AI for finance applications: Opportunities and risks for borrowers

Artificial intelligence (AI) is increasingly being used across the finance industry. Lenders, borrowers and advisers are finding practical applications that can improve efficiency, reduce admin burden and free up experienced decision-makers to focus on judgement and risk assessment.

One area where AI is gaining traction is in the preparation of finance applications. When used appropriately, it can assist with information gathering, analysis and presentation. However, borrowers should be cautious about relying on AI-generated outputs without robust review, as inaccuracies, confidentiality risks and generic content can undermine lender confidence.

While AI can support the finance application process, responsibility for the accuracy, completeness and quality of information ultimately remains with the borrower.

Information collation

One of AI’s most practical applications is assisting with the preparation of information packs. Sophisticated lenders expect clear, internally consistent documentation, including financial information, feasibility studies, budgets, sensitivity analysis, and supporting market data.

AI can help organise and present these materials in a more structured and lender-ready format. This can improve efficiency and reduce the time required to prepare application documentation.

However, AI is only as helpful as the instructions it receives. This means that AI tools require guidance from users who understand the basis of lender’s credit and risk assessment methodologies. Without appropriate oversight, AI-generated content may appear convincing while containing factual inaccuracies, material omissions or unsupported assumptions.

Lenders also expect to see evidence of management capability and a strong understanding of the business. Misdirected or irrelevant AI-generated content is likely to detract from the application and may raise questions about the quality of management information and decision-making.

Borrowers should also carefully consider what information is uploaded into AI platforms. Finance applications frequently contain commercially sensitive information, customer data, forecasts and strategic plans. Organisations should ensure any AI tool complies with internal governance requirements and confidentiality obligations before using it in the application process.

Model testing

AI is increasingly being used to support model testing and scenario analysis. It can assist with stress-testing feasibility models, running downside scenarios and identifying potential pressure points in debt serviceability or covenant compliance. These capabilities can provide a useful starting point for understanding how a project or business may perform under different conditions.

However, care must be taken to ensure sensitivities reflect the realities of the relevant market, geography, and unique business drivers. AI models are also capable of producing outputs that appear credible but are fundamentally flawed. They may misinterpret assumptions, apply inappropriate sensitivities or overlook important risks. For this reason, all AI-assisted analysis should be independently reviewed by experienced advisers who understand lender expectations and market conditions.

Market intelligence

AI can assist in aggregating and summarising publicly available market information. By quickly processing large volumes of data, it can help identify trends, cost drivers, demand indicators and other factors relevant to a funding proposal.

Where the underlying information is reliable, AI can help borrowers synthesise market insights into a more cohesive and evidence-based narrative. This can be particularly valuable as lenders place increasing emphasis on forward-looking risks and market resilience rather than solely relying on historical performance.

However, the quality of any AI-generated insight is dependent on the quality and currency of the data available to it. In evolving markets, AI may rely on outdated information or fail to recognise emerging trends and risks that an experienced adviser would identify through direct market engagement.

Structuring

There are limits to the information available to AI. Unlike public debt markets, many aspects of private lending are not transparent or publicly documented. Current lender appetite, pricing expectations, transaction structures and credit preferences often evolve quickly and are shaped by market conditions and individual relationships.

As a result, experienced advisers remain critical when assessing financing options, negotiating terms and identifying the most appropriate funding solution.

Diligence

Another practical application of AI is in document review and diligence preparation. AI can rapidly analyse large volumes of reports and documentation and highlight inconsistencies, gaps or areas that may attract lender scrutiny during the credit assessment process. Addressing these issues before engaging with lenders can significantly reduce execution risk, improve the quality of the submissions and avoid delays during formal credit assessment.

However, AI remains a supplement to, rather than a substitute for, professional advice. It may overlook critical issues or generate incorrect conclusions with a high degree of confidence. Responsibility for identifying and validating material risks remains with management and their advisers.

Importantly, the risk associated with incomplete or inaccurate outputs remains with the user. While AI may assist in reviewing information, it does not provide the protections associated with engaging appropriately qualified legal, technical or financial advisers. Lenders continue to place significant value on independent expert advice, particularly where complex diligence issues are involved.

Credit priorities and lender engagement 

Finally, there are some outstanding questions about the role AI can play in strategic lender engagement. Given lenders rarely publish detailed credit policies or appetite statements, it will be difficult for AI to best identify how to tailor the information and narrative to each lender. Most importantly, lending remains fundamentally a relationship-driven process.

Credit decisions are based not only on information quality but also on confidence in management capability, governance and execution. AI can help prepare information, but it cannot replace the trust developed through direct engagement with lenders and advisers. Identifying the right lender, understanding their priorities and developing a trusted relationship remain distinctly human activities.

The role of AI

AI should be viewed as an assistive tool rather than a decision-maker. It can help improve efficiency in information gathering, document preparation and preliminary analysis. However, borrowers remain accountable for the accuracy, completeness and confidentiality of the information provided to lenders.

Over-reliance on AI can introduce risks including factual errors, missed diligence issues, privacy breaches and the erosion of lender confidence in management's understanding of its own business. The most effective outcomes are achieved when AI supports, rather than replaces, the experience, judgment and relationships of management and their advisers.

Ultimately, access to capital is built on credibility, trust and informed decision-making. While AI can assist in the preparation process, those foundations remain firmly human.

How BDO can help

Technology can assist in preparing information, but funding outcomes are still driven by expertise, credibility and relationships. BDO's debt advisory team combines technical knowledge, extensive lender networks and hands-on transaction experience to help clients prepare, negotiate and secure financing solutions that support their strategic objectives.