- AI can automate routine operational tasks in trade finance, freeing many women – who have historically worked in support roles – to pursue more strategic and senior positions.
- If designed with inclusivity in mind, AI can reduce bias in recruitment and talent evaluation, helping promote more diverse perspectives within the industry.
- By easing learning barriers and handling complex analysis, AI can broaden access to expertise in trade finance, supporting women’s advancement and participation across the sector.
Artificial intelligence’s (AI) promise for trade finance isn’t just limited to automating formerly manual processes as a means to improve efficiency and accuracy: it can also play a critical role in enabling women’s inclusion and advancement across the industry.
In trade finance, women have historically been concentrated in the ‘support’ layer, largely working in operations, client service, or risk administration. With AI primarily touted as an automation tool to replace manual and repetitive work, it can unleash the space for women to take on tasks that utilise and reflect their full skillsets, paving the way for their advancement into senior positions.
Because AI is also increasingly being used to reduce bias in hiring, it can operate as a tool to further women’s inclusivity in the sector, while promoting diverse thinking that breaks cognitive biases and offers a multitude of perspectives, raising alternative viewpoints that humans overlook – a much-needed approach in a field as complex as trade finance.
Women make up around 45-60% of the workforce in major banks, so AI could be a game changer for women in banking, and consequently for trade finance, and global trade at large.
AI: A promising contender to bias
The benefits of AI for inclusivity can start in the hiring process. According to the World Economic Forum (WEF), over 90% of employers depend on some form of automation for filtering or ranking job applications. While the ever-growing number of applicants is making it increasingly difficult for recruiters to manage hiring processes on their own, relying on traditional AI models risks replicating deep-rooted human biases in automation.
For AI not to reproduce human biases, AI models need to be developed in a way that doesn’t reproduce centuries-old human misconceptions. According to a 2024 study by the United Nations (UN), large language models (LLMs) tend to portray women in domestic roles, associating them with words such as ‘home’ and ‘family’. The UN Development Programme (UNDP) also finds that women make up just 22% of AI professionals, reflecting their absence from developing these models.
While human bias can never fully be eradicated, automated bias perhaps can. For that to take place, LLMs need to be developed with inclusivity embedded into the entire process.
If developed with this in mind, AI can shift how talent is approached, applying an objective stance that’s not constrained by a narrow line of thought. Through guiding it with unbiased prompts and parameters, AI power can be harnessed to appreciate and promote outside-the-box perspectives that challenge the status quo.
Enabling the middle layer beyond operational work
Beyond hiring, AI can assist and enable middle-layer roles. Organisational structures are getting leaner and retention through succession planning is becoming more important. The middle layer in trade finance – a layer which is becoming increasingly more empowered in decision-making – is where the succession pipeline is built, and AI is playing a role in its evolution.
AI taking over the more manual, bureaucratic tasks is removing these burdens from the middle management. The middle layer of trade finance was previously focused on operational work such as sanctions screening, document checking, and compliance checks.
However, these responsibilities are now increasingly automated. AI is being used to conduct screenings and verifications, including anti-money laundering (AML) and know your customer (KYC) checks, offering a faster, more accurate, and cost-efficient alternative.
Agentic AI that uses LLMs to independently reason and plan are now taking over client services as well, providing multi-step workflows with minimal human oversight. AI is also able to spot shifting trends and send early alerts on possible issues within the industry, reducing the need for individuals in the middle layer to focus on commonplace risk, which gives them the time and space to address more long-term, macro risks.
Automation frees individuals to focus on more complex nuances, insights, and value-adding areas that influence decisions, such as local intricacies, market specifics, and how to extrapolate value.
The automation can then allow employees in the middle layer to become more involved, try different approaches, and learn from their choices. They are given more opportunities to use and showcase their skillset to benefit the organisation and its clients.
As AI takes on routine tasks, more women can step into positions or seize opportunities where there is a need for human expertise and human connection, and they can show the true extent of their capabilities.
Women have historically been undermined in roles that require critical thinking and strategic decision-making. According to a Women in the Workplace study, 38% of women report having their judgment questioned in their area of expertise. This amplifies the need for increased visibility of women’s abilities, which is where AI comes in, granting women the space to take on tasks beyond the routine, where they can showcase their expertise.
AI can also be critical in easing the barriers for this expertise to grow.
From imposter syndrome to inclusive systems
Ever-changing regulations and an endless list of acronyms can make trade finance daunting to beginners or career transitioners. Expertise in the field requires exposure, application, and time.
While imposter syndrome in a field this complex is common – and can sometimes feel like it will never fully go away – inclusion means being given permission to experiment, fail, learn, or adapt. If a sector is systemically inclusive, it is one where curiosity is encouraged, positioning the traditional ‘imposter syndrome’ to be interpreted as a sign of growth and potential.
Asking questions, expressing challenges, and seeking guidance – habits common among those with imposter syndrome – all feed growth. Inclusive spaces make room for such growth rather than dismissing these individuals as inadequate, and automation can contribute to this.
AI can flatten the learning curve in trade finance by providing real-time analysis about structures, identifying risks and mitigants, and highlighting or revealing comparable deals. Thereby, groups that aren’t in the traditional ‘inner circle’ of trade finance can disproportionately benefit from AI’s abilities, be it women returning to the workforce after a career break, those working flexibly from home, or those who work outside of the industry’s ‘power centres’.
It’s crucial to note that external encouragement through both interpersonal relationships and institutional governance is also extremely valuable in reframing inclusivity in the sector. Contrasting opinions exist to birth good ideas and diverse thinking: a degree of constructive tension is necessary for individuals to push one another to grow.
However, at the risk of falling into a cliche, what is perhaps even more important is believing in oneself.
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As AI lowers technical barriers, confidence, initiative, and mutual support become the differentiators. Technology alone cannot account for inclusive culture and mindsets embedded in the heart of financial institutions.
Technology, from AI’s complex ability to automate compliance screenings to something as simple as Teams connecting people across the globe, has a crucial role to play in making trade finance more inclusive.
AI has the potential to automate the repetitive work that has historically trapped female talent in the middle. It can remove human bias from how talent is approached, place value onto diverse-thinking, and unlock the opportunity to gain expertise from tight circles to inclusive systems.
As we approach AI for inclusivity, we must remain cautiously optimistic, not because technology is neutral, but because we can be conscious and deliberate when it comes to shaping and using this technology. We have a chance to build a trade finance ecosystem where intelligence is augmented and access to opportunity is broadened.
But it’s important to remember, technology alone is not sufficient. Male allies in the industry must support the women they work with – not just for the women’s sake, but for the industry’s sake.
The question isn’t whether women in trade finance are ready for an AI-driven future: it’s whether we are ready to design a future in a way that truly values women in the industry.
