Financial crises and bank failures: a review of prediction methods

In this article we analyze financial and economic circumstances associated with the U.S. subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. We suggest that the level of cross-border holdings of long-term securities between the United States and the rest of the world may indicate a direct link between the turmoil in the securitized market originated in the United States and that in other countries. We provide a summary of empirical results obtained in several Economics and Operations Research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults; we also extensively outline the methodologies used in them. The intent of this article is to promote future empirical research for preventing financial crises.Subprime mortgage ; Financial crises

Financial crises and bank failures: a review of prediction methods

In this article we provide a summary of empirical results obtained in several economics and operations research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults, as well as outlines of the methodologies used. We analyze financial and economic circumstances associated with the US subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. The intent of the article is to promote future empirical research that might help to prevent bank failures and financial crises.financial crises; banking failures; operations research; early warning methods; leading indicators; subprime markets

Machine Learning applied to credit risk assessment: Prediction of loan defaults

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceDue to the recent financial crisis and regulatory concerns of Basel II, credit risk assessment is becoming a very important topic in the field of financial risk management. Financial institutions need to take great care when dealing with consumer loans in order to avoid losses and costs of opportunity. For this matter, credit scoring systems have been used to make informed decisions on whether or not to grant credit to clients who apply to them. Until now several credit scoring models have been proposed, from statistical models, to more complex artificial intelligence techniques. However, most of previous work is focused on employing single classifiers. Ensemble learning is a powerful machine learning paradigm which has proven to be of great value in solving a variety of problems. This study compares the performance of the industry standard, logistic regression, to four ensemble methods, i.e. AdaBoost, Gradient Boosting, Random Forest and Stacking in identifying potential loan defaults. All the models were built with a real world dataset with over one million customers from Lending Club, a financial institution based in the United States. The performance of the models was compared by using the Hold-out method as the evaluation design and accuracy, AUC, type I error and type II error as evaluation metrics. Experimental results reveal that the ensemble classifiers were able to outperform logistic regression on three key indicators, i.e. accuracy, type I error and type II error. AdaBoost performed better than the remaining classifiers considering a trade off between all the metrics evaluated. The main contribution of this thesis is an experimental addition to the literature on the preferred models for predicting potential loan defaulters

A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications

Enterprise financial risk analysis aims at predicting the enterprises' future financial risk.Due to the wide application, enterprise financial risk analysis has always been a core research issue in finance. Although there are already some valuable and impressive surveys on risk management, these surveys introduce approaches in a relatively isolated way and lack the recent advances in enterprise financial risk analysis. Due to the rapid expansion of the enterprise financial risk analysis, especially from the computer science and big data perspective, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing enterprise financial risk researches, as well as to summarize and interpret the mechanisms and the strategies of enterprise financial risk analysis in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. This paper provides a systematic literature review of over 300 articles published on enterprise risk analysis modelling over a 50-year period, 1968 to 2022. We first introduce the formal definition of enterprise risk as well as the related concepts. Then, we categorized the representative works in terms of risk type and summarized the three aspects of risk analysis. Finally, we compared the analysis methods used to model the enterprise financial risk. Our goal is to clarify current cutting-edge research and its possible future directions to model enterprise risk, aiming to fully understand the mechanisms of enterprise risk communication and influence and its application on corporate governance, financial institution and government regulation

Predicting Credit Default among Micro Borrowers in Ghana

Microfinance institutions play a major role in economic development in many developing countries. However many of these microfinance institutions are faced with the problem of default because of the non-formal nature of the business and individuals they lend money to. This study seeks to find the determinants of credit default in microfinance institutions. With data on 2631 successful loan applicants from a microfinance institution with braches all over the country we proposed a Binary logistic regression model to predict the probability of default. We found the following variables significant in determining default: Age, Gender, Marital Status, Income Level, Residential Status, Number of Dependents, Loan Amount, and Tenure. We also found default to be more among the younger generation and in males. We however found Loan Purpose not to be significant in determining credit default. Microfinance institutions could use this model to screen prospective loan applicants in order to reduce the level of default. Keywords: Microfinance, Loan Default, Default Prediction, Logistic Regressio

Basel II compliant credit risk modelling: model development for imbalanced credit scoring data sets, loss given default (LGD) and exposure at default (EAD)

