquisition targets aligned with their strategic expansion goals. Platforms like GrowthPal function as AI-powered investment bankers, providing buyers with potential acquisition options within just two weeks. This efficiency has led to a rise in micro-fit M&As, bolt-on acquisitions, and cross-border deals, particularly in the IT services sector. Indian IT firms are increasingly acquiring small- to mid-sized IT services companies worldwide to access new clients, expand their geographic footprint, build onshore and nearshore teams, and strengthen their global presence.

Other laws may also require amendments to account for AI's role in deal analysis, decision-making, and compliance automation. The Competition Commission of India (CCI) may need to refine its evaluation criteria to address AI-generated market concentration forecasts and predictive risk assessments. The Securities and Exchange Board of India (SEBI) may need to introduce stricter disclosure norms for AI-assisted transactions to maintain transparency and prevent algorithmic biases in deal structuring. Tax authorities could also revisit existing frameworks to assess how AI-driven valuations and transfer pricing models influence cross-border taxation. As AI adoption in M&A rises, a balanced regulatory approach will be essential – one that fosters innovation while ensuring compliance, fair competition, and investor protection.

AI is fundamentally reshaping cross-border M&A by enhancing efficiency, precision, and strategic foresight. As platforms like GrowthPal demonstrate, AI-driven deal sourcing and due diligence are accelerating transactions while enabling businesses to pursue frequent, lower-risk acquisitions. With generative AI adoption in M&A expected to reach new heights in the coming years, its role in automating negotiations, compliance, and post-merger integration will only expand. However, while AI unlocks new opportunities and reduces transaction costs, its limitations – such as hallucinations, algorithmic biases, and data privacy concerns – necessitate stringent human oversight and regulatory adaptation. India's evolving legal framework, particularly the DPDP Act, must evolve to balance innovation with compliance. A forward-looking regulatory approach will be essential to harness AI's full potential while safeguarding transparency, competition, and investor confidence in global deal-making.

Footnotes:

¹Generative AI in M&A: You're Not Behind—Yet, 4 February 2025, Jeff Haxer, Maja Omanovic, Ben Siegal, and Brooke Houston, https://www.bain.com/insights/generative-ai-m-and-a-report-2025/