Using banking transaction data for social good
At the Digital Footprints Conference, held in Leeds on 14th and 15th May, Research Coordinator Oliver Berry presented a poster on using de-identified consumer transaction data for research. This dataset covers 5.3m individuals aggregated over ~60 income and expenditure categories on a weekly basis.
The SWOT analysis looks at the strengths of using this data for research and the opportunities that come with it. Oliver also assesses the weaknesses of the data, and how Smart Data Foundry mitigates these.
Accessing this data for research
Smart Data Foundry is Smart Data Research UK's Financial Data Service (FINDS).
By supporting academic research into the fundamental causes of poverty, inequality, and economic wellbeing, FINDS data can reveal insights that lead to impactful solutions for these urgent societal issues.
Thanks to our trusted data partners, we offer access to consumer and SME financial data that was previously difficult to obtain, available in near real-time.
Our de-identified microdata and curated data collections provide remarkable granularity, encompassing a broad spectrum of financial variables. On the income side, this includes aspects such as salary, benefits, investments, child and pension credits, and more. The expenditure data is equally extensive, reflecting categories such as council tax, bills, housing, groceries, fuel, childcare, travel, and beyond.
With persistent IDs, researchers can track individuals over a five-year span, which facilitates thorough longitudinal analysis. This approach aids in identifying long-term trends and generates valuable insights into critical social and economic challenges.
Access can be requested via myFoundry, our secure data platform.