The purpose of this thesis is to determine and to better inform industry practitioners to the most appropriate classification and regression techniques for modelling the three key credit risk components of the Basel II minimum capital requirement; probability of default (PD), loss given default (LGD), and exposure at default (EAD). The Basel II accord regulates risk and capital management requirements to ensure that a bank holds enough capital proportional to the exposed risk of its lending practices. Under the advanced internal ratings based (IRB) approach Basel II allows banks to develop their own empirical models based on historical data for each of PD, LGD and EAD.In this thesis, first the issue of imbalanced credit scoring data sets, a special case of PD modelling where the number of defaulting observations in a data set is much lower than the number of observations that do not default, is identified, and the suitability of various classification techniques are analysed and presented. As well as using traditional classification techniques this thesis also explores the suitability of gradient boosting, least square support vector machines and random forests as a form of classification. The second part of this thesis focuses on the prediction of LGD, which measures the economic loss, expressed as a percentage of the exposure, in case of default. In this thesis, various state-of-the-art regression techniques to model LGD are considered. In the final part of this thesis we investigate models for predicting the exposure at default (EAD). For off-balance-sheet items (for example credit cards) to calculate the EAD one requires the committed but unused loan amount times a credit conversion factor (CCF). Ordinary least squares (OLS), logistic and cumulative logistic regression models are analysed, as well as an OLS with Beta transformation model, with the main aim of finding the most robust and comprehensible model for the prediction of the CCF. Also a direct estimation of EAD, using an OLS model, will be analysed. All the models built and presented in this thesis have been applied to real-life data sets from major global banking institutions

Internal credit risk models and digital transformation : what to prepare for? An application to Poland

PURPOSE: The digitization of credit risk through machine learning technology is becoming more attractive, especially nowadays. The article aims to analyze the performance of models estimated with Machine Learning (ML) algorithms in predicting the risk of default compared with standard statistical models such as logistic regression (benchmark model).DESIGN/METHODOLOGY/APPROACH: The indicated models were estimated using an original dataset, including financial information and the credit history of non-financial Polish enterprises. The dataset is also enlarged 20-fold to obtain a set of the so-called Big Data that could also be accepted. The out-of-sample performance comparing one-year-ahead PD estimates and observed default data for the 2015-2020 period was verified about the models under consideration. The period above also includes that associated with the COVID-19 pandemic.FINDINGS: Based on the results obtained, practical information was supplied to credit-risk researchers. Where only a limited data set is available, and where this is confined to financial indicators only, models based on ML are seen to offer a significant increase in discriminant power and precision as compared with statistical models, this being especially the case with an artificially generated set of so-called Big Data.PRACTICAL IMPLICATIONS: Models estimated with ML algorithms can benchmark the probability of default obtained using more apparent statistical models. In practice, this is useful when estimates under the two types of model prove notably different. Application is handy with, for example, more significant or higher-risk borrowers.ORIGINALITY/VALUE: The article seeks to ascertain how the market expansion of a bank's product and digital divisions might be supported without the speed and quality of credit-risk assessment is limited. The inclusion here of the COVID-19 (exogenous economic shock) period ensures the particular usefulness of recommendations for credit-risk analysts.peer-reviewe

Comparative analysis of the frequentist and Bayesian approaches to stress testing

Stress testing is necessary for banks as it is required by the Basel Accords for loss predictions and regulatory and economic capital computations. It has become increasingly important especially after the 2008 global financial crisis. Credit models are essential in controlling credit risk. The search for new ways to more accurately predict credit risk continues. This thesis concentrates on stress testing the probability of default using the Bayesian posterior distribution to incorporate estimation uncertainty and parameter instability. It also explores modelling the probability of default using Bayesian informative priors to enhance the model predictive accuracy. A new Bayesian informative prior selection method is proposed to include additional information to credit risk modelling and improve model performances. We employ cross-sectional logistic regressions to model the probability of default of mortgage loans using both the Bayesian approach with various priors and the frequentist approach. In the Bayesian informative prior selection method that we propose, we treat coefficients in the PD model as time series variables. We build ARIMA models to forecast the coefficient values in future time periods and use these ARIMA forecasts as Bayesian informative priors. We find that the Bayesian models using this prior selection method outperform both frequentist models and Bayesian models with other priors in terms of model predictive accuracy. We propose a new stress testing method to model both macroeconomic stress and coefficient uncertainty. Based on U.S. mortgage loan data, we model the probability of default at the account level using discrete time hazard analysis. We employ both the frequentist and Bayesian methods in parameter estimation and default rate (DR) stress testing. By applying the parameter posterior distribution obtained in the Bayesian approach to simulating the Bayesian estimated DR distribution, we reduce the estimation risk coming from employing point estimates in stress testing. We find that the 99% value at risk (VaR) using the Bayesian posterior distribution approach is around 6.5 times the VaR at the same probability level using the frequentist approach with parameter mean estimates. We furthersimulate DR distributions based on models built on crisis and tranquil time periods to explore the impact changes in model parameters between different scenarios have on stress testing results. We apply the parameter posterior distribution obtained in a Bayesian approach to stress testing to reduce the estimation risk that results from using parameter point estimates. We compute the VaRs and required capital with both parameter instability between scenarios and with estimation risk considered. The results are compared with those obtained when coefficient changes in stress testing models or coefficient uncertainty are neglected. We find that the required capital is considerably underestimated when neither parameter instability nor estimation risk is addressed