Credit Featured Research Articles

Present Bias and Debt-Financed Durable Goods

Abstract It has become common to view credit as problematic for present-biased consumers, who are tempted to incur too much debt because of its deferred costs. But while this view is generally valid when debt is used to finance current consumption, the picture becomes much more nuanced when credit is used to fund the purchase of durable goods—such as houses, cars, and education—which is the most common use of consumer credit. When bundled with the purchase of a durable good, the deferred cost feature of credit can be a feature, not a bug. The reason is that durable goods provide deferred benefits that are also undervalued by present-biased consumers. Moreover, people often need to save in advance to finance the purchase of a durable good, and present-bias makes it difficult to save. As a result, people with present bias tend to underconsume durable goods. We show that the deferred costs of purchase debt can offset these barriers to buying durable goods and make the present-biased consumer better off both by tempting her to buy something that she should—but would not otherwise—buy and by making it easier to save up for the purchase. (JEL D90, K0, G51)

A Deep Federated Learning-Based User Credit Evaluation Model Under Financial Internet of Things Scenarios

With the rapid development of Internet of Things (IoT) technology and the digital transformation of the financial industry, the financial IoT is becoming an important trend in the future financial field. In the era of financial IoT, a large amount of data information is recorded by sensors and devices, which poses new challenges and opportunities for user credit evaluation. In this context, conventional deep learning-based user credit evaluation methods often cannot meet privacy needs. The paper combines the privacy security ability of federated learning with deep learning, and proposes a deep federated learning-based user credit evaluation model under financial IoT scenarios. First, a particle swarm optimization-based backpropagation neural network algorithm is formulated to extract user credit evaluation features, in order to obtain user behavior patterns and credit features. Then, the XGBoost algorithm is employed to output credit evaluation results. As for the training process, the thought of federated learning is integrated to distribute the model to individual participants. In such mode, the model can be updated and trained without exposing user data to the central cloud. The experiments are conducted on real-world data to assess the proposal’s performance. The results show that the proposed credit evaluation framework can achieve better accuracy, under the guarantee of user privacy.

Interest subvention for crop loans in India: Win-win or win-lose ?

Interest subsidies on crop loans are a common feature of rural credit markets in developing countries. The objective behind giving interest subsidies is two-fold – increasing access to credit and improving loan repayment rates, and is therefore regarded as a win-win for the borrower and the lender. This study focusses on a policy intervention that provides interest subsidy for crop loans availed under the Kisan Credit Card (KCC) scheme - India's flagship program to provide short-term agricultural credit for farmers. Using a unique dataset from a large public sector bank in India, we evaluate the causal impact of this intervention on both credit access and loan repayment behavior. We also capture the heterogeneous impact of the policy on new and existing borrowers. The results of econometric analysis - a combination of matching and difference-in-difference techniques - show a substantial increase (35%) in new loans, in addition to 20% decrease in the overall loan amount, indicating increased breadth and depth of access. However, contrary to standard economic theories, we observe a reduction in odds of repayment. Further, we find that the interest rate subsidy worsens the repayment rates of existing borrowers too. Our findings underscore the importance of various operational and behavioral aspects of the agricultural lending ecosystem to design win-win credit policies.

Network centrality and credit risk: A comprehensive analysis of peer-to-peer lending dynamics

This letter analyzes credit risk assessment in the Peer-to-Peer (P2P) lending domain by leveraging a comprehensive dataset from Bondora, a leading European P2P platform. Through combining traditional credit features with network topological features, namely the degree centrality, we showcase the crucial role of a borrower’s position and connectivity within the P2P network in determining loan default probabilities. Our findings are bolstered by robustness checks using shuffled centrality features, which further underscore the significance of integrating both financial and network attributes in credit risk evaluation. Our results shed new light on credit risk determinants in P2P lending and benefit investors in capturing inherent information from P2P loan networks.

SOARD Category 1 CME Credit Featured Articles, Volume 19, November 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, November 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, October 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, October 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, September 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, September 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, August 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, August 2023

A two-stage credit scoring model based on random forest: Evidence from Chinese small firms

Small firms are major contributors to most economies, often supported by government policies. However, the credit scoring of small firms is complicated and costly, making it a challenging field of research. Using loan data from 3045 small firms in China, we design a two-stage expert system for default prediction that quantifies the variables and thresholds that have a key impact. Firstly, we use SMOTE to deal with the imbalanced data and secondly, we employ random forest to build predictive credit features. Dominance analysis shows that, when making default assessments on Chinese small firms, it is important to consider not only financial factors, but also non-financial and macroeconomic factors. In particular, the net cash profit, the firm's legal disputes and the per capita disposable income of urban residents are key factors in credit scoring. Robustness tests show that our proposed methodology performs better than other machine learning models, and this result is robust with observations from other countries.

SOARD Category 1 CME Credit Featured Articles, Volume 19, July 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, July 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, June 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, June 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, May 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, May 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, April 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, April 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, March 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, March 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, February 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, February 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, January 2023

SOARD Category 1 CME Credit Featured Articles, Volume 19, January 2023

SOARD Category 1 CME Credit Featured Articles, Volume 18, December 2022

SOARD Category 1 CME Credit Featured Articles, Volume 18, December 2022

SOARD Category 1 CME Credit Featured Articles, Volume 18, November 2022

SOARD Category 1 CME Credit Featured Articles, Volume 18, November 2022

SOARD Category 1 CME Credit Featured Articles, Volume 18, October 2022

SOARD Category 1 CME Credit Featured Articles, Volume 18, October 2022

SOARD Category 1 CME Credit Featured Articles, Volume 18, September 2022

SOARD Category 1 CME Credit Featured Articles, Volume 18, September 2022