AI Bias in Banking: Embrace Ethical Approaches to Mitigate Risk

Historic data used by banks may hold biases that reflect obsolete lending practices or longstanding social and economic inequalities. Data complexity coupled with the subtlety of some biased elements means issues may be hard to detect; they can be deeply embedded or hidden in plain sight.

As the banking sector’s reliance on GenAI increases, this is a growing concern. If GenAI models learn from biased data, outputs may conflict with current policy and regulatory requirements. Unless such issues are corrected, patterns of bias can escalate and expand over time as models continue to reinforce their learning.

Bias escalation poses a serious threat to fairness in banking decisions. And when banks are found to discriminate against certain customer groups, repercussions range from severe reputational damage to lawsuits and large financial penalties from regulators.

Addressing this can feel like an impossible challenge. GenAI adoption is critical to maximize efficiency and competitivity, but there’s no knowing what patterns it might find. If hidden biases do exist, it’s not feasible to locate and fix them all manually. However, there is a solution. Ethical and responsible GenAI practices can identify and eradicate emerging bias before it results in unfair decisions. Read on to find out more.

Avoid Unfair Banking Decisions With Responsible GenAI

As GenAI adoption unfolds, financial authorities around the world are monitoring it closely. In 2023, the US Consumer Financial Protection Bureau said “regulators need to stay ahead of [AI’s] growth to prevent discriminatory outcomes that threaten families’ financial stability.” More recently, the Australian Prudential Regulatory Authority highlighted specific ethical risks including “the potential for algorithms to develop biases that unfairly discriminate against groups of people or exclude them from some financial services entirely.”

It's not just regulatory bodies voicing concerns. Last year the European Central Bank published The rise of artificial intelligence: benefits and risks for financial stability. This report considers foundational models’ potential to learn, sustain, and amplify any bias inherent in the data they are trained on. It points out that it may be difficult for banks to identify and monitor algorithmic biases that lead to discriminatory customer treatment. The bigger industry picture is also considered. For instance, the report suggests that if most financial institutions use the same or very similar models provided by a few suppliers there’s a risk of systemic bias.

How to Curb GenAI Bias

Since historic bias can become deeply embedded and further perpetuated by GenAI, robust measures are needed to foster ethical practices and maintain them at scale. The following steps help put fairness, transparency, and explainability at the heart of GenAI use:

1. Establish policies to guide responsible use of AI via principles of fairness, accountability, and non-discrimination. Policies can also focus specifically on the avoidance of bias. For instance, they might outline the need for bias detection methods combined with strategies for bias mitigation as well as regular audits and assessments of GenAI outputs.
2. Set standards to provide users with practical guidance on how to comply with company policies for ethical AI. Important principles, such as transparency and explainability, can be translated into clearly defined actions mandated for every GenAI project.
3. Maximize system transparency, with clear reasoning for AI-supported decisions. Tracking how data flows through the system and how decisions are made aids this step. It ensures processes can be evaluated for fairness, with the source of any bias determined more easily.
4. Facilitate explainability by ensuring GenAI models document their decisions with information on how and why certain choices or inferences were made. This can quickly reveal whether bias has influenced the reasoning process.
5. Keep humans in the loop to ensure measures put in place to identify bias are properly implemented. Model behaviour should be monitored to check that decisions are ethically sound, with fallback mechanisms or manual override options implemented if a system exhibits bias.

Making these steps integral to model development and fully embedding them in operations reduces the risk of any historic bias growing and infiltrating real-world deployments.  

Navigating Ethical AI

Anyone involved in the design of GenAI models and frameworks should be educated about the risk of bias. Frameworks must be designed in a way that facilitates transparency and explainability, using techniques that make it easy for all users – including those in non-technical roles – to interpret the system.  

As use of GenAI evolves and expands in banking, there are many ethical factors to consider. Avoiding bias is just one of aspect of this. Amdocs strongly advocates setting up an AI Center of Excellence (CoE) to govern responsible and ethical use of the technology in line with industry requirements and company priorities.

To support banks and other financial organizations on this journey, we’ve created an enterprise playbook entitled: Five Pillars of Ethical AI. Topics covered include policies and standards, compliance, risk management, roles and responsibilities, transparency and explainability. Download it for free HERE.