The Role Of Artificial Intelligence Due Diligence In Driving Mergers And Acquisitions In India’s Fintech Sector
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Category: Corporate & Insolvency
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THE ROLE OF ARTIFICIAL INTELLIGENCE DUE
DILIGENCE IN DRIVING MERGERS AND ACQUISITIONS IN INDIA’S FINTECH SECTOR
Pranjal Sinha, National Law Institute University
ABSTRACT
This study examines how artificial intelligence (AI) is revolutionizing the due diligence (DD) process for mergers and acquisitions (M&A) in India's rapidly growing fintech industry. It looks at the difficulties of conventional DD, current M&A trends, and how artificial intelligence (AI) tools like machine learning, predictive analytics, and natural language processing (NLP) are changing the game. In addition to discussing the advantages and difficulties relevant to India, including legislative issues like the Digital Personal Data Protection Act (DPDP Act, 2023), the article examines examples of AI in M&A DD from across the world. It ends with recommendations for the future of AI's influence on M&A tactics in this fastpaced industry and suggestions for Indian fintech firms.
Introduction
Artificial Intelligence (AI) due diligence in mergers and acquisitions (M&A) pertains to employing AI technologies to improve and simplify the investigation and assessment process of a prospective company. This involves assessing a company's AI resources, such as model clarity, data management, adherence to regulations, and ethical dangers, which are becoming more crucial to a firm's worth. Its importance in contemporary M&A stems from its capacity to analyze large datasets, detect risks and opportunities more effectively, minimize human error, and expedite deal timelines, thus facilitating better decision-making.1 This topic possesses considerable significance for the fintech sector in India, which is experiencing rapid growth. Projections indicate that the sector's revenue may reach $USD 180-
200 billion by the year 2030, increasing from $USD 20 billion in 2023, with India ranking third
globally in terms of the number of fintech enterprises and exhibiting a fintech adoption rate of 87%, significantly exceeding the global average (EY, 2025). This growth is propelled by governmental initiatives such as Digital India,2 robust Digital Public Infrastructure (DPI) encompassing UPI and Aadhaar,3 escalating smartphone penetration, and a technology-savvy population. However, the sector operates within a complex and evolving regulatory framework, which encompasses the recently instituted Digital Personal Data Protection (DPDP) Act, 2023, alongside regulations issued by the Reserve Bank of India (RBI) and other entities (ORF Online, 2025). Technological advancements, particularly in Artificial Intelligence (AI) and Machine Learning (ML), are fundamentally pivotal to fintech operations, facilitating functions such as fraud detection and credit evaluation.
4 As mergers and acquisitions are active within the industry, the application of AI in the due diligence process is crucial for navigating complexities and augmenting the value of transactions. The primary objective of this research is to analyze how AI is transforming M&A due diligence in India's fintech industry. The paper will: McKinsey & Company, Generative AI in M&A: Opportunities in M&A (May, 2024), https://www. mckinsey. com/~/media/mckinsey/business%20functions/m%20and%20a/our%20insights/gen%20a i%20opportunities%20in%20m%20and%20a/gen-ai-opportunities-in-m-and-a. pdf. 2Ministry of Electronics & Information Technology, Government of India, About Digital India, digitalindia. gov. in, https://www. digitalindia. gov. in/. 3 NITI Aayog, India's Digital Public Infrastructure: Accelerating Fintech Growth, NITI. gov. in (Feb. 2024), https://www. niti. gov. in/reports/DPI-fintech-growth.
4 PricewaterhouseCoopers, AI and Machine Learning in Financial Services: Transforming Risk Management, PwC. com (Jan. 2025), https://www. pwc. com/in/en/financial-services/fintech/ai-ml-risk-management. html.
