As we mark International Women’s Day (IWD) and celebrate the Women in FinTech Powerlist (WIFP), on which I have been honoured to feature since 2019, I am now in the unique position to judge this year’s candidates. This affords the opportunity to reflect on inclusivity and the impact we (women) are generating beyond the hype. Female leaders are, in our respective fields asked to participate in panels, give keynote speeches, etc., around greater inclusivity via data-driven technologies across the social, economic, and environmental domains. Unfortunately, such engagement predominantly results in a talking shop. Before, I hear the cry, we have made progress! Undoubtedly, we are doing ‘better’ which as humans, is all we can realistically strive for, to incrementally change our mindsets, culture, and norms, translated into action. Women featured in WIFP exemplify this progress in terms of diversity, drive, and purpose. Likewise, IWD champions break the narrative and drive change for the better.
Reflecting on the above through a critical realist lens - are females sufficiently included in key decision-making processes, beyond tokenism or merely ticking the ED&I box or quota? Let’s look at the evidence. Beginning with FinTech, my area of expertise, inspiring women to start up in this sector makes for a reality check on progress. Approximately 11% of start-ups in London are female-led and despite becoming successful, tend to be replaced by males during the scale-up stage (dropping to <4% outside of London). Writ large in numerous reports is the evidence that although ‘FinTech for good’ is making inroads into inclusion (digital and financial) cited as the purpose of 97% of organisations, the positive impact on the planet is only 9%. What for inclusion? Starting with education’s role in access to computer science/STEM skills required to navigate and drive change in the digital era. Not great reading either, in general, 80% of applications remain white and male for such degrees. Without diversity of thought and perspectives as standard in designing machine learning (ML) algorithms and artificial intelligence (AI) tools, the ‘garbage in, garbage out’ Ava Lovelace strapline prevails, and inclusion suffers. Despite female STEM champions pushing the bar (cf. Powerlist), universities struggle to attract more women to study computer science, due to reported toxic environments and pressure (sound familiar to industry?).
Fast forward to today - the era of unparalleled digital innovation, fairness, and data transparency heralding the end of financial exclusion. The situation should improve, right? Sadly not.
Professor Karen Elliott, Birmingham Business School
The bias problem in ML/AI rears its head in accessible credit scoring and responsible lending, or computer says ‘no’. I experienced this frustrating situation >15 years ago, as a single parent post-divorce and being asked, may we include your (ex)husband’s credit profile, now you are a PhD student (i.e., red flag risk)? I became subject to a premium - flagged as a repayment risk despite a full banking history with the same institution. Fast forward to today - the era of unparalleled digital innovation, fairness, and data transparency heralding the end of financial exclusion. The situation should improve, right? Sadly not. Recently, in extracting my basic Ford Puma from a multi-car to single-car policy, paying £300 per annum, the new quote rose to a staggering £670. The reason? My inability to add additional cars, whilst the policy owner’s premiums remained the same, as they could.
Fair? The poverty premium problem emerges, which is rarely flagged at digital innovation events, nor discussed around the impact or bias embedded in ML, excluding female and other variables (ethnicity, males, postcode, etc.) perspective in such scenarios. That is, a woman (or man) leaving a relationship and is unable to add another car to the policy receives an inflated insurance premium after losing a second income and (usually) being financially responsible as the primary parent. The same car cover was secured for £255; this was achieved by knowing how to search and strike the best deal, demonstrating the value of digital literacy in aiding societal equality and inclusion. In pointing out the ethical flaws of the multi-car policy and business model to the representative (whom I clearly bored senseless), I received the crib sheet ‘thank you for your feedback’ response, progress in >15 years? I felt disappointed and voiceless as a female working in this very sector, despite multiple women driving inclusion - back to the question, are women influencing key decision-making processes?
Are we doing ‘better’ ergo just a talking shop? My scenario is but one instance of a lived experience in the ‘insura-/fin-/prop-’ tech era, and in finance would this comply with Consumer Duty’s purpose to assist vulnerable customers, which come in a variety of profiles (arbitrarily generated by AI, of course). Awkward critical questions must be repeatedly posed to place inclusion at the heart of data-driven co-design and innovation if the trend in ‘better’ is authentic. Without it, my loathing of the tickbox mentality endures, responsibility at all levels to drive inclusion becomes the proverbial ‘hot potato’ nobody wants to address, the box was ticked…Until there is a paradigm mindset shift, and we collectively respect the input of diverse perspectives (e.g., a range of genders and ethnicities) bias in the system will continue to increase the digital divide and exclusion.
