Shortchanged: Uncovering and Analyzing Intimate Partner Financial Abuse in Consumer Complaints
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913431192338432 |
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| author | Bhattacharya, Arkaprabha Lee, Kevin Ravi, Vineeth Staddon, Jessica Bellini, Rosanna |
| author_facet | Bhattacharya, Arkaprabha Lee, Kevin Ravi, Vineeth Staddon, Jessica Bellini, Rosanna |
| contents | Digital financial services can introduce new digital-safety risks for users, particularly survivors of intimate partner financial abuse (IPFA). To offer improved support for such users, a comprehensive understanding of their support needs and the barriers they face to redress by financial institutions is essential. Drawing from a dataset of 2.7 million customer complaints, we implement a bespoke workflow that utilizes language-modeling techniques and expert human review to identify complaints describing IPFA. Our mixed-method analysis provides insight into the most common digital financial products involved in these attacks, and the barriers consumers report encountering when doing so. Our contributions are twofold; we offer the first human-labeled dataset for this overlooked harm and provide practical implications for technical practice, research, and design for better supporting and protecting survivors of IPFA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13944 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Shortchanged: Uncovering and Analyzing Intimate Partner Financial Abuse in Consumer Complaints Bhattacharya, Arkaprabha Lee, Kevin Ravi, Vineeth Staddon, Jessica Bellini, Rosanna Computers and Society Cryptography and Security Human-Computer Interaction Digital financial services can introduce new digital-safety risks for users, particularly survivors of intimate partner financial abuse (IPFA). To offer improved support for such users, a comprehensive understanding of their support needs and the barriers they face to redress by financial institutions is essential. Drawing from a dataset of 2.7 million customer complaints, we implement a bespoke workflow that utilizes language-modeling techniques and expert human review to identify complaints describing IPFA. Our mixed-method analysis provides insight into the most common digital financial products involved in these attacks, and the barriers consumers report encountering when doing so. Our contributions are twofold; we offer the first human-labeled dataset for this overlooked harm and provide practical implications for technical practice, research, and design for better supporting and protecting survivors of IPFA. |
| title | Shortchanged: Uncovering and Analyzing Intimate Partner Financial Abuse in Consumer Complaints |
| topic | Computers and Society Cryptography and Security Human-Computer Interaction |
| url | https://arxiv.org/abs/2403.13944 |