By Ken Greene, Attorney

Artificial intelligence has quickly become a valuable tool throughout the equipment finance industry. Funders and brokers alike are increasingly using AI to streamline underwriting, detect fraud, automate customer communications, monitor portfolios, and improve operational efficiency. While these technologies can provide substantial benefits, they may also introduce legal and regulatory risks that should not be overlooked.

As AI adoption accelerates, equipment finance companies should ensure that innovation is accompanied by appropriate governance and compliance controls.

Many finance companies are exploring AI-driven underwriting tools to enhance credit analysis and accelerate decision-making. However, lenders remain responsible for ensuring that credit decisions comply with applicable federal and state laws. If an AI model relies on historical data containing hidden biases, it may inadvertently produce outcomes that disproportionately affect certain protected groups. Even where no intentional discrimination exists, regulators may scrutinize lending practices that result in a disparate impact. Equipment finance companies should regularly test AI-driven underwriting models, document their review processes, and maintain meaningful human oversight.

One challenge associated with advanced AI systems is explain ability, sometimes referred to as a “black box” problem. Certain AI models generate recommendations without clearly identifying the factors that influenced the result. This can become problematic when a finance company must explain why a transaction was declined or why additional conditions were imposed, akin to the FAA assessing what caused a plane to crash. Regulators increasingly expect lenders to understand and be able to explain automated decision-making processes. A lender or broker that cannot articulate how an AI system reached a conclusion may face compliance challenges during audits, examinations, or litigation.

Equipment finance transactions often involve sensitive customer information, including financial statements, tax returns, bank records, and proprietary business data. Feeding this information into AI platforms—particularly publicly available generative AI tools—can create significant privacy and confidentiality concerns. Companies should establish clear policies governing employee use of AI tools and ensure that confidential customer information is not uploaded into systems that could compromise data security or ownership rights.

Many organizations obtain AI capabilities through third-party vendors. However, outsourcing technology does not outsource responsibility. Regulators continue to emphasize that financial institutions remain accountable for the activities of their service providers. Before deploying AI-based products, finance companies should conduct thorough vendor due diligence, review contractual protections, particularly in the areas of liability and indemnity, evaluate cyber security controls, and assess how the technology complies with applicable laws and regulations.

AI-generated analyses can be helpful, but they are not always accurate. Errors in financial projections, collateral valuations, or risk assessments may lead to poor business decisions, customer disputes, or litigation. Employees should view AI as a decision-support tool rather than a substitute for professional judgment. Companies should also maintain documentation regarding how AI-generated recommendations are reviewed and approved before being implemented.

AI is evolving like wildfire, and, like wildfire, has the ability to lose control. Consequently, the legal framework that surrounds AI is always playing catch-up. Federal and state regulators are increasingly focused on transparency, accountability, cyber security, and consumer protection issues related to artificial intelligence. Equipment finance companies that establish AI governance policies today will be better positioned to adapt to future regulatory requirements.

AI has the potential to significantly improve efficiency and profitability across the equipment finance industry. The companies that derive the greatest benefit, however, will be those that balance technological innovation with strong compliance, risk management, and human oversight. This latter factor, human oversight, is critical. It will, in this writer’s opinion, be the key to whether AI succeeds or fails on virtually every level.

AI has the potential to significantly improve efficiency and profitability across the equipment finance industry. The companies that derive the greatest benefit, however, will be those that balance technological innovation with strong compliance, risk management, and human oversight. This latter factor, human oversight, is critical. It will, in this writer’s opinion, be the key to whether AI succeeds or fails on virtually every level.

Law Offices of Kenneth Charles Greene

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