An Artificial Neural Network Approach for Credit Risk Management

The objective of the research is to analyze the ability of the artificial neural network model developed to forecast the credit risk of a panel of Italian manufacturing companies. In a theoretical point of view, this paper introduces a literature review on the application of artificial intelligence systems for credit risk management. In an empirical point of view, this research compares the architecture of the artificial neural network model developed in this research to another one, built for a research conducted in 2004 with a similar panel of companies, showing the differences between the two neural network models

Prediction of Insolvency of Hungarian Micro Enterprises

The aim of the study is to establish insolvency forecast model with the usage of different statistical methods and compare their efficiency. Besides this the relation and direction between indebtedness and financial distress is also part of the examination. With different approaches we nearly reached the same efficiency, the main focus was on the independent testing sample where we did not apply any modification on the dataset supposing realistic circumstances for predicting the probability of default. The research is focusing on small companies, since their number in the economy is considered high, but for this segment such insolvency forecasts are very rare. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Porównanie wykorzystania sieci neuronowych i analizy dyskryminacyjnej w ocenie niewypłacalności

The paper investigates the use of different structure of NN and DA in the process of establishing the possibility of default. The results of those different methods are juxtaposed and their performance is compared.W artykule opisano wykorzystanie i użyteczność różnych typów sieci neuronowych i modeli analizy dyskryminacyjnej w procesie określania potencjalnej niewypłacalności dłużnika. Następnie wyniki poszczególnych metod, uzyskane na podstawie danych finansowych przedsiębiorstw pochodzących z różnych sektorów gospodarki, zostały porównane i na tej podstawie określono przydatność badanych metod w procesie oceny ryzyka kredytowego

A Self-Learning Knowledge Based System for Credit Evaluation of Loan Application: The Case of Commercial Bank of Ethiopia

This study on prototype self-learning knowledge based system (KBS) is focused on evaluation of loan application used to overcome the challenges that resulted from lack of domain experts and poor loan evaluations. We attempted to design and develop a prototype self-learning KBS that provide advisory services for any credit customers and assists the domain experts in evaluation of customer’s requests for the loan. To develop this prototype system, knowledge was acquired using semi-structured interview from domain experts which are selected using purposive sampling technique from Commercial Bank of Ethiopia (CBE) and critique the acquired knowledge. Explicit knowledge is acquired by analyzing the secondary source of knowledge by method of document analysis. Then, the acquired knowledge is modeled using decision tree that represents concepts and procedures involved in credit evaluation and production rules are used to represent the domain knowledge. The prototype system is implemented using SWI Prolog editor tool. To determine the applicability of the prototype system in the domain area, the system has been evaluated and tested by the domain experts. Eighteen (18) test cases were selected purposively.  Test cases are equally selected from both ineligible and eligible cases. The overall total performance of the prototype system is 77.71%. The performance of the prototype system is hopeful and meets the objective of the study. The study concludes that the major credit production type that advanced to customer is import letter of credit facility, export credit facility, pre-shipment credit facility and merchandise. The   eligibility of application is focused on general and specific criteria. Credit customer is classified as business, corporate and commercial based on the score sheet they achieved. Generally, in this study, the applicability of knowledge of prototype self-learning KBS is proved as hopeful approach in banking industry for credit evaluation. Keywords: KBS, self-learning and credit (loan). DOI: 10.7176/IKM/10-5-02 Publication date:August 31st 202

