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This research article analyses and demonstrates the hidden implications forfairness of seemingly neutral data coupled with powerful technology, such asmachine learning (ML), using Open Banking as an example. Open Banking hasignited a revolution in financial services, opening new opportunities forcustomer acquisition, management, retention, and risk assessment. However, thegranularity of transaction data holds potential for harm where unnoticedproxies for sensitive and prohibited characteristics may lead to indirectdiscrimination. Against this backdrop, we investigate the dimensions offinancial vulnerability (FV), a global concern resulting from COVID-19 andrising inflation. Specifically, we look to understand the behavioral elementsleading up to FV and its impact on at-risk, disadvantaged groups through thelens of fair interpretation. Using a unique dataset from a UK FinTech lender,we demonstrate the power of fine-grained transaction data while simultaneouslycautioning its safe usage. Three ML classifiers are compared in predicting thelikelihood of FV, and groups exhibiting different magnitudes and forms of FVare identified via clustering to highlight the effects of feature combination.Our results indicate that engineered features of financial behavior can bepredictive of omitted personal information, particularly sensitive or protectedcharacteristics, shedding light on the hidden dangers of Open Banking data. Wediscuss the implications and conclude fairness via unawareness is ineffectivein this new technological environment.

The Double-Edged Sword of Big Data and Information Technology for the Disadvantaged: A Cautionary Tale from Open Banking