An empirical analysis on the credit scoring and the intermediary role of financing guarantee institutions of China's car loans

By the end of 2018, China's car ownership has reached 240 million, an increase of 10.51% over 2017, which leads to the increase of automobile financial services and hence the associated automobile credit risks. In order to transfer risks, financial institutions increasingly are choosing to issue auto loans through financing guarantee companies. Therefore, the industry pays more attention to the credit scoring, as it acts as the main risk control measure of auto financing guarantee companies. This leads to the study of the role the financing guarantee company plays and how effective the credit rating is as a risk control mechanism. The purpose is to investigate whether the auto financing guarantee company plays a mediating role by providing credit score. The empirical approach is as follows: a two-stage regression method is used to control or eliminate the influence of personal characteristics and other third-party credit ratings. Through which, we firstly test whether the credit score of an auto financing guarantee company contains additional information besides personal characteristics and third-party credit scores. Second, we test whether additional information of auto financing guarantee company can significantly explain the post-loan performance of whether default or non-default. The conclusions show that even after controlling the third-party credit score and personal characteristics, the credit scoring system of auto financing guarantee companies still has a significant explanation on the performance of post-loan default. In other words, it plays an intermediary role by providing credit evaluation services, which has a direct decision reference for the financial institutions that ultimately provide credit. Based on this, this study puts forward corresponding management enhancement and loan risk management suggestions.No final de 2018, a propriedade automóvel na China atingiu 240 milhões, um aumento de 10.51% sobre 2017, o que leva ao aumento dos serviços financeiros automóvel e, portanto, dos riscos de crédito automóvel associados. Para mitigar riscos, as instituições financeiras optam, cada vez mais, por conceder empréstimos automóvel através de empresas de garantia. Por conseguinte, a indústria presta mais atenção à pontuação do crédito, uma vez que esta atua como a principal medida de controlo do risco das empresas de garantia de financiamento-automóvel. Isto conduz ao estudo do papel desempenhado pela empresa de garantia de financiamento e da eficácia da sua notação de crédito como mecanismo de controlo dos riscos. Com base no sistema de notação de crédito da T’s e num total de 119.798 registos de empréstimos, este estudo examina o poder explicativo da notação de crédito das empresas de garantia de financiamento automóvel no incumprimento dos mutuários e as funções mediadoras destas empresas. Utiliza-se um método de regressão em dois estágios para controlar ou eliminar a influência de características pessoais e outros ratings, testando primeiro se a notação de crédito de uma empresa de garantia contém informações adicionais e testando, depois, se as informações adicionais da empresa de garantia podem explicar significativamente o desempenho do mutuário pós-empréstimo, As conclusões mostram que, mesmo após controlar a notação de crédito de terceiros e as características pessoais, o sistema de notação de crédito das empresas de garantia tem uma explicação significativa no desempenho do mutuário pós-empréstimo. Ou seja, ele desempenha um papel mediador, fornecendo serviços de avaliação de crédito que têm influência direta na decisão das instituições financeiras que, finalmente, fornecem crédito. Correspondentemente, esta investigação apresenta sugestões de melhoramento da gestão do risco de crédito

Transfer Learning in Dynamic Business Environments: An Application in Earnings Forecast for Public Firms

In dynamic business environments, the underlying true data pattern changes rapidly. Machine learning models built upon historical data may not be responsive to the changes. A simple solution is to re-train a machine learning model using the re-collected current data. However, current data are often scarce. Therefore, it would be optimal to adapt the machine learning model built on historical data to the current period. In this study, we propose a two-step transfer learning method for enhancing machine learning in dynamic data environments. Our insight is that, by comparing current data and historical data, we gain information on the change of data environments, which guides the training of machine learning using historical and current data sets simultaneously. In this research-in-progress, we evaluate our method and an existing state-of-art algorithm in the earnings prediction tasks. Preliminary results show the effectiveness of transfer learning in dynamic business environments

AUTOENCODER BASED GENERATOR FOR CREDIT INFORMATION RECOVERY OF RURAL BANKS

By using machine learning algorithms, banks and other lending institutions can construct intelligent risk control models for loan businesses, which helps to overcome the disadvantages of traditional evaluation methods, such as low efficiency and excessive reliance on the subjective judgment of auditors. However, in the practical evaluation process, it is inevitable to encounter data with missing credit characteristics. Therefore, filling in the missing characteristics is crucial for the training process of those machine learning algorithms, especially when applied to rural banks with little credit data. In this work, we proposed an autoencoder-based algorithm that can use the correlation between data to restore the missing data items in the features. Also, we selected several open-source datasets (German Credit Data, Give Me Some Credit on the Kaggle platform, etc.) as the training and test dataset to verify the algorithm. The comparison results show that our model outperforms the others, although the performance of the autoencoder-based feature restorer decreases significantly when the feature missing ratio exceeds 70%

