D ${ }^{3}$: Delayed Default-Intention Based Default Prediction in Financial Loan Service

Mengying Zhu, Mengyuan Yang, Guanjie Cheng, Guofang Ma

Abstract: Loan default prediction is a crucial component of risk management in financial loan services. In practice, significant monetary losses often stem from initially creditworthy loans that later default unexpectedly. This phenomenon arises because such loans, while assessed as low-risk at disbursement initially, have a high default-intention to arise, in a delayed manner, at an indeterminate time during the repayment period after disbursement. We term such default-intention as Delayed Defaultintention. In this paper, we present a new and pressing task, namely, Delayed Default-intention based Default prediction ($\mathrm{D}^{3}$), which is of practical significance but has been rarely studied in prior research. The core challenge of $D^{3}$ task lies in its farsighted inference of delayed default-intention, as it does not manifest immediately after disbursement. To address this, we propose a survival analysis framework for the $\mathrm{D}^{3}$ task and a novel RW-D ${ }^{3}$ method, which models the repayment willingness (RW) in a loan as a negatively correlated alternative to delayed default-intention. RW-D ${ }^{3}$ systematically initializes, dynamizes, and recovers the original RW representations based on user behavior sequences, enhancing their predictive capacity from a short-term to a long-term perspective. Additionally, RW-D ${ }^{3}$ provides a comprehensive prediction of defaults triggered by delayed default-intention by jointly considering repayment status and timing. Extensive experiments demonstrate the superiority of RW-D ${ }^{3}$ over state-of-the-art methods in both its predictive effectiveness and explainability in financial loan services.

External IDs:dblp:conf/icws/ZhuYCM25