Evidence from mass-transactional data that chaotic spending behaviour precedes consumer financial distress

The prevalence of digital footprints can allow researchers to study the personalities of millions of individuals with improved ecological validity. We present spending entropy as a candidate personality trait derived as a feature of an objective big data source---mass-transactional data from millions of bank accounts. Entropy measures the unpredictability of spending and acts as a measure of the chaotic nature of a person's life. Over and above how much money people spend, and what the money is spent on, spending entropy positively relates to future financial distress. High entropy leads to increased probability of missed payments across financial products. Entropy temporally relates to future distress three months ahead including more severe measures of distress. We replicate our findings in personal current account, loan, and mortgage holders in a second financial institution. Our findings suggest that high-dimensional data can be used to build psychological traits that predict outcomes in novel situations.