Banking AI: Guardrails for a Smarter Future

Recently, Bridgeforce leaders led a frank discussion about the role of AI in consumer lending. The conversation focused on what’s happening inside financial services today, not just industry hype. Practical truths emerged: AI in banks, credit unions and fintechs enhances expertise, builds efficiency, and creates customer value when paired with strong oversight and guardrails.

Human Expertise at the Core

“AI needs to be prompted by someone who has both knowledge and experience. When you hear about AI failing, it’s often when there isn’t oversight from someone with actual relevant direct experience in that area.”   — Adam Thornber

AI in banking depends on skilled professionals who provide context and oversight. Models can process information at scale, but they lack judgment. Failures often happen when AI is put in situations that require the very judgment it’s missing.

Consider what would occur if a large bank launched a customer service chatbot to handle disputes and the bot began inventing answers and hallucinating policies that didn’t exist. Air Canada, experienced the “nonexistent policy” incident. Air Canada’s chatbot provided incorrect refund information. The airline was ordered to compensate a passenger given the airline failed to ensure accuracy of its chatbot.

Or if a lender automated small-dollar loan approvals. If bias crept into the model and created patterns that unfairly favor or disadvantage certain groups, geographies or applicant profiles, it’d generate inconsistent treatment and raise compliance concerns.

A recent study found a pattern of racial bias in recommendations for psychiatric treatment generated by (LLMs) large languages models. The LLMs often proposed different treatments for patients, “…at times making dramatically different recommendations for the same psychiatric illness…”.

In banking, AI failures can damage trust and invite regulatory scrutiny.

Top 5 Actions to Embed Human Intelligence with AI Workflows

1. Design oversight checkpoints, embedding them into procedures and controls.
2. Define AI oversight roles across business, compliance and IT.
3. Assign experienced staff to review AI outputs consistently and randomly in areas like credit, collections, and disputes.
4. Use AI to accelerate tasks, while ensuring people validate results.
5. Provide employees with training so they know both the strengths and limits of AI tools.

Change Management: People, Process, and Technology

“Yes, AI adoption is about technology. However, it’s more of a transformation journey. You must align your people, your processes, and your tech if you want to unlock the real value. If you don’t bring everyone along, you’ll end up with stalled projects and missed opportunities.”   — Andrew Domino

Introducing AI reshapes how teams work, how decisions are made, and how customers are served. Organizations that focus only on technology often find themselves with expensive pilots that never scale.

Signs of Stalled Adoption

• Employees resist new tools because they don’t understand the intent.
• AI outputs don’t connect with existing workflows.
• Questions are raised about governance (internally and externally) because oversight wasn’t built in from the start.

Top Four Steps to Fully Operationalize AI Adoption

1. Start with the end in mind. Clearly define the “problem” you’re trying to solve and envision what you are trying to achieve as a business objective.
2. Communicate the vision. Link AI to measurable outcomes such as faster dispute resolution or improved customer service.  Then, report on those outcomes!
3. Reskill staff. Train employees whose daily work will shift, equipping them for oversight and analysis roles.
4. Redesign workflows. Build AI into the way business gets done, not as a bolt-on system.

One instance of AI implementation is a credit union that used artificial intelligence to handle increased use of communications channels. Employees were not displaced. Instead, basic questions were answered, allowing staff to focus on complex interactions. Transparent communication will ensure that staff embrace change.

Thoughtful Adoption of Automation to Get to AI Deployment

“Financial institutions are focusing on cost-effective modernization to remain competitive. This includes adopting advanced fintech solutions, optimizing regulatory compliance processes, and leveraging customer data for personalized experiences. They are implementing process automation to help decrease operating expenses. And if you can do that, then you free up more funds for investment in technology that supports new products and services. When it comes to innovation, I see them [FIs] dipping their toes into AI in careful, thoughtfully executed ways.”   — Michelle Macartney

Banks, credit unions and fintechs alike face pressure to reduce operating expenses while delivering innovation. Process automation creates capacity to invest in technology that supports new products and services. AI plays a role in this modernization push, particularly in customer personalization and efficiency gains.

Examples of Thoughtful Use of AI

• Automating reconciliations and back-office document review to reduce manual errors.
• Using data-driven models to tailor marketing offers while maintaining compliance oversight.
• Applying AI to identify fraud patterns but always validating outputs with investigators.
• Using large language models and agentic AI to review consumer communications (disputes and complaints) to increase efficiency and completeness while including human review.