Provide an overview of recent M&A trends and traditional DD challenges in Indian fintech. • Describe AI tools and technologies used in DD. • Analyze applicable case studies and examples. • Discuss the benefits and challenges of AI DD in the Indian fintech context. • Examine the impact of the regulatory environment. • Offer recommendations for fintech companies. • Present a future outlook for AI in fintech M&A. Overview of M&A in India’s Fintech Sector India's financial technology sector has emerged as a significant arena for mergers and acquisitions (M&A), propelled by various factors such as market consolidation, the quest for economies of scale, the assimilation of novel technologies and human capital, and the diversification into new consumer demographics. 5 In the year 2024, the fintech landscape experienced 153 M&A transactions with a cumulative valuation of $USD 2.
8 billion, thereby accentuating its vibrancy (PwC, 2024). During the preceding five-year period (approximately 2019-2024), the sector recorded 133 M&A activities involving 88 distinct entities, with the payments and enabling technologies sub-sectors demonstrating the highest levels of engagement (PwC, H1 2024). Significant transactions, albeit some encountering obstacles, have characterized the industry terrain. For example, PayU's acquisition of PaySense in 2020 for $USD 185 million (aimed at establishing a comprehensive digital lending platform) constituted a noteworthy strategic maneuver. More recently, the proposed acquisition of BillDesk by PayU, valued at $USD 4.
7 billion and announced in 2021, was ultimately rescinded in October 2022 due to the non-fulfillment of specific pre-conditions, thereby underscoring the intricate nature of large-scale consolidation within the fintech domain (Inc42, 2022; Economic Times, 2022). Although high-value and successful acquisitions of Indian fintech firms by larger entities were less frequently highlighted in public discourses during the
5 Ernst & Young, India Fintech M&A
The Next Wave of Consolidation, EY. com (Apr. 2024), https://www. ey. com/en_in/fintech-ma-report-2024. period from 2022 to 2024, particularly when juxtaposed with funding rounds or smaller and undisclosed transactions, the aggregate volume of deals remains noteworthy. Conventional due diligence methodologies in M&A are frequently beset by numerous challenges. These challenges encompass excessive time consumption, where manual examination of extensive documentation incurs significant costs (potentially accounting for up to 10% of the overall deal value in advisory fees), and a heightened vulnerability to human error or oversight. 6 The phenomenon of data overload is prevalent, complicating the extraction of pertinent information from irrelevant data.
Moreover, ensuring thorough regulatory compliance across various domains (including financial regulations, data privacy, and corporate law) within constrained timelines imposes substantial pressure on M&A teams. These constraints may culminate in incomplete risk evaluations, overlooked opportunities, and ultimately, a depreciation of value in the post-acquisition phase. AI in Due Diligence: Tools and Technologies Artificial Intelligence (AI) introduces a comprehensive array of tools and technologies that are fundamentally transforming the due diligence paradigm by rectifying the inefficiencies inherent in conventional methodologies. Natural Language Processing (NLP): NLP is essential for the examination of contracts.
AIenhanced instruments possess the capability to swiftly analyze and comprehend vast quantities of legal and financial documents, extracting essential clauses, recognizing irregularities, obligations, and potential risks such as change-of-control provisions or atypical indemnities. 7 This process automates a traditionally arduous segment of legal due diligence, thereby enhancing both precision and efficiency. Predictive Analytics: Through the examination of historical and contemporaneous data, predictive analytics can anticipate a target organization's prospective performance, evaluate potential market vulnerabilities, and simulate various post-merger scenarios. 8 Within the 6 Deloitte, Navigating the Labyrinth: Overcoming Traditional Due Diligence Hurdles, Deloitte. com (May 2024), https://www2. deloitte.
com/us/en/insights/topics/mergers-and-acquisitions/challenges-traditional-duediligence. html. 7 Kira Systems, The Power of NLP in Legal Due Diligence: A Contract Review Revolution, KiraSystems. com (accessed May 27, 2025), https://www. kirasystems. com/blog/nlp-legal-due-diligence. 8 Boston Consulting Group, Predictive Analytics in M&A: Sharpening the Edge in Dealmaking, BCG. com (June 2024), https://www. bcg. com/publications/2024/predictive-analytics-mergers-acquisitions. financial technology sector, this methodology can be utilized to forecast loan default probabilities, customer attrition rates, or the efficacy of new product integrations, thereby providing a more empirical foundation for valuation and strategic compatibility analysis.
