Key Takeaways

• AI is becoming a governance and risk issue, not just a technology opportunity.
• Open-source AI can improve transparency, optionality and control over AI infrastructure.
• Vendor dependency can create concentration risk across models, data and critical infrastructure.
• Explainability and auditability matter where AI supports credit-related judgments.
• Data provenance and AI supply chain oversight are becoming governance priorities.

How AI Is Changing the Credit Workflow

AI is increasingly becoming embedded across the credit workflow, from early-stage research to portfolio monitoring and reporting. For private credit teams, the key issue is not simply whether AI can improve efficiency, but how AI-supported outputs are governed, reviewed and evidenced.

• Analysis: AI can support credit research, borrower analysis, document review and scenario work by helping teams process larger volumes of information more quickly.
• Monitoring: AI can help identify emerging risks, covenant issues, borrower developments and portfolio signals that may require further review.
• Reporting: AI can assist with drafting, summarizing and structuring credit materials, making reporting faster while increasing the need for clear review and accountability.
• Oversight: As AI becomes more embedded in credit workflows, firms need stronger controls, evidence trails and review processes to ensure outputs can be challenged, validated and explained.

The practical challenge is to keep human judgment central while using AI to improve speed, coverage and consistency across credit work.

For practical guidance on prompt design, agentic AI, context management and responsible use, see AI in Credit Best Practice.

Hot Topic: Vendor Lock-In and Concentration Risk

How Does Vendor Lock-In Create AI Concentration Risk?

The webinar positioned open-source AI as a way to create optionality and reduce concentration risk. As Michael Berns put it, “vendor lock-in is the new subprime.”

Key implications for credit professionals include:

• Concentration risk is not limited to portfolios — it can also exist within AI infrastructure.
• Over-reliance on a single AI provider may reduce resilience and increase exposure to pricing, availability, and governance risks.
• Open-source AI offers greater optionality through the ability to evaluate, switch or adapt models over time.
• Firms should assess AI dependencies with the same discipline applied to other critical third-party relationships.

Hot Topic: Explainability, Regulation and Model Risk

Why is Explainable AI Becoming More Important in Financial Services?

“Trust me” is not an AI governance strategy. In regulated financial services, the ability to show how AI-supported outputs were generated, reviewed and evidenced is becoming central to responsible adoption. Key implications for private credit professionals include:

• In regulated environments, AI-supported decisions must be explainable, auditable, and defensible.
• Firms may face increasing scrutiny over how AI-generated outputs are produced, reviewed, and evidenced.
• Greater transparency can help reduce model risk and support governance, compliance, and regulatory requirements.
• For private credit professionals, maintaining clear audit trails may become as important as achieving efficiency gains.

Hot Topic: IP, Data Sovereignty and AI Supply Chains

Why Do Data Sovereignty and AI Supply Chains Matter?

The webinar highlighted that the AI supply chain needs the same discipline as software. As firms adopt and fine-tune models, they may need to evidence where their data, model components and intellectual property originate. Key implications for private credit professionals include:

• Firms may face increasing scrutiny over the provenance of AI models, data, and intellectual property.
• Data sovereignty and control over sensitive information remain key governance considerations.
• AI supply chains require the same rigor, transparency and oversight applied to software and third-party technology providers.
• Understanding where AI outputs come from may become increasingly important for governance, compliance, and risk management.

What this Means for Private Credit Professionals

• AI risk is becoming part of credit risk.
• Vendor dependency can create concentration risk.
• Explainability and auditability matter where AI supports credit-related judgments.
• Data provenance and AI supply chain oversight are becoming governance priorities.

Practical Questions for Firms

As AI adoption accelerates, firms should focus on three practical questions:

• Where are we exposed to AI vendor dependency?
• Can we explain and evidence AI-supported credit outputs?
• Do we understand where our AI data, models and infrastructure come from?

Frequently Asked Questions

What is Open-Source AI in Financial Services?

Open-source AI refers to AI models and technologies that organizations can evaluate, adapt and govern with greater transparency and control than many proprietary alternatives. In the session, open-source AI was discussed as a way to improve optionality and reduce vendor lock-in.

What is AI Concentration Risk?

AI concentration risk can arise when firms become overly dependent on one AI provider, model or infrastructure layer, increasing exposure to pricing, availability, governance and third-party risk.

Why Does AI Governance Matter in Private Credit?

AI governance matters because AI may influence credit analysis, monitoring, documentation and reporting. Strong governance helps firms maintain transparency, accountability and control where AI supports credit-related judgments.

Why Does Explainability Matter for Credit Decisions?

Explainability helps firms understand how AI-supported outputs are produced, reviewed and evidenced. This is important where AI supports decisions that may need to be challenged, validated or documented.

Build the Skills Behind these Insights with the GCC

The themes explored in this session are also reflected in the Global Credit Certificate (GCC), which has been updated to cover both private credit and the role of AI in modern credit analysis.

For professionals working in or around private credit, the GCC provides a structured way to build the credit skills needed to assess borrowers, understand market structures and apply sound judgment across changing credit environments. The syllabus includes a dedicated private credit chapter covering market drivers, transaction types, borrower analysis, loan structures, investor protections, fund vehicles, regulatory focus and portfolio considerations.

The GCC also includes a dedicated chapter on the role of AI in modern credit analysis. This aligns directly with the webinar themes of governance, explainability, AI literacy, third-party dependency and responsible adoption. The chapter explores how AI is changing credit workflows, from data extraction and risk analytics to real-time monitoring, supply chain complexity, oversight functions and emerging trends such as agentic AI.

Together, these syllabus areas reflect the central insight of the webinar: modern credit professionals need more than traditional analytical skills. They need to understand how private markets operate, how AI is reshaping the credit lifecycle, and how to apply governance, judgment and risk discipline in a more technology-enabled environment. Explore the Global Credit Certificate to build practical expertise in private credit, AI in credit analysis and the skills needed for the future of credit decision-making.

For readers considering how AI, private credit and regulation are reshaping long-term skills and career paths, see Credit Careers.