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

A Review and Bibliography of Early Warning Models

This note is intended to share some observations regarding a non-exhaustive collection of the early warning literature from 1971 to 2011. Evolution of the interest in early warning models, methodological spectrum of studies and coverage of economic variables are briefly discussed in addition to providing a bibliography.Early warning systems, bibliometric analysis

Comparison of Islamic and Conventional Banking Financial Performance during the Covid-19 Period

The COVID-19 pandemic that is spreading in Indonesia has affected economic growth, likewise banks sector. This study aims to determine the financial performance factors that are affected by the COVID-19 pandemic, both in Islamic and conventional banking which are included in the CBGB 2 category so that banks in Indonesia can anticipate it. This study uses the Artificial Neural Network (ANN) method with 6 financial performance variables in the period of January 2020 - September 2020, namely Capital Adequacy Ratio (%), Operating Expenses / Operating Income (%), Net Operation Margin (%), Landing on Deposits. Ratio (%), Short Term Mismatch (%) which are used as the independent variable, as well as Return on Assets which is used as the dependent variable. The results showed that the COVID-19 pandemic affected financial performance factors in the form of a Funding to Deposit Ratio of 35.21%; Short Term Mismatch of 26.92% and Net Operation Margin of 26.92% in Islamic banking. Whereas in conventional banking, Operating Expenses to Operating Income was 72.87% and the Capital Adequacy Ratio was 17.31%. This result is also in line with previous research where Islamic banking is more vulnerable than conventional banking in facing financial crises

A divide-and-conquer strategy using feature relevance and expert knowledge for enhancing a data mining approach to bank telemarketing

The discovery of knowledge through data mining provides a valuable asset for addressing decision making problems. Although a list of features may characterize a problem, it is often the case that a subset of those features may influence more a certain group of events constituting a sub-problem within the original problem. We propose a divide-and-conquer strategy for data mining using both the data-based sensitivity analysis for extracting feature relevance and expert evaluation for splitting the problem of characterizing telemarketing contacts to sell bank deposits. As a result, the call direction (inbound/outbound) was considered the most suitable candidate feature. The inbound telemarketing sub-problem re-evaluation led to a large increase in targeting performance, confirming the benefits of such approach and considering the importance of telemarketing for business, in particular in bank marketing

Banking Financial Performance Before and During the Covid 19 Pandemic in Indonesia: Analysis of Comparison Between Islamic and Conventional Banking

Banking is one of the financial institutions that is very influential on the economic conditions of a country. The level of banking liquidity is a reflection of the condition of the national economy. This study examines the differences in the financial performance of conventional banking and Islamic financial performance before and during the COVID-19 pandemic in Indonesia. The variables used to measure banking financial performance are risk profile, earnings, and capital.The data used are financial reports published by Otoritas Jasa Keuangan (OJK). The analysis used is the Multivariate Analysis of Variance (MANOVA). The results of the analysis found that there was no difference in the financial performance of Islamic banking on risk profile, earning, and capital indicators before and during the COVID-19 pandemic; there is no difference in the conventional financial performance of earning indicators before and during the Covid 19 pandemic; and there is no difference in the financial performance of conventional banking earning indicators during covid 19 and Islamic banking financial performance indicators of earning before covid 19. This analysis shows that the performance of Islamic finance is still able to deal with the impact of the COVID 19 pandemic in Indonesia

Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques

Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques

We are interested in forecasting bankruptcies in a probabilistic way. Specifically, we compare the classification performance of several statistical and machine-learning techniques, namely discriminant analysis (Altman's Z-score), logistic regression, least-squares support vector machines and different instances of Gaussian processes (GP's) -that is GP's classifiers, Bayesian Fisher discriminant and Warped GP's. Our contribution to the field of computational finance is to introduce GP's as a potentially competitive probabilistic framework for bankruptcy prediction. Data from the repository of information of the US Federal Deposit Insurance Corporation is used to test the predictions.Bankruptcy prediction, Artificial intelligence, Supervised learning, Gaussian processes, Z-score.

An Intelligent System for Detecting Irregularities in Electronic Banking Transactions

Frauds have historically been the major cause of bank losses. It has led to failures of some banks in the pasts, contributing toshareholders losing their investments in the banks. Information technology is a critical component in creating value in thebanking sectors, it provides decision makers with an efficient means to store, calculate, report and predict bank frauds andsecurity failures. Information system security views this challenge as a prediction problem that attempts to detect irregulartransactions in the banking sector operations scenario. This study applies neural network techniques to the bank fraudprediction problem. Using Nigerian banks as a point of reference, we design a Neural Network-Based Model that employsmultilayered Feed Forward Artificial Neural Network on database system for collecting training data for the Artificial NeuralNetwork. The Intelligence of the system is being tested on data extracted from statements of accounts from three differentbanks in Nigeria and the results were discussed.Keywords: Artificial neural network, transactions, bank fraud, financial institutions & cyber securit