Machine Learning (ML): ML algorithms demonstrate exceptional proficiency in recognizing patterns and anomalies within extensive datasets that may elude human analysts. In the realm of financial modeling, ML can amalgamate heterogeneous financial and non-financial data, yielding more precise valuations in contrast to traditional models, while adapting to intricate non-linear relationships. 9 Furthermore, ML is employed for advanced risk evaluation, fraud detection within the operations of the target entity, and sentiment analysis derived from news or social media platforms to assess brand reputation. These AI methodologies significantly enhance the efficiency of the due diligence process. The automation of repetitive tasks such as document organization and preliminary review can potentially diminish due diligence timelines by 50% or more.
The enhancement of accuracy is attributable to AI's capability to consistently apply established criteria and assimilate new data, thereby reducing human bias and error. The resulting cost savings are a direct outcome of abbreviated timelines and more effective resource distribution. Ultimately, AI augments decision-making by equipping M&A teams with more profound, expedited, and comprehensive insights into the target organization, thereby enabling a concentration on strategic considerations rather than laborious data processing.
10 Case Studies and Examples While publicly accessible, case studies specific to India that meticulously document the comprehensive application of artificial intelligence in the context of fintech mergers and acquisitions due diligence are still in the nascent stages of emergence; however, prevailing global trends and the capabilities articulated by Indian technology enterprises provide significant insights. The scarcity of such localized case studies inherently underscores a prospective area for further scholarly investigation and documentation as the integration of AI becomes more entrenched within the Indian mergers and acquisitions ecosystem.
9 Gartner, Machine Learning for Enhanced Financial Due Diligence in M&A, Gartner.com (Mar. 2025),
https://www.gartner.com/en/finance/trends/ml-financial-due-diligence-ma.
10 Accenture, AI in Due Diligence
Unlocking Efficiency, Accuracy, and Value, (Feb. 2025), https://www.accenture.com/us-en/insights/strategy/ai-due-diligence-value.
On a global scale, artificial intelligence is progressively permeating various sectors. For example, a recent scholarly investigation into the utilization of Generative AI by prominent consulting firms within the realm of mergers and acquisitions due diligence revealed that Generative AI is predominantly employed in facilitating documentation-related tasks, such as the structuring of reports, drafting of proposals, and summarization of text. Nevertheless, its deployment in fundamental financial analysis or direct risk assessment remains cautious, primarily due to apprehensions regarding accuracy (hallucinations), data privacy, and transparency, which underscores the notion that Generative AI is presently regarded as a tool to enhance rather than supplant human expertise. 11 Certain Indian technology service providers (e. g.
, iTech India) are proactively advocating for artificial intelligence solutions tailored for mergers and acquisitions, emphasizing their proficiencies in automated document review, natural language processing-driven contract analysis, risk identification, and even the automated generation of preliminary M&A documents such as teasers. This trend signifies an escalating acknowledgment and evolution of AI instruments specifically designed for mergers and acquisitions within India, notwithstanding the fact that particular instances of fintech M&A deployments remain proprietary or publicly undocumented. The insights gleaned from these global observations and local capability discourses are highly relevant to India's fintech sector.
