Deep Generative Models for Reject Inference in Credit Scoring

Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. In this research, we use deep generative models to develop two new semi-supervised Bayesian models for reject inference in credit scoring, in which we model the data generating process to be dependent on a Gaussian mixture. The goal is to improve the classification accuracy in credit scoring models by adding reject applications. Our proposed models infer the unknown creditworthiness of the rejected applications by exact enumeration of the two possible outcomes of the loan (default or non-default). The efficient stochastic gradient optimization technique used in deep generative models makes our models suitable for large data sets. Finally, the experiments in this research show that our proposed models perform better than classical and alternative machine learning models for reject inference in credit scoring

A Micro Data Approach to the Identification of Credit Crunches

This paper presents a micro data approach to the identification of credit crunches. Using a survey among German firms which regularly queries the firms’ assessment of the current willingness of banks to extend credit we estimate the probability of a restrictive credit supply policy by time taking into account the creditworthiness of borrowers. Creditworthiness is approximated by firm–specific factors, e.g. the firms’ assessment of their current business situation and their business expectations. After controlling for the banks’ refinancing costs, which are also likely to affect the supply of loans, we derive a credit crunch indicator, which measures that part of the shift in the willingness to lend that is neither explained by firm-specific factors nor by refinancing costs.credit crunch, loan supply, surveys, nonlinear binary outcome panel-data models

Learning Latent Representations of Bank Customers With The Variational Autoencoder

Learning data representations that reflect the customers' creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the credit risk assessment in retail banks. In this research, we adopt the Variational Autoencoder (VAE), which has the ability to learn latent representations that contain useful information. We show that it is possible to steer the latent representations in the latent space of the VAE using the Weight of Evidence and forming a specific grouping of the data that reflects the customers' creditworthiness. Our proposed method learns a latent representation of the data, which shows a well-defied clustering structure capturing the customers' creditworthiness. These clusters are well suited for the aforementioned banks' activities. Further, our methodology generalizes to new customers, captures high-dimensional and complex financial data, and scales to large data sets.Comment: arXiv admin note: substantial text overlap with arXiv:1806.0253

Boon or Burden? The Effect of Private Sector Debt on the Risk of Sovereign Default in Developing Countries

We explore how the share of the private sector in total external debt affects perceived creditworthiness and the likelihood of sovereign default in developing countries. While there are theoretical arguments both in favor and against a stabilizing role of private-sector borrowing, the evidence clearly supports the notion that a greater share of the private sector in total external debt is associated with a reduced likelihood of sovereign default. --International Investment,Sovereign Risk

APPLICATION OF RECURSIVE PARTITIONING TO AGRICULTURAL CREDIT SCORING

Recursive Partitioning Algorithm (RPA) is introduced as a technique for credit scoring analysis, which allows direct incorporation of misclassification costs. This study corroborates nonagricultural credit studies, which indicate that RPA outperforms logistic regression based on within-sample observations. However, validation based on more appropriate out-of-sample observations indicates that logistic regression is superior under some conditions. Incorporation of misclassification costs can influence the creditworthiness decision.finance, credit scoring, misclassification, recursive partitioning algorithm, Agricultural Finance,

A Panel Data Analysis of the Repayment Capacity of Farmers

Using a balanced panel of 264 unique Illinois farmers from 2000 to 2004, this study identifies the most pertinent factors that explain the repayment capacity of farmers. After correcting for endogeneity bias caused by farmer-specific effects, one year lagged debt-to-asset ratio and soil productivity are both found to be significantly correlated with the coverage ratio at the 5% significance level using random effects. The finding is significant because it can enhance agricultural lenders ability to assess creditworthiness, screen borrowers, manage loan loss reserves, and price loans, thereby decreasing lenders costs associated with defaulted loans and ultimately reducing the costs borne by the government and taxpayers.panel data, random effects, coverage ratio, financial efficiency, solvency, liquidity, repayment capacity, profitability, creditworthiness, Agricultural Finance,

Racial Minority Lending Trends at the Farm Service Agency

A binomial logistic framework is used to determine important linkages between the FSA's decision on each loan application and the applicants financial and demographic attributes. Using data on both rejected and accepted FSA loan applications, empirical results indicate loan approval decisions were not significantly influenced by the borrowers' racial class and that, in contrast to the credit risk assessment standards employed by commercial lenders, the collective influence of more stringent and objective credit scoring measures on FSA loan approval decisions is insignificant.Agricultural Finance,

ENHANCING THE PERFORMANCE OF RISK-RATING MODELS AT COMMUNITY BANKS

The study identifies important criteria that should be used by lenders in risk-rating of their farm customers. Comparisons of model results are made to assess how robust model results are over time. Linear and logistic regression is used to determine that the debt-to-asset ratio is a major predictor of repayment ability and the asset turnover rate and family living expenses are strong predictors of farm performance.Financial Economics,

Determinants of Spread and Creditworthiness for Emerging Market Sovereign Debt:A Panel Data Study

This study uses a panel-data framework to identify the determinants of the spread over US Treasuries of emerging market sovereign issues as well as of the creditworthiness of the issuers,where the latter is represented by the Institutional Investor's creditworthiness index. We use a sample of 16 emerging market economies, together with time series data for the period 1998 to 2002 when analysing the spread, and from 1987 to 2001 when analysing the creditworthiness. The results suggest that for both the spread and the creditworthiness, significant explanatory variables include the economic growt rate, the debt-to-GDP ratio, the reserves-to-GDP ratio, and the debt-to-exports ratio. In addition, the spread is also determined by the exports-to-GDP ratio, and the debt service to GDP,while the creditworthiness is influenced by the inflation rate and a default dummy variable.