Reinforcement Learning for Household Finance: Designing Policy via Responsiveness

Arka Prava Bandyopadhyay and Lilia Maliar

No 19794, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: We use model-free reinforcement learning (RL) to investigate how a mortgage servicer can optimize her actions towards a borrower. Our methodology differs from the conventional heuristic approach, since we do not use subjective and qualitative judgments of industry and legal experts. We are the first to exploit the borrower’s soft information post-securitization and her responsiveness to the servicer, to estimate an RL-policy rule. When maximizing her reward, the servicer learns the borrower’s type dynamically. By doing so, the servicer can preempt the borrower’s adversarial behavior, thereby increasing the borrower’s cooperation.

Date: 2024-12
References: Add references at CitEc
Citations:

Downloads: (external link)
https://cepr.org/publications/DP19794 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:19794

Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP19794

Access Statistics for this paper

More papers in CEPR Discussion Papers from Centre for Economic Policy Research 33 Great Sutton Street, London EC1V 0DX, UK.
Bibliographic data for series maintained by CEPR ().