Financial Risk Assessment via Long-term Payment Behavior Sequence Folding

TL;DR

This paper introduces LBSF, a novel method for modeling long-term user payment behaviors by folding sequences based on merchants, improving financial risk prediction accuracy in online financial services.

Contribution

The paper proposes a merchant-based sequence folding technique and multi-field encoding to better capture long-term payment behavior patterns for risk assessment.

Findings

LBSF outperforms existing models in risk prediction accuracy.

Folding sequences by merchants enhances long-term behavior modeling.

The approach effectively captures behavioral changes over time.

Abstract

Online inclusive financial services encounter significant financial risks due to their expansive user base and low default costs. By real-world practice, we reveal that utilizing longer-term user payment behaviors can enhance models' ability to forecast financial risks. However, learning long behavior sequences is non-trivial for deep sequential models. Additionally, the diverse fields of payment behaviors carry rich information, requiring thorough exploitation. These factors collectively complicate the task of long-term user behavior modeling. To tackle these challenges, we propose a Long-term Payment Behavior Sequence Folding method, referred to as LBSF. In LBSF, payment behavior sequences are folded based on merchants, using the merchant field as an intrinsic grouping criterion, which enables informative parallelism without reliance on external knowledge. Meanwhile, we maximize the utility of payment details through a multi-field behavior encoding mechanism. Subsequently, behavior aggregation at the merchant level followed by relational learning across merchants facilitates comprehensive user financial representation. We evaluate LBSF on the financial risk assessment task using a large-scale real-world dataset. The results demonstrate that folding long behavior sequences based on internal behavioral cues effectively models long-term patterns and changes, thereby generating more accurate user financial profiles for practical applications.