Optimizing Due Diligence with AI: A Comparative Analysis of Investment Outcomes in Technology-Enabled Private Equity

Aadit Sharma, Bolaji Iyanu Adekunle, Jeffrey Chidera Ogeawuchi, Abraham Ayodeji Abayomi and Omoniyi Onifade

International Journal of Scientific Research in Science and Technology, 2024, vol. 11, issue 2, 1082-1092

Abstract: This paper explores the transformative impact of artificial intelligence (AI) on due diligence processes within private equity (PE) firms, focusing on its role in optimizing investment outcomes. Traditional due diligence practices in PE, often marked by time-consuming manual assessments and subjective decision-making, are increasingly being supplemented or replaced by AI-enabled tools that enhance efficiency, accuracy, and the ability to process large datasets. This study conducts a comparative analysis of investment performance, contrasting AI-driven due diligence with conventional approaches. By examining the application of AI in financial analysis, legal review, and data integration, the paper highlights AI’s ability to detect anomalies, predict revenue, and mitigate risks, ultimately leading to more informed and timely investment decisions. The paper also explores the strategic and operational implications of AI adoption in PE firms, including the shift in skillsets, organizational workflows, and risk management practices. While AI brings significant advantages in terms of decision-making speed and precision, the paper emphasizes the need for transparent governance, human oversight, and responsible AI deployment. Finally, the paper offers policy recommendations for PE firms to integrate AI technologies, balancing innovation with ethical considerations effectively. The findings demonstrate that AI is not only revolutionizing due diligence but is also positioning private equity firms for more robust and data-driven investment strategies.

Keywords: Artificial Intelligence (AI); Due Diligence; Private Equity (PE); Investment Outcomes; Risk Management; Data Integration (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v11:y2024:i2:id:840

DOI: 10.32628/IJSRST251222732

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