They imply that the initial adoption of artificial intelligence in due diligence will likely concentrate on augmenting human capabilities, automating tasks characterized by high volume and low judgment, as well as enhancing the speed and comprehensiveness of analyses. As assurance in artificial intelligence tools increases and apprehensions regarding data security and accuracy are effectively mitigated, it is anticipated that their application in more critical dimensions of due diligence will broaden. Benefits and Challenges of AI Due Diligence in India’s Fintech Sector The integration of AI into due diligence provides significant advantages for mergers and acquisitions in India's fintech industry: • Accelerated Deal Processes: The automation of document evaluation, data processing, and
11 Harvard Business Review, How Generative AI Is Changing Mergers and Acquisitions, (Jan. 15, 2025),
https://hbr. org/2025/01/how-generative-ai-is-changing-mergers-and-acquisitions. report creation can notably shorten the duration of due diligence, thereby speeding up the entire M&A transaction timeline. 12 • Enhanced Risk Evaluation: AI is capable of detecting subtle trends, irregularities, and possible warning signs within extensive datasets (financial, operational, compliance-related) that human analysts might overlook, resulting in a more thorough and precise risk assessment of the target fintech. • Greater Precision in Recognizing Opportunities and Risks: AI tools can systematically review contracts, financial reports, and compliance documents, reducing human errors and biases, which enhances the accuracy in pinpointing both prospects for value creation and potential liabilities.
• Decrease in Costs: Shorter transaction timelines and more effective allocation of resources due to automation can lead to significant savings in consultancy fees and internal resource utilization. Challenges: Despite these benefits, there are several challenges that must be overcome for successful AI implementation in due diligence within India's fintech sector: • Regulatory Challenges (e. g. , India’s Digital Personal Data Protection Act): The DPDP Act, 2023, imposes strict requirements concerning the handling of personal data, including the necessity for explicit consent and the assurance of data integrity and security. 13 AI technologies employed in due diligence must adhere to these regulations, particularly when managing sensitive customer information from a target fintech.
The "black box" characteristic of certain AI models may also create issues regarding the transparency and accountability mandated by the Act. • Concerns about Data Privacy and Security: Fintech firms manage large volumes of sensitive financial and personal information. Implementing AI in due diligence requires robust data governance frameworks to avert breaches and ensure that data used for training and
12 KPMG, Accelerating M&A
The AI Advantage in Due Diligence Timelines, (Apr. 2025), https://advisory.kpmg.us/articles/2025/ai-advantage-ma-due-diligence.html. 13 Anu Tiwari, Sara Sundaram, Utkarsh Bhatnagar, Naman Lodha & Anushri Mandal, New Data Law – Financial Services Implications, CAM ( 24 August 2023), https://corporate.cyrilamarchandblogs.com/2023/08/fig-paperno-24-series-1-new-data-law-financial-services-implications/.
analysis
is anonymized as necessary and securely managed, especially if third-party AI tools or cloud services are utilized (EY India, 2025). Approximately 47% of financial planners in India express concerns regarding AI’s data privacy and cybersecurity risks (FPSB India, 2024). • Demand for AI Proficiency and Skilled Workforce: To effectively utilize AI in due diligence, professionals need to be knowledgeable not only about M&A processes but also about AI concepts. This encompasses understanding AI capabilities and limitations, interpreting AI-generated insights, and managing associated risks. A lack of such expertise can impede adoption. 14 • Possible Biases or Limitations in AI Systems: AI models are trained on historical data, and if this data contains biases (such as those in lending decisions), the AI may perpetuate or even exacerbate these biases.
AI may also "hallucinate" or produce inaccuracies if not configured correctly or if the data is flawed. Human oversight is essential to verify AI results and mitigate these issues. Regulatory Environment and Its Impact India's regulatory framework for fintech and artificial intelligence (AI) is evolving and significantly influences the incorporation of AI in mergers and acquisitions (M&A) due diligence. The key components include: • Data Protection Laws: The Digital Personal Data Protection (DPDP) Act of 2023 is essential. 15 Although its regulations are still in development, its core principles concerning consent, purpose limitation, data minimization, and security greatly affect the handling and processing of personal data during due diligence. Acquirers employing AI tools to analyze a target fintech's customer information must ensure compliance with these regulations.
The Act also addresses cross-border data transfers, which is pertinent if AI due diligence tools are utilized or managed outside of India. 16 14 Thomas H. Davenport & Steven M. Miller, Working with Ai: Real Stories of Human-Machine Collaboration 45-50 (2022).
