612 research outputs found
Brake or Step On the Gas? Empirical Analyses of Credit Effects on Individual Consumption
Understanding the effects of credit on consumption is crucial for guiding users’ consumption behavior, designing financial marketing strategies, and identifying credit\u27s value in stimulating the economy. Whereas several studies have endeavored on this issue, most simply utilize observations of a single credit channel and/or focus on an overall effect without considering the potentially heterogeneous short-term and long-term consumption changes. This study, leveraging a quasi-experimental design with high-resolution transaction data, examines how people respond to credit in both short- and long-term periods. Results show that credit users’ consumption amount significantly expand by 51.74% after getting access to credit in the short term. However, they ultimately cut their consumption by 4.02% to cope with financial constraints in the long term. We also reveal and quantify the spillover effects of credit on consumption with savings channels. We draw on regulatory focus theory to rationalize the changes on consumers’ consumption behavior after credit activation
iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservation
Continuous-time dynamic graph modeling is a crucial task for many real-world
applications, such as financial risk management and fraud detection. Though
existing dynamic graph modeling methods have achieved satisfactory results,
they still suffer from three key limitations, hindering their scalability and
further applicability. i) Indiscriminate updating. For incoming edges, existing
methods would indiscriminately deal with them, which may lead to more time
consumption and unexpected noisy information. ii) Ineffective node-wise
long-term modeling. They heavily rely on recurrent neural networks (RNNs) as a
backbone, which has been demonstrated to be incapable of fully capturing
node-wise long-term dependencies in event sequences. iii) Neglect of
re-occurrence patterns. Dynamic graphs involve the repeated occurrence of
neighbors that indicates their importance, which is disappointedly neglected by
existing methods. In this paper, we present iLoRE, a novel dynamic graph
modeling method with instant node-wise Long-term modeling and Re-occurrence
preservation. To overcome the indiscriminate updating issue, we introduce the
Adaptive Short-term Updater module that will automatically discard the useless
or noisy edges, ensuring iLoRE's effectiveness and instant ability. We further
propose the Long-term Updater to realize more effective node-wise long-term
modeling, where we innovatively propose the Identity Attention mechanism to
empower a Transformer-based updater, bypassing the limited effectiveness of
typical RNN-dominated designs. Finally, the crucial re-occurrence patterns are
also encoded into a graph module for informative representation learning, which
will further improve the expressiveness of our method. Our experimental results
on real-world datasets demonstrate the effectiveness of our iLoRE for dynamic
graph modeling
Insights from Niche Markets: Explainable and Predictive Values of Consumption Tendency on Credit Risks
The rapid development of FinTech drives the growing popularity of digital payment transactions. This phenomenon, especially given the increasing number of offline and online transactions being recorded in a real-time manner, offers great opportunities for financial service platforms to track consumers’ consumption tendencies and dynamically monitor and evaluate their creditworthiness. In our recent research, we first theorized the value of category-level consumption tendency based on the self-regulatory theory and employed econometric methods to empirically test the relationship between category-level consumption tendency and credit behavior. Then, we proposed a Deep Hierarchical Partial Attention-based Model (DHPAM) to predict credit default risk with full employment of product category features. We provided strong experimental evidence to show that the proposed DHPAM outperforms the state-of-the-art machine learning models. This paper, based on theories, empirical analyses, and a prediction model, offers comprehensive and practical guidance on the optimal utilization of consumption information in credit risk management
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