Detecting Consumers' Financial Vulnerability using Open Banking Data: Evidence from UK Payday Loans
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910196668825600 |
|---|---|
| author | Medina-Olivares, Victor Calabrese, Raffaella |
| author_facet | Medina-Olivares, Victor Calabrese, Raffaella |
| contents | This paper examines whether repeated payday loan use, commonly known as the debt trap, harms borrowers' financial wellbeing. Using Open Banking data from 1,815 UK borrowers observed between 2017 and 2018, we model borrowing intensity using a two-state hidden Markov model (HMM). The HMM outperforms single-regime alternatives and identifies two distinct borrowing patterns: occasional (low-intensity) and persistent (high-intensity) use. Each regime exhibits a characteristic relationship between borrowing intensity and wider transaction behaviour. We translate the decoded state sequence into a practical monitoring rule based on sustained high-intensity exposure. Defining a trigger event as 12 consecutive weeks in the high-intensity regime, we find that 36.4% of borrowers experience at least one such event. Among those who do, high-intensity weeks represent 17.8% of all borrower-week observations on average. Together, these results provide evidence for a persistent high-intensity borrowing pattern and demonstrate that it can serve as a simple, interpretable rule for monitoring prolonged reliance on payday loans. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_01749 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Detecting Consumers' Financial Vulnerability using Open Banking Data: Evidence from UK Payday Loans Medina-Olivares, Victor Calabrese, Raffaella Applications General Finance This paper examines whether repeated payday loan use, commonly known as the debt trap, harms borrowers' financial wellbeing. Using Open Banking data from 1,815 UK borrowers observed between 2017 and 2018, we model borrowing intensity using a two-state hidden Markov model (HMM). The HMM outperforms single-regime alternatives and identifies two distinct borrowing patterns: occasional (low-intensity) and persistent (high-intensity) use. Each regime exhibits a characteristic relationship between borrowing intensity and wider transaction behaviour. We translate the decoded state sequence into a practical monitoring rule based on sustained high-intensity exposure. Defining a trigger event as 12 consecutive weeks in the high-intensity regime, we find that 36.4% of borrowers experience at least one such event. Among those who do, high-intensity weeks represent 17.8% of all borrower-week observations on average. Together, these results provide evidence for a persistent high-intensity borrowing pattern and demonstrate that it can serve as a simple, interpretable rule for monitoring prolonged reliance on payday loans. |
| title | Detecting Consumers' Financial Vulnerability using Open Banking Data: Evidence from UK Payday Loans |
| topic | Applications General Finance |
| url | https://arxiv.org/abs/2306.01749 |