Key Insights
- AI-enabled service paired with human access builds deposit loyalty beyond competitive rates alone.
- Three in four borrowers want humans involved in loan approvals and closing.
- 85% of borrowers trust lenders more when AI use is clearly disclosed.
The trust that forms between soldiers and their fellow service members is built over time, through shared challenges and mutual reliance. They learn to read one another’s cues, step in when needed, and trust that their teammates are doing their part even when they can’t see it. That confidence doesn’t come from a single dramatic moment, but from countless smaller interactions where people show up, follow through, and communicate clearly.
In much the same way, trust between customers and community financial institutions (CFIs) grows when institutions can strike the right balance between artificial intelligence (AI)–backed assistance and human built relationships. When used thoughtfully and intentionally, AI can take on tedious, back‑end work, freeing bankers to spend more time focusing on customers and strengthening those human connections.
AI Adoption Is Rising, but Human Trust Still Rules
Generative AI is rapidly gaining momentum within the banking industry with 49% of CFIs using the technology in some way as of 2026, according to “What's Going On in Banking 2026: AI, Crypto, and Fraud – Oh My!,” a recent research report from Cornerstone Advisors. But CFIs have quickly discovered that elaborate AI algorithms don’t automatically translate into bottom-line performance. Customer trust remains a key factor in a financial institution’s overall profitability. Maintaining that trust requires finding a healthy balance between AI usage and enabling people to maintain human connections within their banks. Furthermore, people want transparency regarding how their financial institutions are using AI and customer data.
According to PwC’s 2026 Consumer Lending Radar report, three out of four customers want humans involved in loan approvals and closing, while 85% of borrowers trust lenders more when they disclose that they are using AI and the ways that they are using it. To deliver the greatest possible return on investment to boards and regulators, CFIs must evaluate AI and customer experience projects through a lens focusing on three core financial levers: deposit costs, loan economics, and credit risk.
Deposit Costs: Build Loyalty Beyond Rate
Deposits remain central to profitability, and the cost of attracting and retaining them directly affects net interest margin. Rather than competing solely on rate, CFIs can reinforce loyalty by pairing convenient AI-enabled service with reliable access to knowledgeable bankers, particularly when customers have questions, face a financial decision, or need advice.
CFIs should consider:
- Defining clear human-escalation paths. Make it easy for customers to reach a banker from digital channels, especially for account problems, potential fraud, life events, and complex product questions.
- Giving frontline staff a complete customer view. Connect relevant account, interaction, and service-history data so customers do not have to repeat their circumstances when moving between a chatbot, contact center, and branch or relationship manager.
- Identifying deposit customers who may need outreach. Use AI-enabled insights to flag customers with declining balances, expiring CDs, repeated service issues, or other signals of attrition, then assign a banker to follow up with a relevant conversation rather than a generic offer.
- Using AI to remove internal friction. Apply AI to repetitive administrative work, such as document collection, data entry, call summaries, routine servicing requests, and internal research, so bankers can devote more time to proactive outreach and advisory conversations.
A human presence does not mean every task requires a banker. It means customers know a qualified person is available when the situation warrants it, and that the institution has used technology to make that interaction more informed and useful.
Build Human Oversight Into Lending
Borrowers may appreciate faster, more personalized experiences, but they also want human involvement in consequential financial decisions. As stated previously, PwC found that three out of four customers want humans involved in loan approvals and closing. Zendesk similarly found that 70% of customers expect employees to have full context of their financial situation and 62% value recommendations tailored to their individual financial circumstances.
CFIs can translate those expectations into practical lending controls. CFIs should consider the following:
- Automate administrative tasks. Use AI to support document intake, data extraction, application-status updates, and preparation of internal loan summaries.
- Define when human involvement is required. Set clear thresholds based on loan amount, complexity, product type, or a borrower’s individual circumstances.
- Give borrowers a dedicated point of contact. Assign a named banker or lender to guide customers through consequential lending decisions, particularly during underwriting and closing.
- Use context to make recommendations relevant. Draw on a borrower’s known financial circumstances and stated needs rather than relying on generic product prompts or automated messages.
- Measure the full lending experience. Track application completion, processing time, fallout rates, borrower satisfaction, and employee time saved to ensure efficiency improvements also support better outcomes.
Credit Risk: Make AI Decisions Explainable
AI-supported underwriting and risk scoring can help CFIs identify potential problems earlier and make credit decisions more consistent. However, those benefits depend on employees being able to understand, challenge, and explain how a model reached its recommendation.
Transparency also matters to customers. PwC found that 85% of borrowers trust lenders more when the institution discloses its AI use and explains how it is being used. Clear disclosures and strong internal governance can therefore support both the customer experience and regulatory readiness.
CFIs should consider:
- Explain AI use in plain language. Develop customer-facing disclosures that clarify when AI supports a lending decision, where human judgment remains involved, and how borrowers can ask questions or seek assistance.
- Create a clear decision record. Document the data and factors that inform model recommendations, as well as the resulting outputs and any employee overrides.
- Set boundaries for automated recommendations. Require human review for exceptions, borderline applications, and other decisions where a model may not capture important borrower context.
- Train employees to challenge the model. Equip lenders and credit analysts to recognize when AI-generated recommendations may be incomplete, inconsistent, or inappropriate for a borrower’s circumstances.
- Test models on an ongoing basis. Regularly review AI-supported decisions for fairness, data quality, and compliance with fair-lending, adverse-action, and model-risk-management requirements.
When CFIs can explain how AI informs credit decisions (and when employees retain the authority to question its outputs) they can use the technology to improve risk management without diminishing customer trust.
Ultimately, CFIs should view AI not as a replacement for relationship banking, but as a tool to make those relationships stronger and more scalable. By using AI to reduce administrative burdens, preserve meaningful human involvement in high-stakes decisions, and clearly explain how technology informs customer experiences, institutions can build the trust that supports lasting loyalty, stronger economics, and sounder risk management.
