On June 7, 1968, female machinists working at Ford Dagenham in London went on strike in protest of the gender pay gap. The demonstrations eventually resulted in the passing of the UK’s Equal Pay Act of 1970 and subsequently the Equality Act of 2010, which provides a legal framework to protect the rights of individuals and advance equality of opportunity for all.
Although it’s been over 50 years since the introduction of the Equal Pay Act, the issues of gender inequality and pay gap remain. According to the Office for National Statistics UK, the gender pay gap among all employees is 15.5%, which means, on average, women are paid 86p for every £1 paid to men. While employers recognize these gender imbalance issues, they often lack insights into where the pay gaps are, the reasons behind them and the best way to address them.
By leveraging technologies such as Open Banking APIs and machine learning, businesses can be better equipped with the insights needed to achieve their diversity and inclusion objectives.
Using Open Banking to Identify Pay Gaps
Open Banking – which provides third-party financial service providers with access to consumer banking transactions and other financial data through the use of APIs – is a recent advancement in the world of banking and financial services in the UK and EU. As part of this data-sharing service, third-party providers and fintechs can use Open Banking API technology to consolidate data from customers’ multiple bank accounts with their explicit consent. Although Open Banking is predominantly aimed at providing innovation in banking, its potential can be extended to solve the deep-rooted problem of gender inequality.
Employers in the UK and several EU countries are required to report metrics about gender pay gaps to the government or authorized bodies. No such regulations exist as yet for many other regions, however, such as the U.S., Ireland, China, UAE, Austria and Hungary. But in either case, reporting this information doesn’t equate to taking action on it. With technology-driven practices, businesses could gain not just visibility into gender pay gap issues but also clarity into where they’re most acute, which would better inform their strategies on achieving equality.
Under the Open Banking framework, data-aggregation fintechs (i.e., account information service providers) such as Revolut, Yolt and Plaid, or job posting sites such as LinkedIn and Glassdoor, could combine forces with regulators or work councils to produce a dashboard highlighting sector-specific metrics on gender pay, as well as insights into areas of discrepancy.
The salary data obtained by data aggregators with the user’s consent could be further enriched with elements such as age, gender, organization, industry sector and job title. This enriched dataset could reveal pay gap trends across industry sectors and job roles.
With such a dashboard freely available in the public domain, businesses would gain vital input for formulating their diversity and inclusion policies and monitoring their progress without having to invest in the technology infrastructure or data gathering themselves.
Financial services organizations and investment firms may also have a role to play, especially new types of businesses like The Big Exchange, an investment firm that invests only in companies that contribute to a positive social and environmental impact. The firm is already leveraging the Open Banking framework to offer an aggregated view of investments to its customers. It could extend this capability to also develop a gender pay gap dashboard, working with regulators through Open Banking standards.
Machine Learning Can Fix the ‘Leaky Pipeline’
Another gender equality issue that could be addressed through technology such as machine learning is “the leaky pipeline,” or the number of women who leave the technology profession. According to the WISE campaign, the percentage of tech professionals who are female has remained consistently low, at 16%, for the past decade. In the UK, although 35% of higher-ed STEM students are women, they make up only 24% of the STEM workforce.
There could be several reasons for women leaking out of STEM careers, including hostile work environments, lack of mentoring, lower recommendations, lower pay and work-life balance issues. These reasons could be discovered through the use of technology tools.
Companies that are already leveraging emerging technologies could feed data attributes like salary, benefits, bonuses, promotions, performance reviews, commute time, family details and maternity details into machine-learning algorithms to score employees on satisfaction levels. These algorithms could predict attrition patterns among its female employees and highlight the underlying reasons for departures by attributes such as department or job level.
By coupling HR data with employee survey data, feedback mechanisms and reviews on job posting sites, businesses could then apply sentiment analysis to identify ways to increase retention among women employees. For instance, analysis might reveal that women were unhappy about overtime and travel but satisfied with pay and management restructuring. Through these insights, organizations could better understand the probability and causes of female attrition and work on focused targets for remediating the issue.
Bringing it into Play
While data-driven insights could provide a jumpstart for informed diversity strategies, technology shouldn’t be considered a silver bullet for resolving gender inequality issues. Bias in data collection, for example, could cause unintended discrimination against a particular segment. Businesses need to ensure the right controls and processes are in place so that technology-enabled decisions are fair and accurate.
In my conversations with clients, it’s clear that organizational objectives differ when it comes to diversity strategy; some are moving forward because they believe it’s the right thing to do, while others are seeking tangible business benefits before inching ahead. Either way, research shows that gender diversity is clearly correlated with profitability but that women still remain underrepresented.
With the right mix of technology, behavior and cultural change, gender inequality will no longer be a “blind spot.” From promoting gender balance in decision making to incorporating equal pay, it’s about time we acknowledge the half of our population as equals.
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