Data-Driven or Dataless? Detecting Indicators of Mental Health Difficulties and Negative Life Events in Financial Resilience Using Prompt-Based Learning

Financial resilience has been an important area of focus for the business sector since the outbreak of the pandemic. Currently, the assessment of financial resilience is typically completed through the review of financial statements. However, such resilience is commonly linked to negative life events and may be further impacted by the presence of mental health difficulties. As such, identifying and understanding these elements may provide a more complete understanding of an individual’s financial situation. We discuss the development of a challenging automated financial resilience detection system that aims to identify factors that may have negative impacts upon the resilience of individuals. This makes use of textual data to identify elements, such as the occurrence of negative life events or mental health difficulties, that indicate that individuals may be vulnerable to exploitation through the misselling of products. In addition to a traditional data-driven supervised approach, this work also demonstrates applying prompt-based learning to these tasks without the need for the training data (i.e., a dataless approach).