15 The Digital Personal Data Protection Act, 2023, No. 22, Acts of Parliament, 2023 (India)
16 Nadia Narain, Artificial Intelligence, Real Risks
Legal Due Diligence Issues And Purchase Agreement Provisions Aimed At Managing AI Risk In M&A Transactions, MONDAQ ( March 28, 2025), https://www.mondaq.com/canada/new-technology/1603586/artificial-intelligence-real-risks-legal-due-diligenceissues-and-purchase-agreement-provisions-aimed-at-managing-ai-risk-in-ma-transactions.
Fintech-Specific Regulations (e. g. , RBI Guidelines): The Reserve Bank of India (RBI) has been proactive in regulating the fintech sector, providing guidelines on digital lending, payments, KYC/AML, and cybersecurity. While a comprehensive long-term AI usage policy from the RBI is not yet in place, it has recognized the pervasive application of AI/ML in areas like customer service, risk management, and KYC (The Digital Fifth, 2024). For AI applications in lending by fintech companies, the RBI (through its Working Group on Digital Lending) stresses the importance of explainability, transparency, and fairness, often referred to as the "duty of explanation". These principles are crucial when evaluating a target fintech’s AI system during due diligence. Additionally, RBI's data localization requirements for payment systems must be considered.
• AI Governance Policies: India has yet to establish a comprehensive AI-specific regulation similar to the EU AI Act. Nonetheless, the government's India AI Mission indicates a commitment to fostering AI development and integration. Current governance primarily derives from existing IT laws, data privacy regulations (DPDP Act), and sector-specific directives. The focus is on responsible AI practices, data governance, and risk mitigation, particularly concerning bias. Incorporating compliance into AI-driven due diligence processes is essential. This includes: • Ensuring that AI tools and procedures conform to the consent and data processing stipulations of the DPDP Act. • Confirming that the target fintech's AI systems (for instance, in credit scoring) meet the RBI's standards for fairness and transparency.
• Implementing strong data security protocols throughout the due diligence process to safeguard sensitive information accessed by AI tools. • Conducting algorithmic audits, when possible, to identify biases and verify the reliability of the AI models being assessed as part of the target's assets. 17
17 Martin Baumgartner, How AI will impact due diligence in M&A transactions (Jan. 17, 2024),
https://www.ey.com/en_ch/insights/strategy-transactions/how-ai-will-impact-due-diligence-in-m-and-atransactions.
Recommendations
for Fintech Companies Indian fintech firms aiming to incorporate AI into their M&A due diligence processes should consider the following practical recommendations: • Strategic Adoption of AI Tools: Begin by utilizing AI tools that specifically tackle current challenges in the due diligence process, such as natural language processing for contract analysis or machine learning for identifying anomalies in extensive financial datasets. Consider creating or obtaining customized AI solutions that are possibly refined with industry-specific data to enhance precision and contextual comprehension (EY India, 2025 - regarding bespoke LLM for BFSI). Clearly define the goals for AI implementation and set metrics to evaluate return on investment and overall effectiveness. • Investment in AI Education and Training: Equip M&A teams with the skills to effectively engage with AI technologies.
This training should cover the use of AI tools, interpretation of their results, understanding their limitations, and recognizing potential biases. Cultivate a collaborative environment where AI complements human expertise rather than seeks to replace it. • Emphasize Regulatory Compliance and Data Management: Integrate adherence to the DPDP Act and other pertinent regulations (RBI, SEBI) into AI-driven due diligence processes from the beginning. Establish robust data governance protocols, including data mapping, access restrictions, and security measures, particularly when handling sensitive personal and financial information. Ensure that third-party AI providers also comply with these rigorous compliance and security requirements.
Address Risks Linked to AI Implementation: • Human Oversight: Ensure substantial human oversight to verify AI-generated insights, particularly for significant decisions. Seasoned professionals should evaluate AI results for context, subtleties, and importance. • Bias Recognition and Mitigation: Remain vigilant about potential biases in AI algorithms and the training data. Take proactive steps to identify and reduce such biases to guarantee fair and accurate evaluations.
