0

Neural networks in bank insolvency prediction

Nov 30, 2009

DOI:

Published in: International Journal of Computer Science and Network Security

he current paper aims to predict bank insolvency before the bankruptcy using neural networks, to enable all parties to take remedial action. Artificial neural networks are widely used in finance and insurance problems. Artificial neural networks are used to predict the insolvency. The back propagation network and the Kohonen self-organizing map (SOM) are used as the representative types for supervised and unsupervised artificial neural networks respectively. The results of applying the artificial neural networks methodology to predict financial distress based upon selected financial ratios show abilities of the network to learn the patterns corresponding to financial distress of the bank. In all cases, the percent correctly classified in the simulation sample by the feed-forward back propagation network is above 92 percent. After simulate the SOM network the percent correctly classified is above 94 percent. In spite of the limited data used in this study, artificial neural networks show significant signs for providing early warning signals and solvency monitoring. In addition, it is obvious from the results that SOM gives better results than feed-forward back propagation network

Other Researches

Big data: ethical issues

The internet played a heroic role in the information revolution by bringing with it a greater scope of change, not only technologically, but societally as well. Convergence of computers and communications and what they do with information has change...

Predicting energy Consumption using artificial neural networks: a case study of the UAE

Predicting energy consumption is very important for improving resource planning and for more efficient production. This study uses artificial neural network (ANN) models to predict energy consumption in the United Arab Emirates (UAE). The multilayer...

Generational Diversity and Work Values

Work values are a significant factor that impacts employees’ job satisfaction and their commitment to work. Understanding generational diversity among employees and using the right strategy to manage them are important for organizational success. Di...

Loan decision models for the Jordanian commercial banks

Credit risk remains the most critical challenge facing bank’s management as it adversely affects the profitability and stability of the bank. However, despite the rise in loan delinquency and the serious competition in the banking market, loan appli...

Big Data: Unleashing The Match Between Quality and Quantity

The quantity and complexity of data in business activities are growing exponentially. The advancement of technology, low-cost hardware and storage devices, and extensive use of the Internet and online applications pushed the projected size of the co...

Credit risk assessment model for Jordanian commercial banks: neural scoring approach

Despite the increase in the number of non-performing loans and competition in the banking market, most of the Jordanian commercial banks are reluctant to use data mining tools to support credit decisions. Artificial neural networks represent a new f...

Credit risk management for the Jordanian commercial banks: A business intelligence approach

Commercial banks in Jordan are regarded as vitally important and competitive financial organizations that seek profit by providing various financial services to various customers while managing different types of risk. Credit forms a cornerstone of ...

Using data mining for an intelligent marketing campaign

Recently, as the population of customers is becoming global, the growing competitive pressure has forced the marketing industry to use different strategies in order to create valuable customers and serve them profitably. Through scrutinizing custome...

Neuro-based artificial intelligence model for loan decisions

Problem statement: Despite the increase in consumer loans defaults and competition in the banking market, most of the Jordanian commercial banks are reluctant to use artificial intelligence software systems for supporting loan decisions. Approach: T...

Applying neural networks for loan decisions in the Jordanian commercial banking system

Artificial Neural Networks play an increasingly important role in financial applications for such tasks as pattern recognition, classification, and time series forecasting. This study develops a proposed model that identifies artificial neural netwo...