By David Eads
As new artificial intelligence (AI) solutions and products enter the market, financial institutions should do their due diligence before investing. AI brings a world of possibilities for enhancing efficiency when done right. But adopting AI isn’t just about automating a process or adding a new tool. It’s about trust, transparency and long-term alignment with business goals and regulatory expectations.
Whether an institution is exploring AI for customer onboarding, credit risk scoring, or commercial loan analysis, asking the right questions upfront is essential. These five questions can help banking leaders evaluate AI solutions more effectively and ensure they’re adopting technologies with purpose and clarity.
1. What problem is this AI solving, and how will success be measured?
AI should be a solution to a defined business challenge, not a shiny object in search of a use case. Without clarity on what success looks like, ROI will be difficult to prove or achieve. Are you trying to reduce loan processing times? Maybe the goal is to improve fraud detection accuracy or streamline compliance reviews. Ask how the AI aligns with your specific pain points and how performance will be measured over time.
For example, in commercial lending, AI can be used to automate financial spreading, which is a time-intensive task that slows down credit decisions. By clearly identifying the problem and expected outcomes, such as reducing loan turnaround times or improving analyst productivity, banks can set realistic expectations and measure performance effectively.
2. How is data used, stored and protected and is it being used to train models?
Banks handle highly sensitive customer data, and any AI system that interacts with it must meet strict standards for privacy, security and regulatory compliance.
Ask whether the AI model learns from your institution’s data and if so, how. Some solutions explicitly do not train on customer inputs, preserving data privacy. Others may use data to improve model performance unless safeguards are in place. In all cases, confirm encryption protocols, storage practices and access controls.
If customer data is used to retrain the model and that model is later deployed elsewhere, there’s a risk that details from one bank’s borrowers, such as business names, financial metrics or deal structures, could influence outputs in another institution’s environment. Even if this happens unintentionally, it could result in serious reputational, legal, or regulatory consequences.
Banks should demand clear answers about how their data is handled and what safeguards are in place to prevent unintended data exposure or model leakage. Make sure the provider clarifies whether any part of your data contributes to ongoing model training or fine-tuning and how that is governed.
3. Can the AI explain its decisions and validate outputs?
Banks must understand how an AI system arrives at a recommendation or output, especially in credit, compliance, or risk contexts.
Look for systems that make it easy for users to trace AI outputs back to source data. For example, in credit analysis, a system that shows where a number came from in the original document, such as linking a financial value back to a specific balance sheet, helps credit analysts validate the output.
Other safeguards, like conditional formatting that checks whether subtotals match totals in financial statements, can help catch errors and improve confidence in AI-generated outputs.
If the AI can’t show its work, it could introduce risk (bias, inaccuracies, etc.) instead of reducing it. These tools give human reviewers a clear path to validate the AI’s work and make informed decisions. Explainability isn’t just a nice-to-have, it’s essential for trust, auditability and compliance.
4. What controls exist to prevent inaccuracies or hallucinations?
Generative AI models are powerful, but they can still make mistakes. In some cases, they may produce confident but incorrect or entirely fabricated information, which can be especially problematic in banking environments.
Ask what safeguards are in place to reduce hallucinations. For example, some systems use traditional coding techniques to introduce checks and balances to improve accuracy and highlight errors. This reduces the risk of AI hallucination, which is when AI generates inaccurate or made-up statements.
Financial institutions should favor solutions that combine the speed of generative AI with the discipline of rule-based validations. This hybrid approach helps ensure AI-generated insights are reliable and grounded in source data.
5. How does the platform evolve as new models and regulations emerge?
AI development has been fast, and no single model is best for every task. Banks need platforms that can adapt, swapping in the most effective models for different use cases as new technologies emerge.
Look for solutions that are configurable or flexible enough to incorporate the latest advancements. Flexibility is key, especially for institutions planning to scale their AI capabilities across business lines. A platform that supports multiple models or that can switch models as needed helps ensure you’re always using the right tool for the job. In a rapidly changing landscape, agility is just as important as functionality. The ability to adapt ensures compliance and competitive advantage.
AI has the potential to make banking smarter and faster, but only if adopted thoughtfully. The right questions help bankers identify the solutions best suited to meet their specific needs. Whether you’re exploring AI for customer support, document analysis, or commercial loan origination, keep these five questions in mind. The goal isn’t just to adopt AI, it’s to adopt it with clarity, security and purpose.
About the Author
David Eads is the CEO at Vine Financial Inc. Vine is a faster, more accurate, and more auditable Commercial Lending Accelerator. David is a serial entrepreneur with a unique mix of business and technology skills.