Vendor Due Diligence: When utilizing third-party AI solutions, perform comprehensive due diligence on the vendors, focusing on their technology, security practices, data management protocols, and adherence to regulations. • Pilot Initiatives: Initiate pilot projects to evaluate AI tools in a controlled setting before fully integrating them into live M&A transactions. Future Outlook Artificial Intelligence is set to significantly transform M&A strategies within India's fintech industry over the next few years. • Technological Progress: The advancements in AI, especially Generative AI and sophisticated predictive analytics, are expected to develop at a rapid pace. GenAI is likely to extend its capabilities beyond merely summarizing documents to aiding in the preparation of initial reports, uncovering intricate patterns in unstructured data, and even simulating negotiation scenarios.
18 Enhanced analytics will provide more refined risk modeling and synergy evaluations. Although quantum computing remains on the horizon, it has the potential to greatly enhance AI's analytical capabilities. • Market Trends: As India's fintech sector evolves, there will likely be an increase in M&A activities aimed at consolidation, acquiring new capabilities, and entering new markets. AIenhanced due diligence will emerge as a key competitive advantage, allowing companies to perform diligence more swiftly and comprehensively. The pressure of global competition may also compel Indian fintech firms to embrace advanced AI technologies to align with international standards in M&A practices.
According to Bain & Company, while GenAI is currently utilized for generating ideas and reviewing data, the development of proprietary tools utilizing owned data will be essential for standing out in the market (Bain & Company, 2024). • Potential Changes in Regulatory Frameworks: Regulatory bodies, both globally and in India, are likely to create more detailed guidelines or frameworks regarding the use of AI in financial services, including M&A. This may include requirements for AI model transparency, 18 Chris O'Leary and Raees Nakhuda, How AI for M&A due diligence is changing every aspect of the deal process ( April 16, 2023), https://legal. thomsonreuters. com/en/insights/articles/how-ai-and-document-intelligence-arechanging-the-legal-tech-game.
auditability, and bias reduction. The complete enactment of the DPDP Act and any future modifications or specific industry codes will heavily influence the application of AI in handling personal data during due diligence. The implications for the competitiveness and expansion of India's fintech sector in the long term are significant. Companies that successfully incorporate AI into their M&A strategies will be in a stronger position to pinpoint valuable targets, reduce risks, and achieve enhanced success after mergers. This will lead to a more efficient, innovative, and resilient fintech ecosystem in India. Nonetheless, the focus will remain on AI as a supportive tool rather than a replacement for experienced M&A professionals.
Conclusion The incorporation of Artificial Intelligence into the due diligence process marks a significant shift for mergers and acquisitions within India's vibrant fintech landscape. AI tools such as NLP, predictive analytics, and machine learning provide considerable benefits by simplifying complex analyses, improving risk detection precision, cutting costs, and hastening deal timelines. These advantages are vital in a rapidly consolidating and changing market like India's fintech sector. Nevertheless, fully realizing the potential of AI in M&A due diligence depends on overcoming several challenges.
These include navigating the complexities of India's data protection regulations, particularly the DPDP Act of 2023, ensuring data privacy and security, promoting AI literacy among M&A professionals, and addressing the inherent biases and limitations of AI tools through careful human oversight. In the future, we can expect to see more advanced AI applications, including Generative AI, taking on an even larger role. For Indian fintech firms, the strategic adoption of AI in due diligence, investment in skilled personnel, and strict adherence to compliance will be essential for maintaining a competitive advantage and achieving successful M&A results. A key area for further exploration is the documentation and examination of India-specific case studies where AI has been effectively employed in fintech M&A due diligence.
Such studies would offer invaluable practical insights and benchmarks for the industry, transitioning from theoretical advantages to demonstrating real-world applications and lessons learned within the specific Indian context. This will further hasten the adoption and enhancement of AI-driven M&A strategies, contributing to the ongoing growth and sophistication of India's fintech ecosystem.