A recipe for fairness? Tackling AI bias in banking

The introduction of generative artificial intelligence (gen AI) has arguably made it easier to access and execute AI strategically across the banking ecosystem. Natural language interfaces lower the barrier to entry, enabling more banks to apply AI to various use cases, from customer personalization and credit decisioning to fraud detection.

But this ease of access brings new risks. The data that gen AI models are trained on often contains historical bias that users cannot easily detect. When that bias is baked into a model it can quickly expand and spread, compromising fairness and exposing the bank to legal, financial, and reputational harm.

To avoid baking bias in, it must be carefully sifted out. Getting AI right and mitigating risks isn’t a matter of luck, it’s about method.

Bad ingredients spoil the batch

When a gen AI model is trained on biased data, the bias quietly grows. Over time, the model may reinforce or even amplify biased patterns, leading to increasingly unfair or discriminatory decisions in critical areas such as lending, credit scoring, pricing, and customer targeting.

Take lending as an example. Even if gen AI is not directly applied to lending decisions, it can influence them via data preparation, automation, or risk analysis processes. If a model is founded on historical lending data shaped by credit access inequities, it may replicate the patterns, unfairly penalizing some customer segments.

The consequences are far-reaching, exposing the bank to risk on multiple fronts. Non-compliance with regulatory policies for fairness can result in large financial penalties. And the reputational damage and loss of customer trust can be even more harmful when bias comes to light.

Baking in responsibility

Addressing gen AI bias isn’t just a job for data scientists or IT teams. Ethical and responsible use of gen AI is a board-level issue that intersects with risk management, compliance, brand reputation, and long-term growth strategy.

Regulators have already signaled that the scrutiny placed on AI is set to intensify, although leadership shifts in Washington also raise uncertainty about the scope of revamped banking regulation overall. Globally, emerging frameworks include the EU’s AI Act, and to date, proposed U.S. legislation had emphasized transparency, accountability, and bias mitigation. U.S. agencies have also examined how algorithms impact fair lending. As with any regulations and the future makeup of regulatory agencies, many proposed iterations could follow. There’s little doubt, however, that AI will feature in U.S. banking regulatory considerations going forward.

To stay ahead, banking leaders need to be proactive about fostering ethical use of gen AI, establishing policies, setting standards, and implementing robust governance measures. Sifting out bias and baking in responsibility will deliver better, fairer gen AI outcomes that build trust rather than eroding it.

A recipe for responsible AI

Just as a clear, well-structured recipe helps avoid baking disasters, strategic frameworks offer steps to mitigate the risk of gen AI bias.

1. Use High-Quality Ingredients

What you put into a GenAI algorithm determines what comes out. So, invest in diverse, representative, unbiased data. All data should be carefully curated and audited so that issues can be identified and fixed. Bias with direct links to race, gender, or age may be easy to detect. However, some biases stay hidden in the mix and only become apparent when they are perpetuated over time.

2. Test the Mix Regularly

Ongoing bias testing and auditing should be central to banks’ GenAI development, deployment, and management. This involves continuous monitoring, with models stress-tested across different customer segments and scenarios to identify any bias impacts. Ensure direct and indirect bias is accounted for. Seemingly neutral factors such as postcodes can act as a proxy for race or other personal characteristics that have historically been subject to discrimination. Regular internal or external audits can surface hidden patterns that might otherwise be missed.

3. Ensure Clarity in Every Layer 

Each stage of the GenAI model must be transparent and explainable. This is vital to enable effective audits which catch emerging bias early. Explainable AI (XAI) tools can be used to illuminate how models make decisions, what data points they consider, and why certain outcomes occur. Making AI-powered decisions explainable to bank customers as well as employees fosters deeper trust and may deliver competitive advantage over organizations that do not – or cannot – achieve this level of transparency.

4. Make Accountability Part of the Mix

Human oversight and transparency are essential, especially when it comes to decisions that affect customers’ financial stability. Clear lines of accountability must be established: who is responsible for approving AI models, monitoring outcomes, and escalating concerns when issues arise? Coupling this with structured human-in-the-loop processes enables rapid intervention if it appears that sensitive decisions – such as loan or credit card approvals – have been influenced by bias.

5. Follow Protocols

Robust procedures should be implemented across the organization to ensure consistency in bias detection and mitigation, but GenAI governance cannot function in isolation. Banks need cross-functional governance models with oversight from compliance, legal, technology, risk management, and business leaders. This ensures GenAI initiatives align with organizational appetite for risk, regulatory obligations, and customer commitments. Banks may also consider establishing an AI ethics committee or appointing AI risk officers to guide policy development, monitoring, and ethical considerations across the AI lifecycle.

Ethical by design

Responsible gen AI is not the cherry on the cake; it must be baked in from the beginning, shaping every aspect of how the technology is developed and used. The risk of bias is significant and persistent, requiring careful assessment before model training and continuous monitoring after deployment. This demands high-quality data, transparent processes, human oversight, and strong governance at every stage.

One upside of a rigorous approach to responsible gen AI is that it can become a positive differentiator, boosting customer trust and commercial performance.

An investment in gen AI and especially, ethical gen AI practices, can help position financial institutions to:

• Build deeper customer trust through fairness and transparency.
• Reduce regulatory exposure and demonstrate leadership to supervisory bodies.
• Differentiate competitively, offering personalized services while upholding customer-centric values.
• Drive sustainable growth, harnessing gen AI’s full potential across personalization, customer engagement, and operational efficiency.

AI bias is a complex challenge that touches many aspects of a bank’s operations and values. Leaders who build strong, ethical foundations will safeguard their organizations against future risks and create enduring trust in an increasingly digital financial world. The future of banking belongs to those who bake in ethics from the outset.

Deborah Koens is the Services Business Leader for Amdocs Cloud Studio.