The Rise of AI in Due Diligence and Private Debt Markets

Private debt has emerged as one of the fastest-growing asset classes in the global finance sector, with projections indicating it will exceed US$2.3 trillion by 2027, as reported by Investment News. With this growth comes mounting complexity, larger deal volumes, increasingly intricate capital structures, and heightened regulatory expectations. Traditional due diligence practices, which often rely on spreadsheets, manual assessments, and disjointed data sources, find it challenging to keep up with the speed and sophistication investors now require.

Artificial intelligence (AI) is filling this void. By automating repetitive tasks, processing unstructured data on a large scale, and identifying risks in near real-time, AI transforms how investors conduct diligence and evaluate credit. Crucially, AI is not merely about improving efficiency; it is becoming a key differentiator in a competitive private debt landscape, where the capacity to evaluate risks more quickly and accurately can significantly influence the success of deals.

Speed and Scale: From Weeks to Hours 

AI’s most immediate impact is on speed and scale. Companies can assess more opportunities without compromising quality by condensing weeks of manual labour into just a few hours. The following examples illustrate how investment firms are compressing diligence cycles and broadening their coverage without compromising quality. 

Liquidity Group, a fintech specialising in private credit analytics and deal underwriting, leverages proprietary AI tools to evaluate nearly 7,000 companies annually with a team of just seven analysts. This platform can generate deal term sheets in roughly three days, compared to the six to eight-week industry norm.

Similarly, Schroders Capital, the private markets arm of global asset manager Schroders, has introduced its Generative AI Investment Analyst. The tool drafts substantial portions of investment summaries, enabling teams to accelerate due diligence while ensuring that final decisions remain in the hands of human experts.

Improving Accuracy: Document Parsing and Risk Detection 

Document-heavy workflows remain among the most significant bottlenecks in due diligence, where errors or overlooked details can materially affect investment outcomes. AI-driven parsing and natural language processing are now enhancing both the accuracy and efficiency of risk detection in private debt markets, as seen in the examples below:

Affinity, a relationship intelligence platform designed for dealmakers, uses AI to automate market research, competitor analysis, and document parsing. Integrating Natural Language Processing (NLP) saves deal teams up to 70% of their time and improves accuracy in identifying inconsistencies across reports.

According to the VCI Institute, a professional development platform specialising in private equity and value creation, firms using NLP-based parsing tools reduced data collection times by 50%. It detected financial discrepancies earlier than through manual review.

Brightwave, an AI research platform designed for private capital markets, illustrates how “deep research” changes diligence. Its system can connect supplier contract clauses with historic cost anomalies, patterns that human analysts might overlook. Accenture estimates that such AI systems could automate 30% of diligence tasks and augment another 20%.

Specialised Legal and Compliance Enhancements 

Legal and compliance tasks often pose significant challenges in private debt transactions, particularly during fund formation and the onboarding of investors. The repetitive aspects of due diligence questionnaires, background checks, and documentation frequently hinder investment.

AI is enhancing the process of fund formation and documentation. By training large language models (LLMs) on legal and compliance materials, private debt managers can produce draft responses to intricate due diligence questionnaires (DDQs) in several hours rather than weeks. Human verification remains essential, but this acceleration reduces investor friction while maintaining oversight.

Several platforms exemplify this impact, for instance:

• Xapien, an AI platform based in the UK that focuses on background checks and reputational risk, combines structured and unstructured data from worldwide sources.

  
  

• Dow Jones Risk & Compliance, a data intelligence division of Dow Jones, leverages Xapien to reduce counterpart due diligence from several days to minutes, enhancing compliance and client confidence.

  
  

• Arc Intelligence, a provider of predictive analytics, illustrates how AI can detect early warning signs of borrower defaults by examining repayment histories and portfolio trends. This forward-thinking strategy is particularly significant in private credit, where defaults pose substantial downside risks.

  
  

• Similarly, Dealroom, a global data provider catering to startups and venture capital, applies AI to analyse intricate financial and contractual documents, effectively uncovering risk insights.

Emerging Platforms: Domain-Focused AI Tools: 

AI adoption in private debt is not limited to large institutions. A new generation of startups is building purpose-built solutions that target specific pain points across diligence workflows. The following examples demonstrate how domain-focused platforms are redefining the deal room experience:

• Hebbia, an AI startup based in the U.S., has created Matrix, a document intelligence platform utilised by investment banks, asset managers, and private equity firms. Matrix enables users to search through thousands of deal-room documents using plain English and provides answers with citations, thereby alleviating diligence bottlenecks.

  
  

• Intanify, an AI platform designed for small and medium-sized enterprises and intellectual property audits, integrates knowledge-graph intelligence with expert metadata to identify potential “red flags” during diligence. Its emphasis on automated risk scoring makes it an invaluable resource for mid-market transactions.

Challenges: Bias, Security, and Human Oversight: 

While the advantages are evident, using AI in due diligence has drawbacks. If not adequately managed, issues such as bias in training data, excessive dependence on automation, and security risks can undermine results. Companies are addressing these challenges through robust governance frameworks and human oversight: 

• Atalaya Capital Management, a private credit investment firm, has pointed out that AI tools can sometimes struggle when applied to new asset classes or regions not represented in the training data. Managing domain shift is still a pressing challenge.

  
  

• Palladium Digital, a digital transformation consultancy based in the UK, has created PrismGPT, a secure AI framework designed to prevent hallucinations and safeguard client confidentiality during due diligence. Such tailored frameworks are becoming crucial as firms strive to balance innovation with data sensitivity.

Conclusion: A Strategic Imperative, not a Silver Bullet 

AI is undoubtedly transforming due diligence and private debt markets, providing quicker deal cycles, deeper risk insights, and innovative legal and compliance workflows. The adoption trend is evident from Liquidity Group’s swift analytics to Hebbia’s language-driven matrices, Intanify’s risk intelligence, and Xapien’s compliance features. 

However, caution is necessary: data quality, relevance to the domain, AI security, and human oversight are crucial safeguards for implementation. For knowledgeable audiences, AI is not merely a trend but a strategic necessity that requires stringent governance, careful integration, and a maintained role for human judgment. 

As private credit markets become increasingly competitive and intricate, firms that utilise AI with accuracy and caution will shape the future of due diligence excellence.