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Article by Dara Kelly, Partner, Co- lead Advisory, and George Zairis, Senior Manager, Advisory at Grant Thornton Luxembourg, as published in Insight/Out magazine #33
Following Morningstar DBRS and Fitch Ratings, S&P Global Ratings confirmed Luxembourg’s ‘AAA’ rating with a stable outlook, which is a tangible proof that Luxembourg remains a key financial hub in Europe and continues to strengthen its position in Private Equity and Deal Advisory services. In a world that the complexity of the transactions keeps increasing and regulatory scrutiny has become part of our day-to-day life, firms must leverage cutting-edge technologies to maintain a competitive edge and advantage. Artificial Intelligence (AI) has emerged as a crucial element, which is contributing to the efficiency, accuracy, and strategic depth of deal advisory and overall advisory services. While AI is not and will never be a replacement for human expertise, its ability to process vast amounts of data, identify risks, and refine valuations is reshaping how deals are structured and executed.
AI in Due Diligence: The way to a faster and more reliable analysis
Due diligence can be a critical step in any transaction, requiring an in-depth assessment of financials as well as legal risks and operational performance. This involves gaining a strong understanding of the target’s corporate structure, evaluating the industry landscape in which it operates, conducting a preliminary financial analysis to identify potential obstacles to the transaction, and determining the strategic implications of the deal on the target’s operations. Traditionally and undeniable, this process has been time-consuming, involving manual document reviews and fragmented data sources. AI-powered tools are now transforming due diligence by automating document review as natural language processing enables AI to analyze contracts, regulatory filings, and financial statements at scale, flagging inconsistencies and compliance risks. At the same time, identifying financial irregularities and inconsistencies becomes easier with the help of machine learning algorithms, which can detect irregularities in financial statements that may indicate hidden risks, such as revenue manipulation or cost misstatements. From time-efficiency perspective, recent research shows that AI-driven pre-screening tools enable dealmakers to evaluate a larger pool of potential targets in less time, contributing to a better-informed investment decision. Additionally, by automating repetitive tasks – human error can be reduced and attention will be solely on the decision-making, allowing dealmakers to focus on strategic evaluation. Last but not least, from cost efficiency perspective, AI minimizes the need for extensive manual effort, making due diligence more cost-efficient and accessible to mid-sized firms as well.
Improving Accuracy and Objectivity by using AI in the valuation assessment
It is an undeniable fact that valuation remains one of the most challenging aspects of deal advisory, often relying on subjective assumptions and market trends that can shift rapidly – especially with the uncertainty of the current macroeconomic and geopolitical global landscape – even in a country such as Luxembourg, with its tradition of stability and growth. Reliable valuations are essential for companies and Private Equity firms, serving as a foundation for transactions, tax assessments, financial reporting, and accounting purposes. Boards, shareholders, investors, lenders, transaction partners, and regulatory authorities must be able to trust the robustness of valuations to make accurate decisions.
Let’s think about the full potential of an AI-driven valuation model in terms of precision by integrating real-time market data, as they process vast datasets, including industry trends, economic indicators, and public/private market comparable in order to improve valuation accuracy. Additionally, machine learning models could perform scenario analysis and stress testing by simulating multiple economic and business conditions, providing more dynamic and risk-adjusted valuations. Traditional valuation methods, such as DCF and comparable company analysis, can be refined by AI, which continuously updates assumptions based on evolving market dynamics at both macro and micro level. AI-driven analytics can also help in the elimination of cognitive bias, ensuring valuations remain data-driven and mitigating the risk of over-optimistic or conservative estimates.
Optimizing the Deal Sourcing and Risk Assessment
The Luxembourg market is becoming increasingly competitive, making efficient deal sourcing and risk assessment more critical than ever.
The role of AI will be to set the foundations in order to identify high-potential targets through algorithms that scan financial markets data, corporate announcements, and M&A trends so they can detect acquisition opportunities before they become widely known. It also improves risk profiling by leveraging AI-driven risk assessment models to evaluate a company’s financial health, industry position, and macroeconomic exposure, providing a more comprehensive and detailed risk profile. Additionally, AI will contribute to a higher quality ESG due diligence by assessing sustainability metrics, regulatory adherence, and reputational risks more efficiently than manual reviews, ensuring a more thorough and data-driven evaluation process.
Challenges and Considerations in AI Adoption
While AI can have numerous advantages, financial institutions- and not only – have to be able to navigate and address several challenges when integrating AI into deal advisory processes. Ensuring compliance with Luxembourg’s and EU’s stringent data protection laws is critical when leveraging AI analytics, as firms must navigate complex regulatory frameworks to protect sensitive data. AI’s role in decision-making must align with financial regulations to maintain transparency and accountability, ensuring that technology is used responsibly. Despite AI’s capabilities, human expertise remains and will always remain essential, as it the crucial component of the decision-making process. Moreover, AI adoption requires significant investment in technology, training, and system integration, making implementation costs a crucial consideration for firms looking to leverage AI effectively. Finally, the ethical use of AI is becoming a focal point of debate within the industry as it requires- and will require even more in the future – alignment with established guidelines to prevent biases and ensure fair decision-making in the advisory process.
Conclusion
We are more than convinced that market leaders such as banks, advisory firms and asset managers, which are looking to maintain a competitive edge in Luxembourg’s evolving financial landscape are realizing that the integration of AI in deal advisory is no longer optional.
While it is an undeniable fact that AI can contribute to the efficiency and precision, human expertise remains indispensable in interpreting complex transactions and strategic decision-making. Firms that successfully integrate AI into their deal advisory processes will benefit from faster, more data-driven decisions, reduced risk exposure, and enhanced deal outcomes. As regulatory frameworks evolve, ensuring AI compliance and ethical use will be key considerations for the industry moving forward. By embracing AI, Luxembourg’s deal advisory market can continue to set high standards for efficiency, transparency, and investment success in the European financial sector.