Credit information quality and corporate debt maturity : theory and evidence

This paper provides new theoretical and empirical evidence suggesting that the quality of credit information may be a key element in explaining the maturity structure of corporate debt around the world. In markets with poor credit information and hence a high degree of uncertainty about borrower quality, the authors find suboptimal equilibria in which short-term contracts are preferred either as a hedge against uncertainty to limit losses in bad states (in the symmetric information case) or as a screening device to learn about borrower credit quality in the course of a repeated lending relationship (in the asymmetric information case). The results of the model are supported by the econometric analysis of panel data from both industrial and developing economies. The authors find that countries with better quality of credit information (for example, as a result of improvements in credit reporting systems or accounting standards) are characterized by a higher share of long-term debt as a proportion of total corporate debt ceteris paribus. The findings suggest that promoting institutions and policies to improve the quality of credit information is an important prerequisite for increasing access of firms to long-term finance.Banks&Banking Reform,Financial Intermediation,Economic Theory&Research,Insurance&Risk Mitigation,Financial Crisis Management&Restructuring

Essays on Strategies for Increasing Repayment Rates of Digital Microloans

Access to credit can act as a highly effective tool for poverty reduction and economic growth. The ability to borrow increases the propensity of low-income people to start and maintain businesses, educate their children and withstand financial shocks. These factors, in turn, can help them to move out of poverty and lead to more sustainable economic development. However, traditional financial institutions have inherent limitations that have impeded their ability to serve the poor. Digital lenders are able to leverage the widespread adoption of mobile phones and mobile money to extend credit quickly and conveniently to more people, especially in developing countries. However, due to a lack of credit bureaus and available financial histories of borrowers, digital lenders frequently need to amass vast amounts of data in order to screen borrowers and experiment to find the appropriate loan amount by gradually increasing credit limits based on past repayment. This can lead to high user default rates and over-indebtedness. The lack of collateral during loan applications also means that digital lenders have limited mechanisms for enforcing repayment of loans. Both of these challenges threaten to limit further adoption of digital credit. Through three experimental studies conducted with an airtime lender, I explore theoretical and empirical mechanisms for reducing default rates of digital loans. In the first study, I demonstrate that limited mobile phone data contain enough signals for creating effective credit assessment methods that minimize privacy risks to borrowers. In the second study, I find that increasing credit limits negatively impacts repayments and future borrowing, and offer recommendations for increasing credit limits while minimizing the drawbacks. In the final study, I draw on theories from psychology and consumer behavior to develop vivid repayment reminders. This study found that vivid reminders had limited effectiveness for increasing loan repayment and reducing loan duration. Taken together, these three studies propose new avenues for digital lenders to reduce default rates. The hope of this dissertation is that these proposed methods would lead to a reduction in interest rates, that would ultimately benefit the borrowers

An empirical study on credit evaluation of SMEs based on detailed loan data

Small and micro-sized Enterprises (SMEs) are an important part of Chinese economic system.The establishment of credit evaluating model of SMEs can effectively help financial intermediaries to reveal credit risk of enterprises and reduce the cost of enterprises information acquisition. Besides it can also serve as a guide to investors which also helps companies with good credit. This thesis conducts an empirical study based on loan data from a Chinese bank of loans granted to SMEs. The study aims to develop a data-driven model that can accurately predict if a given loan has an acceptable risk from the bank’s perspective, or not. Furthermore, we test different methods to deal with the problem of unbalanced class and uncredible sample. Lastly, the importance of variables is analyzed. Remaining Unpaid Principal, Floating Interest Rate, Time Until Maturity Date, Real Interest Rate, Amount of Loan all have significant effects on the final result of the prediction.The main contribution of this study is to build a credit evaluation model of small and micro enterprises, which not only helps commercial banks accurately identify the credit risk of small and micro enterprises, but also helps to overcome creditdifficulties of small and micro enterprises.As pequenas e microempresas constituem uma parte importante do sistema económico chinês. A definição de um modelo de avaliação de crédito para estas empresas pode ajudar os intermediários financeiros a revelarem o risco de crédito das empresas e a reduzirem o custo de aquisição de informação das empresas. Além disso, pode igualmente servir como guia para os investidores, auxiliando também empresas com bom crédito. Na presente tese apresenta-se um estudo empírico baseado em dados de um banco chinês relativos a empréstimos concedidos a pequenas e microempresas. O estudo visa desenvolver um modelo empírico que possa prever com precisão se um determinado empréstimo tem um risco aceitável do ponto de vista do banco, ou não. Além disso, são efetuados testes com diferentes métodos que permitem lidar com os problemas de classes de dados não balanceadas e de amostras que não refletem o problema real a modelar. Finalmente, é analisada a importância relativa das variáveis. O montante da dívida por pagar, a taxa de juro variável, o prazo até a data de vencimento, a taxa de juro real, o montante do empréstimo, todas têm efeitos significativos no resultado final da previsão. O principal contributo deste estudo é, assim, a construção de um modelo de avaliação de crédito que permite apoiar os bancos comerciais a identificarem com precisão o risco de crédito das pequenas e micro empresas e ajudar também estas empresas a superarem as suas dificuldades de crédito

Credit Scoring Refinement using Optimized Logistic Regression

A poor credit scoring model will give a poor power for predicting defaulted loan. There are many approaches for modeling the default prediction, such as classical logistic regression and Bayesian logistics regression. In this paper, we applied both classical logistic regression and AUC (Area under Curved) optimized using Nelder-Mead Algorithm for refining a credit scoring model that has already been used for several years by an International bank in Indonesia. Both classical logistics regression and AUC optimized method perform well in improving the model, but logistic regression still better in some aspects. AUC Optimized model has higher AUC than logistic regression model but has lower Kolmogorov-Smirnov Score (KS-Score