In a recent panel discussion at our Women in Tech Leadership: Financial Services event — hosted by our team in London (in collaboration with Microsoft) — we were fortunate enough to gather several innovative and insightful AI leaders from some of the world’s largest financial and technology firms. They were invited to discuss a simple but consequential topic for the assembled audience: will Financial Services use AI to erode or entrench inequality.

AI and Financial Services: building a fairer future

Artificial Intelligence (AI) is reshaping Financial Services at an unprecedented pace. From credit scoring to customer support, AI promises efficiency and innovation, but it also raises critical questions about fairness, inclusion and trust.

Democratising finance through AI

Traditionally, access to credit and financial opportunities has been controlled by rigid systems that often excluded certain groups. AI offers a unique chance to meaningfully challenge and change that. By analysing alternative data such as rent payments, utility bills, and behavioural signals, AI can create a more holistic view of creditworthiness. This opens doors for individuals who lack traditional credit histories, such as young adults or those new to financial systems.

From reactive to empathetic banking

Progressive financial institutions are beginning to deploy AI to identify vulnerability signals, such as cognitive or financial distress, and offer support rather than penalties. This shift from reactive to empathetic banking demonstrates how technology can be a force for good when designed with inclusion at its core.

Accessibility and inclusive design

AI-driven tools are improving accessibility for customers with diverse needs. Voice-enabled systems for visually impaired users and coaching tools for neurodiverse individuals are examples of how FinTechs are embedding inclusivity into product design.

However, inclusion must remain a priority because, without it, technology risks reinforcing the very barriers it seeks to overcome. 

The challenge of bias and scale

AI is not immune to bias. In fact, it can amplify discrimination (as we have seen in many recent public instances) if historical data carries legacy prejudices. Unlike human decision-making, which is inconsistent but localised, biased AI can scale harm rapidly. Mitigating this risk requires diverse development teams, robust bias testing, and continuous monitoring, not just at launch, but throughout the lifecycle of AI systems.

Skilling for the AI era

As AI becomes ubiquitous (embedded in everyday tools and citizen services) organisations must invest in workforce skilling. Formal training, ethical frameworks, and escalation processes for complex cases are essential. Partnerships with community organisations can also help bridge the digital divide and prevent widening gaps between the digitally fluent and vulnerable.

Regulation and responsibility

Regulatory approaches vary. For example, the EU’s AI Act offers clarity and prohibited categories, while the UK favours a principles-based model that encourages flexibility and outcome-focused thinking. Both have merits but, ultimately, organisations must take responsibility for ethical AI deployment, prioritising fairness over short-term gains.

Positive use cases and future potential

Beyond risk, AI brings enormous potential for good:

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Alternative credit scoring

to include underserved populations

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Financial coaching

embedded in apps to improve literacy

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Accessibility tools

that empower customers with disabilities

AI strategy and implementation

Explainable AI

that transforms opaque decisions into actionable advice, building trust and transparency

Looking ahead, AI could accelerate breakthroughs in science, medicine, and beyond (and there are many real-world examples of this already) provided we harness it ethically and inclusively.  

Key takeaways for leaders 

  • Start with inclusion: Design systems that serve everyone, not just the digitally literate 
  • Audit for bias: Implement rigorous testing and monitoring throughout the AI lifecycle 
  • Invest in skills: Equip teams with the knowledge to use AI responsibly 
  • Think long-term: Fairness may not deliver immediate profit, but it builds trust and resilience  

AI is not just a technological revolution, it’s a societal one. The choices we make today will define whether it becomes a tool for empowerment or exclusion. 

Ultimately, it’s up to leaders in Financial Services and the broader technology industries to ensure that these powerful tools are being harnessed properly to create a fairer, more balanced workplace and society.  

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More from the event

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Four speakers seated in front of a lush green plant wall, engaged in a panel discussion at a Women in Tech Leadership event. Audience members are visible in the foreground.
Two speakers seated on stage in front of a green plant wall, addressing an audience at a Women in Tech Leadership event. A banner with the event name is partially visible on the right.
A speaker seated on a chair, holding a pen and notebook, speaking into a microphone during a Women in Tech Leadership event. A green plant wall is visible in the background.