The use of artificial intelligence (AI) is increasing across the financial industry. Lenders, borrowers, and advisors are finding practical applications that can improve efficiency, reduce administrative burden, and free up experienced decision makers to focus on judgment and risk assessment.
One area where AI is gaining traction is in the preparation of financial applications. When used properly, it helps in gathering, analyzing, and presenting information. However, borrowers should be wary of relying on AI-generated output without robust review, as inaccuracies, confidentiality risks, and generic content can undermine lender trust.
While AI can support the loan application process, the responsibility for the accuracy, completeness, and quality of the information ultimately rests with the borrower.
Information verification
One of the most practical applications of AI is to assist in the preparation of information packs. Sophisticated financial institutions expect clear and internally consistent documentation, including financial information, feasibility studies, budgets, sensitivity analyses, and supporting market data.
AI can help organize and present these materials in a more structured, lender-friendly format. This increases efficiency and reduces the time required to prepare application documents.
However, the AI is only as useful as the instructions it receives. This means that AI tools require guidance from users who understand the fundamentals of lender credit and risk assessment methodologies. Without proper oversight, content generated by AI can appear persuasive despite containing factual inaccuracies, material omissions, or unsupported assumptions.
Lenders also expect to see evidence of management ability and a deep understanding of the business. Misguided or irrelevant content generated by AI can impair application functionality and raise questions about the quality of management information and decision-making.
Borrowers should also carefully consider what information is uploaded to AI platforms. Financial applications often include commercially sensitive information, customer data, forecasts, and strategic planning. Organizations should ensure that AI tools comply with internal governance requirements and confidentiality obligations before using them in application processes.
Testing the model
AI is increasingly being used to support model testing and scenario analysis. This helps stress test feasibility models, run downside scenarios, and identify potential pressure points in debt repayment ability or contract compliance. These features provide a useful starting point for understanding how your project or business performs under different conditions.
However, care must be taken to ensure that the sensitivity reflects the realities of the relevant market, geography, and unique business drivers. AI models can also produce outputs that appear reliable but are fundamentally flawed. You can misunderstand assumptions, apply inappropriate sensitivities, or overlook important risks. For this reason, all AI-assisted analysis must be independently reviewed by an experienced advisor who understands lender expectations and market conditions.
market intelligence
AI helps aggregate and summarize publicly available market information. Processing large amounts of data quickly helps identify trends, cost drivers, demand metrics, and other factors related to funding proposals.
When the underlying information is reliable, AI can help borrowers integrate market insights into a more coherent, evidence-based narrative. This could be particularly valuable as lenders increasingly focus on future risk and market resilience, rather than relying solely on past performance.
However, the quality of the insights generated by AI depends on the quality and currency of the available data. In evolving markets, AI can rely on outdated information or fail to recognize new trends and risks that experienced advisors would identify through direct market engagement.
structuring
There are limits to the information that AI can obtain. Unlike public bond markets, many aspects of private lending are not transparent or publicly documented. Current lender appetite, pricing expectations, deal structures and credit preferences often change rapidly and are shaped by market conditions and personal relationships.
As a result, experienced advisors remain critical when evaluating financing options, negotiating terms and identifying the most appropriate financing solution.
diligence
Another practical use of AI is document review and diligent preparation. AI can quickly analyze large volumes of reports and documents and highlight discrepancies, gaps, or areas that may come under lender scrutiny during the credit evaluation process. Addressing these issues before engaging with a lender can significantly reduce execution risk, improve the quality of your submission, and avoid delays during formal credit evaluation.
However, AI remains a complement to expert advice rather than a replacement for it. You may miss important issues or draw incorrect conclusions with a high degree of confidence. Management and its advisors are responsible for identifying and verifying material risks.
Importantly, the risks associated with incomplete or inaccurate output remain with the user. While AI may assist in reviewing information, it does not provide the protections associated with the involvement of appropriately qualified legal, technical, or financial advisors. Lenders continue to value independent expert advice, particularly where complex diligence issues are involved.
Credit priorities and relationships with lenders
Finally, there are some unanswered questions about the role AI can play in strategic financial institution engagement. Given that lenders rarely publish detailed credit policies or appetite statements, it is difficult to determine how AI can best tailor information and narratives to each lender. Most importantly, lending remains a fundamentally relationship-driven process.
Credit decisions are based not only on the quality of information, but also on confidence in management capabilities, governance, and execution. While AI can help prepare information, it cannot replace the trust built through direct engagement with lenders and advisors. Identifying the right lender, understanding their priorities, and building a trusting relationship remain distinctly human activities.
The role of AI
AI should be seen as a support tool, not a decision maker. It contributes to the efficiency of information gathering, document preparation, and preliminary analysis. However, the Borrower remains responsible for the accuracy, completeness, and confidentiality of the information provided to the Lender.
Over-reliance on AI can lead to risks such as factual errors, lack of time and attendance issues, privacy violations, and diminished lender confidence in management’s understanding of their business. The most effective outcomes are achieved when AI supports, rather than replaces, the experience, judgment, and relationships of executives and their advisors.
Ultimately, access to capital is built on credibility, trust, and informed decision-making. While AI can assist in the preparation process, its foundation remains firmly in humans.
How BDO can help
Technology can help prepare information, but fundraising outcomes still depend on expertise, credibility, and relationships. BDO’s debt advisory team combines technical knowledge, an extensive lender network, and real-world transactional experience to help clients prepare, negotiate, and secure financing solutions that support their strategic objectives.
