How financial data can inform policy: Key insights from our webinar with Joseph Rowntree Foundation

At Smart Data Foundry, we believe that financial data has the potential to shape better policies, improve economic decision-making, and ultimately benefit society. But how can this data be used responsibly, and what challenges must be addressed?

These were the key questions explored in our recent webinar, where experts from Smart Data Foundry, the Joseph Rowntree Foundation (JRF), Resolution Foundation, the University of Edinburgh, and the University of Nottingham shared their insights. The discussion covered the opportunities and challenges of financial data and how researchers can use it to uncover new insights into income volatility, fuel poverty, and economic security.

Why Financial Data Matters for Policy

JRF Insights Infrastructure was launched a year ago to address a critical gap in evidence on income volatility and economic security in the UK. Rosario Piazza, JRF’s Chief Insights Architect, explained,

We wanted to try a new approach, using a new dataset, to understand fluctuations in income and economic security at an individual level. But we also wanted to provide free access to close to real-time data for research and analysis purposes.

This initiative has opened up opportunities for policymakers and researchers to explore financial trends with a level of previously unavailable detail. However, as our panellists highlighted, working with financial data also presents unique challenges.

Key Takeaways

Financial data fills critical gaps in economic research

Traditional surveys like those from the ONS and DWP, are useful but often slow. Real-world financial transaction data provides a more immediate and detailed picture of income volatility, spending patterns, and economic security.

Data preparation is crucial

Financial data isn’t research-ready. Cleaning, structuring, and ensuring data literacy (e.g., coding skills in Python or R) are essential steps before analysis can begin.

Privacy and security must come first

De-identified financial data is stored in a secure, Trusted Research Environment (TRE) to ensure responsible handling while maintaining access for approved researchers to derive valuable insights.

Representativeness is a key concern

While Smart Data Foundry’s Income Volatility dataset covers a sample of the UK banked population, researchers must carefully consider how well financial data reflects different demographic groups. Magdalena explained that the dataset encompasses around 10% of the population of Great Britain, with 1.2 million accounts included in the Income Volatility dataset.

This isn’t a niche dataset; it’s a significant sample with robust geographical and demographic coverage.

Alternative definitions for financial hardship are needed

Existing measures, such as fuel poverty classifications, may not fully capture the realities low-income households face. Financial transaction data can help refine these definitions for more accurate policymaking.

Combining financial data with lived experience strengthens insights

Data alone doesn’t tell the whole story. By integrating it with real-life experiences, policymakers can better understand what is happening and why.

Timely, high-quality data is essential for effective policymaking

Economic pressures, such as the cost-of-living crisis, are evolving rapidly. Access to up-to-date financial data can help policymakers respond more effectively.

Using Financial Data to Understand Economic Pressures

Mapping Financial Vulnerability: How Economic Shocks Impact Income and Spending

Jonathan Crook’s team is investigating financial vulnerability across the UK using transaction data to understand how economic changes, such as interest rate hikes, inflation, and Universal Credit adjustments, impact income and spending, particularly for low-income groups. Their research compares different definitions of vulnerability, maps geographic and demographic trends, and analyses coping strategies like cutting expenses or borrowing.

The teams' preliminary findings show that income volatility is highest among young people, men, and both the lowest and highest income deciles, with fluctuations increasing during economic shocks like COVID-19. By utilising the Income Volatility Dataset in the future, the hope is to provide a richer view. Jonathan noted,

These insights are of absolutely crucial policy importance, if policymakers are going to intervene, they need to understand not just who is vulnerable, but how they respond to financial shocks.

Tracking Income Volatility and Essential Spending

Simon Pittaway shared how financial data is helping researchers quantify the financial pressures facing low to middle-income households “We feel pretty certain that the share of income spent on essentials like food, clothing, and housing has been rising over time. But mapping exactly how much worse it’s got has been difficult.”

Simon’s work at the Resolution Foundation focuses on using real-world financial data to track how people’s spending patterns are changing. His team works on the research for Unsung Britain, a major project investigating the shifting financial realities of those in the bottom half of the income distribution.

“The ONS data is useful, but it’s often slow and becoming less reliable. With the cost-of-living crisis moving so fast, we need data that can keep up. The Income Volatility data is really granular in terms of its time and you can pick up changes that occur in very short time periods.”

Rethinking Fuel Poverty Definitions

Torran Semple, a PhD researcher at the University of Nottingham, is using financial data to challenge existing definitions of fuel poverty, “Fuel poverty definitions have changed a lot over the years, but are they actually helping us identify the right people?”

His research suggests that official estimates may be underestimating the problem. Working with local councils, he found that self-reported fuel poverty levels were 2.5 times higher than government statistics. Moving forward, he will be incorporating Smart Data Foundry’s Income Volatility Data to explore alternative definitions:

To challenge the current approach, we need better data on income patterns. The financial data from Smart Data Foundry could be a game-changer, allowing us to explore new definitions like the Scottish Government’s approach, that take living standards into account.

Looking Ahead

This webinar was an important discussion on how financial data can inform policy, and it’s just the beginning. As we continue working with partners like JRF and leading researchers, we aim to provide policymakers with the best data to drive impactful, evidence-based decisions.

We look forward to sharing more updates from Jonathan, Torran, and Simon’s research and exploring further ways to make financial data work for the public good. If you’re interested in collaborating or learning more, get in touch!