“What’s in the box?”
This question can come from a joyous child tearing into a nondescript package – or it can come from Brad Pitt’s infamous line in the crime thriller Seven.
Most of the time, what’s in the box ends with smiles and laughter.
In banking, though, the “box” is figurative – a black box where models and AI agents process and decide on data. And what’s inside that box is no laughing matter. If the wrong things come out, the story can shift from one of financial success to a regulatory cautionary tale.
Seven earned approximately 10 times its budget at the box office. Banks, by contrast, have no interest in learning what it’s like to star in their own version of a crime thriller.
When AI makes decisions that matter
That’s not just theory, either. It’s already happening.
Recent research shows how decisions inside the box can create real-world harm. One study revealed that large language models tend to recommend higher interest rates and loan denials for Black applicants compared to white applicants, even when credit scores are the same. On average, Black applicants need credit scores 120 points higher to get approved at the same rate, according to the study.
Such discrepancies can be systemic across major LLMs. And a simple solution, like adding more emphasis to the GenAI prompt, doesn’t solve the issue.
Why banking needs trustworthy AI now
As AI continues to transform banking and its processes, the need for trustworthy AI has never been more apparent. Models and AI agents are making decisions inside the (figurative) box that businesses must be able to decipher and defend – and that’s where explainability and trustworthy AI begin.
Trustworthy AI is a framework for designing, developing and deploying AI systems that are safe, ethical, transparent and accountable. At its core, trustworthy AI ensures that AI serves humanity, enhancing well-being, promoting fairness and avoiding harm.
Demystifying the machine: The role of explainability
One of the most critical pillars of trustworthy AI is explainability. It refers to the ability to understand and articulate how and why an AI system makes a particular decision. Without it, users are left in the dark, unable to trust or challenge the outcomes of AI-driven processes.
For instance, in the aforementioned study, imagine being denied a loan or receiving a pricing recommendation without knowing why. Even with AI-driven efficiency gains, this lack of transparency can be potentially dangerous.
Explainability empowers users, regulators, and developers to scrutinize AI decisions, identify biases and ensure accountability.
Transparency builds trust (and compliance)
Explainability is a key pillar for building trust. It ensures organizations can clearly communicate what their AI systems do, why they make certain decisions and how they reach their conclusions. This transparency is not only a technical requirement but also a moral and strategic imperative.
Moreover, explainability supports regulatory compliance. In a highly regulated industry like banking, the consequences of noncompliance can be severe. As governments around the world introduce AI regulations, such as the EU AI Act, banks must demonstrate that their AI systems are not only effective but also fair and understandable. By proactively embedding explainability into AI governance, firms can stay ahead of regulatory requirements and avoid costly missteps.
For a deeper dive on this topic, watch this video: Circle of Trust: A 360-View of Developing Trustworthy AI.
Shaping a culture of explainability
Explainability isn’t just about compliance. It’s about culture. Banks that prioritize explainability foster a culture of responsible innovation, where employees are empowered to question, improve and trust the systems they build and use. Keeping humans involved – rather than relying solely on autonomous decision-making – ensures employees remain engaged and accountable. This cultural shift is essential for long-term success in an AI-driven world.
This culture of responsible AI use extends beyond a company’s four walls. Firms that demonstrate ethical and responsible AI practices are more likely to succeed, as consumers and employees increasingly expect businesses to act with integrity. In fact, 63% of consumers make purchasing or advocacy decisions based on a brand’s beliefs and values. This expectation for trust and accountability also applies to the organizational use of AI.
For more insights and statistics on the importance of responsible AI, check out the e-book A Comprehensive Approach to Trustworthy Data and AI Governance.
Opening the box helps AI work for everyone
Trustworthy AI and explainability are not luxuries. They are necessities. You need to know what is happening inside that black box. Banks cannot simply “set it and forget it” because AI knows best. Spoiler alert: ignoring what’s inside the box can lead to outcomes no business wants to face – costly mistakes, regulatory penalties, or reputational damage.
By embracing explainable and trustworthy AI, banks can build deeper trust with customers, meet regulatory expectations and drive innovations that genuinely benefit society – all while keeping the surprises safely out of the box.
Ready to learn more about responsible AI innovation? Start here.
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