Mood & Money Lab
where finance meets mental health.
University College Dublin
We use machine learning and statistical analysis to understand how financial behaviour and mental health are interconnected — and to build tools that help people with mental health conditions, particularly bipolar, help manage their finances better.
Based at the School of Computer Science, we partner with clinical institutions, financial services, and patient advocacy groups to conduct research that is rigorous, fair, and grounded in real-world impact.
Featured Research what we're working on.
All projectsFinHealth: Assessing Mood States from Financial Behaviour
activePeople with bipolar disorder may experience periods of high mood (mania) that can lead to impulsive spending and serious financial harm, including debt or bankruptcy. Current mood monitoring relies largely on self-report, which may miss early warning signs. This study explores whether patterns in bank transaction data—such as changes in spending—can help detect mood shifts earlier and support better financial management. Using anonymised transaction data and machine learning, we aim to identify links between financial behaviour and mental health outcomes, providing a foundation for future financial interventions for this vulnerable population.
Participatory Interview Study
under reviewThere is limited data and little research on identifying meaningful features for assessing mood from financial behaviour. This study addresses this gap using a human-centred, participant-informed approach that captures insights beyond those typically identified by clinical experts, while accounting for differences across geoeconomic regions to ensure broader applicability.
Recent News & publications.
Latest news
Our paper Manifestations of Mood in Money: Unravelling Financial Behaviours for Passive Assessment in Bipolar Disorder was accepted at the 20th EAI International Conference on Pervasive Computing Technologies for Healthcare.
Our paper Money and Mental Health: Spending as a Mental Health Indicator - Psychological, Behavioral, Economic Perspectives and Data Collection was accepted at CHI 2025 The Future of Money and HCI.
Featured publications
All publications →Manifestations of Mood in Money: Unravelling Financial Behaviours for Passive Assessment in Bipolar Disorder
Oluwadara Adedeji, John Olusegun, Keith Gaynor, Mark Matthews
Pervasive Health 2026
Mood and money are closely intertwined in bipolar disorder (BD), yet it remains unclear whether financial behaviour provides reliable mood-contingent signals, and it is largely absent from digital phenotyping research. Advances in open banking enable objective, passive measurement of financial behaviour at scale. We conducted semi-structured interviews with 19 (11 UK, 4 Ireland, 4 Nigeria) adults with BD to examine how mood manifests in financial behaviour, identify potential financial mood markers, and explore data-sharing preferences. Participants reported distinct mood-related changes in spending and reciprocal effects of money on mood. Three themes emerged: (i) symptomatic drivers shaped by credit access, seasonality, and social context; (ii) candidate markers such as transaction frequency, timing, category, and amount; and (iii) conditional willingness by most participants to share data influenced by privacy, anonymity, and granularity. High-mood spending reflected goal pursuit, generosity, and insomnia, while depression involved both reduced spending and retail therapy, challenging simple bidirectional assumptions. These findings provide empirical insight into financial data as a potential mood marker and inform future research and privacy-sensitive data collection.
Money and Mental Health: Spending as a Mental Health Indicator - Psychological, Behavioral, Economic Perspectives and Data Collection
Oluwadara Adedeji, John Olusegun, Mark Matthews
CHI 2025
Symptomatic financial behavior in bipolar disorder can increase stress, worsen symptoms, and hinder recovery. While spending sprees signal high mood states, research on personal experiences remains limited. This study explores mood-spending dynamics through semi-structured interviews in Ireland and Nigeria (N=5, projected N=10), assessing geo-economic influences and data-sharing preferences for future research. Participants reported varied impulsive spending patterns and emphasized privacy, security, and transparency in financial data sharing. These findings highlight the need for protective financial technologies for vulnerable individuals