Examples of Careless AI Use Can Cause Risk

• Deploying AI chatbots in disputes without escalation paths to humans.
• Letting generative AI create compliance documentation without validation.
• Scaling loan decisioning tools without bias testing.

Careful pilots, monitored outcomes, and staged rollouts prevent these risks and allow lenders to capture value without overreaching.

Data Quality: The First Guardrail

“Using AI on your data is only really worth it if you’ve got really good data that is incredibly rich. And it doesn’t change the fact that you need to do that part first before you then apply the AI tool to the data.”  — Adam Thornber

Strong data is the foundation for success. Poor data undermines outcomes and introduces risk. The often quoted trope applies here: garbage in, garbage out.

One bank may roll out AI for credit bureau dispute management. If the training data is fragmented and inconsistent, the system will deliver unreliable resolutions and frustrate customers. Another financial institution might deploy AI for fraud detection, but incomplete data could cause the tool to flag thousands of legitimate accounts—eroding trust and creating costly follow-up work.

Top Three Steps to Prepare Data for AI

1. Conduct a data health check to identify inaccuracies and gaps.
2. Establish ongoing data governance to keep information accurate and compliant.
3. Invest in data enrichment processes (e.g., data mapping, data dictionaries) before deploying AI at scale.

Monitoring: Guardrails for Every Model

“You have to add another layer to AI which is very, very careful monitoring. You simply cannot ‘set it and forget it’ with continuously learning technology. AI can learn on bad data, which gives you a result you don’t want. In the near term, my concern is that institutions see AI as something that’s going to save time. And it will—but without monitoring, it could get you into trouble quickly. Monitoring on AI is critical.”  — Michelle Macartney

AI requires continuous oversight. Models adapt based on data inputs, which means they can drift in harmful directions if left unchecked. An example of human-in-the-loop (HITL) monitoring that works is Wimbledon 2025 (Testlio, 2025). The AI line judge system failed mid-match and a live official stepped in to correct the call. Ironically, the system wasn’t working due to being accidentally deactivated by a human.

Consider an AI system built for collections messaging. Even though it was trained on historical practices, the AI begins recommending overly aggressive outreach strategies. Without monitoring, it could generate consumer complaints and attract regulatory attention. For instance, the National Eating Disorders Association (NEDA) removed its chatbot from the help hotline because it was providing harmful advice about eating disorders (recommending counting calories and measuring body fat).

Three Steps to Building Guardrails for AI

1. Create monitoring dashboards to track outputs in real time.
2. Establish review schedules for retraining models with updated, accurate data.
3. Define escalation paths so staff can intervene quickly when anomalies appear.

Monitoring is not optional. It is the layer that keeps AI safe, compliant, and aligned with business objectives.

Bridgeforce’s Role: Turning Potential Into Results

“At Bridgeforce, we don’t build proprietary AI platforms. Our role is the interpreter—helping clients operationalize AI; to bridge the gap between technology and business outcomes, and make sure the solutions actually work in their environment.”  — John Sanders

Bridgeforce helps financial institutions make AI work. That means aligning strategy with operations, guiding vendor selection, and embedding oversight frameworks. The focus is on measurable results, not experimental pilots.

Key elements of our approach

• Translating AI capabilities into real-world banking use cases.
• Designing governance frameworks to ensure compliance.
• Supporting change management so staff adopt tools effectively.
• Monitoring performance to ensure long-term value.

Banks and credit unions benefit from having an experienced partner who understands both technology and the operational realities of financial services.

Practical Steps for Success with Banking AI

Successfully harness the power of banking AI and drive measurable business outcomes by moving beyond theory and taking decisive, practical steps.

Start by setting a clear goal, get your data in shape and bring your team along for the journey. With the right guardrails and a purposeful approach, you’ll unlock real results: faster processes, stronger compliance and better experiences for your customers.

1. Define the strategy. Identify measurable goals and link them directly to business priorities.
2. Prepare the data. Invest in accuracy, richness, and governance before tool deployment.
3. Plan for people. Equip staff with skills and redesign workflows to integrate AI.
4. Establish guardrails. Implement governance, monitoring, and human oversight for every use case.
5. Scale with purpose. Pilot carefully, measure results, and expand based on success.

Ready to see how banking AI can deliver real impact? Connect with Bridgeforce today to turn practical insights into measurable results.