Optimizing Due Diligence with AI: A Comparative Analysis of Investment Outcomes in Technology-Enabled Private Equity
DOI:
https://doi.org/10.32628/IJSRST251222732Keywords:
Artificial Intelligence (AI), Due Diligence, Private Equity (PE), Investment Outcomes, Risk Management, Data IntegrationAbstract
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.
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References
R. Ippolito, Private capital investing: the handbook of private debt and private equity. John Wiley & Sons, 2020.
S. Zambelli, "Due Diligence in Private Equity," in The Palgrave Encyclopedia of Private Equity: Springer, 2024, pp. 1-10.
M. Sharma and E. Prashar, "Private Equity Due Diligence," Private Equity: Opportunities and Risks, vol. 290, 2015.
M. Farboodi and L. Veldkamp, "Long-run growth of financial data technology," American Economic Review, vol. 110, no. 8, pp. 2485-2523, 2020.
F. Corea, Artificial intelligence and exponential technologies: Business models evolution and new investment opportunities. Springer, 2017.
L. Rodríguez-Mazahua, C.-A. Rodríguez-Enríquez, J. L. Sánchez-Cervantes, J. Cervantes, J. L. García-Alcaraz, and G. Alor-Hernández, "A general perspective of Big Data: applications, tools, challenges and trends," The Journal of Supercomputing, vol. 72, pp. 3073-3113, 2016.
S. Paleti, J. Singireddy, A. Dodda, J. K. R. Burugulla, and K. Challa, "Innovative Financial Technologies: Strengthening Compliance, Secure Transactions, and Intelligent Advisory Systems Through AI-Driven Automation and Scalable Data Architectures," Secure Transactions, and Intelligent Advisory Systems Through AI-Driven Automation and Scalable Data Architectures (December 27, 2021), 2021.
O. Bajulaiye, M. Fenwick, I. Skultetyova, and E. P. Vermeulen, "Digital transformation in the hedge fund and private equity industry," Lex Research Topics in Corporate Law & Economics Working Paper, no. 2020-1, 2020.
F. Halper, "Advanced analytics: Moving toward AI, machine learning, and natural language processing," TDWI Best Practices Report, 2017.
P. Ghavami, Big data analytics methods: analytics techniques in data mining, deep learning and natural language processing. Walter de Gruyter GmbH & Co KG, 2019.
L. Wanless, C. Seifried, A. Bouchet, A. Valeant, and M. L. Naraine, "The diffusion of natural language processing in professional sport," Sport Management Review, vol. 25, no. 3, pp. 522-545, 2022.
T. Shaik et al., "A review of the trends and challenges in adopting natural language processing methods for education feedback analysis," Ieee Access, vol. 10, pp. 56720-56739, 2022.
E. J. Go, J. Moon, and J. Kim, "Analysis of the current and future of the artificial intelligence in financial industry with big data techniques," Global Business & Finance Review (GBFR), vol. 25, no. 1, pp. 102-117, 2020.
N. Yang, "Financial big data management and control and artificial intelligence analysis method based on data mining technology," Wireless Communications and Mobile Computing, vol. 2022, no. 1, p. 7596094, 2022.
C.-H. Wang, "Considering economic indicators and dynamic channel interactions to conduct sales forecasting for retail sectors," Computers & Industrial Engineering, vol. 165, p. 107965, 2022.
V. S. Kumar, "Artificial Intelligence in Economic Analysis: An Overview of Techniques, Applications and Challenges," Asian Journal of Economics, Finance and Management, pp. 388-396, 2024.
Z. B. Yusof, "Analyzing the Role of Predictive Analytics and Machine Learning Techniques in Optimizing Inventory Management and Demand Forecasting for E-Commerce," International Journal of Applied Machine Learning, vol. 4, no. 11, pp. 16-31, 2024.
N. Rane, S. Choudhary, and J. Rane, "Artificial intelligence and machine learning in business intelligence, finance, and e-commerce: a review," Finance, and E-commerce: a Review (May 27, 2024), 2024.
V. Kumar, "CONTEMPLATING THE ROLE OF ARTIFICIAL INTELLIGENCE IN LEGAL FIELD," ACTA SCIENTIAE, vol. 7, no. 2, pp. 166-180, 2024.
R. Chandra and K. Sanjaya, Artificial Intelligence and Law. Academic Guru Publishing House, 2024.
T. Sewandono, "Auditing Artificial Intelligence," Delft University of Technology, 2023.
M. Taleb and H. J. Kadhum, "The role of artificial intelligence in promoting the environmental, social and governance (ESG) practices," in International Conference on Explainable Artificial Intelligence in the Digital Sustainability, 2024: Springer, pp. 256-279.
M. A. Bedekar, M. Pareek, S. S. Choudhuri, P. Abhishek, J. Jhurani, and R. Shah, "AI in Mergers and Acquisitions: Analyzing the Effectiveness of Artificial Intelligence in Due Diligence," in 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS), 2024, vol. 1: IEEE, pp. 1-5.
S. A. Mangaldas, "AI in Mergers and Acquisitions: Analyzing the Effectiveness of Artificial Intelligence in Due Diligence," 2020.