Computing the Performance of FFNN for Classifying Purposes

 Classification is one of the most hourly encountered problems in real world. Neural networks have emerged as one of the tools that can handle the classification problem. Feed-Forward Neural Networks (FFNN's) have been widely applied in many different fields as a classification tool. Designing an efficient FFNN structure with the optimum number of hidden layers and minimum number of layer's neurons for a given specific application or dataset, is an open research problem and more challenging depend on the input data. The random selections of hidden layers and neurons may cause the problem of either under fitting or over fitting. Over fitting arises because the network matches the data so closely as to lose its generalization ability over the test data. In this research, the classification performance using the Mean Square Error (MSE) of Feed-Forward Neural Network (FFNN) with back-propagation algorithm with respect to the different number of hidden layers and hidden neurons is computed and analyzed to find out the optimum number of hidden layers and minimum number of layer's neurons to help the existing classification concepts by MATLAB version 13a. By this process, firstly the random data has been generated using an suitable matlab function to prepare the training data as the input and target vectors as the testing data for the classification purposes of FFNN. The generated input data is passed on to the output layer through the hidden layers which process these data. From this analysis, it is find out from the mean square error comparison graphs and regression plots that for getting the best performance form this network, it is better to use the high number of hidden layers and more neurons in the hidden layers in the network during designing its classifier but so more neurons in the hidden layers and the high number of hidden layers in the network makes it complex and takes more time to execute. So as the result it is suggested that three hidden layers and 26 hidden neurons in each hidden layers are better for designing the classifier of this network for this type of input data features

Poniendo el cerebro a trabajar: Evaluación del índice de crédito para préstamos P2P basados en el modelo de redes neuronales artificiales

Effective assessment of a borrower's various credit indexes is key for unravelling the problem of information asymmetry in the context of Peer-to-Peer Lending (P2P). Mitigating adverse selection of high default potential borrowers continues to plague P2P lending platforms. In order to understand which factors determine borrower credit status (ie. loan approval, loan repayment potential, risk of default), this study renders an Artificial Neural Network Model on one of the most popular P2P lending platforms. Our results show that the interest rate, the ratio of loan to income and the loan term are the most important indicators in reflecting the borrower’s credit status, while the frequency of inquiries, the borrowing category have a relatively low degree of importance. This study finds that the borrower’s credit index status is better explained at the lower quantiles and becomes more difficult to discern at higher quantiles. This work also finds that for longer loan terms, the borrower repayment pressure and the default rates rise with higher loan-to-income ratios and higher interest rates. Additionally, we find that higher credit rankings and higher expected returns lead to higher probabilities of defaulting. To reduce the probability of borrower default, this study recommends building lending groups or lending pools, selecting higher income credit candidates and increasing credit limits. To validate our results, we perform robustness tests that modify the learning coefficient and the training-to-validation data ratio in order to show that the empirical results of this paper are robust and effective.La evaluación efectiva de los diversos índices de crédito de un prestatario es clave para desentrañar el problema de la asimetría de la información en el contexto del préstamo entre pares (P2P). La mitigación de la selección adversa de prestatarios con alto potencial de incumplimiento continúa plagando las plataformas de préstamos P2P. Para comprender cuales son los factores que determinan el estado crediticio del prestatario (es decir, la aprobación del préstamo, el potencial de pago del préstamo, y el riesgo de incumplimiento), este estudio presenta un Modelo de Redes Neurales en una de las plataformas de préstamos P2P más populares. Nuestros resultados muestran que la tasa de interés, la relación entre el préstamo y el ingreso, y el plazo del préstamo son los indicadores más importantes para reflejar el estado crediticio del prestatario, mientras que la frecuencia de las consultas, la categoría de endeudamiento tiene un grado relativamente bajo de importancia. Este estudio encuentra que el estado del índice de crédito del prestatario se explica mejor en los cuantiles más bajos y se vuelve más difícil de discernir en cuantiles superiores. Este trabajo también concluye que para plazos de préstamo más largos, la presión de la amortización del prestatario y las tasas de incumplimiento aumentan con mayores ratios de préstamo en relación al ingreso y mayores tasas de interés. Además, encontramos que las clasificaciones de crédito más altas y los rendimientos esperados más altos conducen a mayores probabilidades de incumplimiento. Para reducir la probabilidad de impago del prestatario, este estudio recomienda construir grupos de préstamos, seleccionar candidatos de mayor ingreso y aumentar los límites de crédito. Para validar nuestros resultados, realizamos pruebas de robustez que modifican el coeficiente de aprendizaje y la relación de datos de entrenamiento a validación para mostrar que los resultados empíricos de este documento son sólidos y efectivos