A. Leogrande, "Unlocking Hidden Value: A Framework for Transforming Dark Data in Organizational Decision-Making," 2024.
Y. S. Balcıoğlu, "Revolutionizing Risk Management AI and ML Innovations in Financial Stability and Fraud Detection," in Navigating the Future of Finance in the Age of AI: IGI Global, 2024, pp. 109-138.
S. Feng, "Integrating artificial intelligence in financial services: Enhancements, applications, and future directions," Applied and Computational Engineering, vol. 69, pp. 19-24, 2024.
C. J. Lin, "AI-Augmented Targeting and Reining in the Law of the Horse," Penn St. L. Rev., vol. 129, p. 483, 2024.
A. NEGRINI, "The integration of ChatGPT in corporate foresight practices: a comparative analysis of traditional and AI-augmented scenario generation in the healthcare domain," 2023.
P. Wolodko, "The Future of Business Valuation: How Technological Advancements are Influencing the Valuations Industry," Universidade Catolica Portuguesa (Portugal), 2024.
C. Tores, "Evaluating SBIR Proposals: A Comparative Analysis using Artificial Intelligence and Statistical Programming in the DoD Acquisitions Process," Acquisition Research Program, 2024.
B. Rai, "Transformative Evolution: A Comprehensive Study on FinTech’s Transformation with Emphasis on Leveraging AI Copilot for UX Enhancement."
O. Sanchez, "The Role of Artificial Intelligence in Investment Decision Making: A Study of Senior Management Perceptions within Private Equity and Venture Capital Firms," Dublin, National College of Ireland, 2020.
M. LIEPERT, "Leveraging Technology: Enhancing Operations and Boosting EBITDA in Private Equity Owned Portfolio Companies," Theoretical and Practical Research in Economic Fields (TPREF), vol. 15, no. 2 (30), pp. 186-195, 2024.
R. Xu and D. Zhao, Digital Transformation of Private Equity in China. Springer, 2023.
R. Ejjami, "AI-driven justice: Evaluating the impact of artificial intelligence on legal systems," Int. J. Multidiscip. Res, vol. 6, no. 3, pp. 1-29, 2024.
J. Xiao, J. Wang, W. Bao, T. Deng, and S. Bi, "Application progress of natural language processing technology in financial research," Financial Engineering and Risk Management, vol. 7, no. 3, pp. 155-161, 2024.
M. Antoncic, "Uncovering hidden signals for sustainable investing using Big Data: Artificial intelligence, machine learning and natural language processing," Journal of Risk Management in Financial Institutions, vol. 13, no. 2, pp. 106-113, 2020.
M. Chui and S. Francisco, "Artificial intelligence the next digital frontier," McKinsey and Company Global Institute, vol. 47, no. 3.6, pp. 6-8, 2017.
A. Ashta and H. Herrmann, "Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance," Strategic Change, vol. 30, no. 3, pp. 211-222, 2021.
H. S. Ayoub and H. Y. Aljuhmani, "Artificial Intelligence Capabilities as a Catalyst for Enhanced Organizational Performance: The Importance of Cultivating a Data-Driven Culture," in Achieving Sustainable Business Through AI, Technology Education and Computer Science: Volume 2: Teaching Technology and Business Sustainability: Springer, 2024, pp. 345-356.
Z. Zong and Y. Guan, "AI-driven intelligent data analytics and predictive analysis in Industry 4.0: Transforming knowledge, innovation, and efficiency," Journal of the Knowledge Economy, pp. 1-40, 2024.
N. K. Rajagopal et al., "Future of business culture: An artificial intelligence‐driven digital framework for organization decision‐making process," Complexity, vol. 2022, no. 1, p. 7796507, 2022.
E. Anton, T. D. Oesterreich, M. Aptyka, and F. Teuteberg, "Beyond digital data and information technology: conceptualizing data-driven culture," Pacific Asia Journal of the Association for Information Systems, vol. 15, no. 3, p. 1, 2023.
R. Dubey, D. J. Bryde, Y. K. Dwivedi, G. Graham, and C. Foropon, "Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view," International Journal of Production Economics, vol. 250, p. 108618, 2022.
K. Manheim and L. Kaplan, "Artificial intelligence: Risks to privacy and democracy," Yale JL & Tech., vol. 21, p. 106, 2019.
J. Paul, "Privacy and data security concerns in AI," 2024.
G. O. Mbah, "Data privacy in the era of AI: Navigating regulatory landscapes for global businesses," 2024.
S. Joseph, T. M. Kolade, O. Obioha Val, O. O. Adebiyi, O. S. Ogungbemi, and O. O. Olaniyi, "AI-powered information governance: Balancing automation and human oversight for optimal organization productivity," Asian Journal of Research in Computer Science, vol. 17, no. 10, p. 10.9734, 2024.
G. Nagar, "Leveraging Artificial Intelligence to Automate and Enhance Security Operations: Balancing Efficiency and Human Oversight," Valley International Journal Digital Library, pp. 78-94, 2018.
S. H. Ivanov, "Automated decision-making," foresight, vol. 25, no. 1, pp. 4-19, 2023.
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