Data Mining Practical Machine Learning Tools and Techniques

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Credit Risk Measurement: Developments over the Last 20 Years

Online peer-to-peer (P2P) lending is a new form of loans. Different from traditional banks, lenders provide loans to borrowers directly through P2P platforms. Since many P2P loans are unsecured per...

Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending

Credit scoring has been regarded as a core appraisal tool of different institutions during the last few decades and has been widely investigated in different areas, such as finance and accounting. Different scoring techniques are being used in areas of classification and prediction, where statistical techniques have conventionally been used. Both sophisticated and traditional techniques, as well as performance evaluation criteria, are investigated in the literature. The principal aim of this paper, in general, is to carry out a comprehensive review of 214 articles/books/theses that involve credit scoring applications in various areas but in particular primarily in finance and banking. This paper also aims to investigate how credit scoring has developed in importance and to identify the key determinants in the construction of a scoring model, by means of a widespread review of different statistical techniques and performance evaluation criteria. Our review of literature revealed that there is no overall best statistical technique used in building scoring models and the best technique for all circumstances does not yet exist. Also, the applications of the scoring methodologies have been widely extended to include different areas, and this subsequently can help decision makers, particularly in banking, to predict their clients' behaviour. Finally, this paper also suggests a number of directions for future research. Copyright © 2011 John Wiley & Sons, Ltd.

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H. a. abdou and j. pointon

Una RNA (Red Neuronal Artificial) es un modelo matemático inspirado en el comportamiento biológico de las neuronas y en la estructura del cerebro. Esta también puede ser vista como un sistema inteligente que lleva a cabo tareas de manera distinta a como lo hacen las computadoras actuales. Si bien estas últimas son muy rápidas en el procesamiento de la información, existen tareas muy complejas, como el reconocimiento y clasificación de patrones, que demandan demasiado tiempo y esfuerzo aún en las computadoras más potentes de la actualidad, pero que el cerebro humano es más apto para resolverlas, muchas veces sin aparente esfuerzo (considere el lector como ejemplo el reconocimiento de un rostro familiar entre una multitud de otros rostros). El cerebro puede considerarse un sistema altamente complejo. Su unidad básica, la neurona, está masivamente distribuida con conexiones entre ellas (se calcula que hay aproximadamente 10 billones de neuronas en la corteza cerebral y 60 trillones de conexiones neuronales). Si bien hay distintos tipos de neuronas biológicas, en la figura 1 se muestra un esquema simplificado de un tipo particular que es muy común. Vemos que la misma está compuesta por:

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Redes neuronales artificiales

Purpose




One of the key elements in the banking industry relies on the appropriate selection of customers. To manage credit risk, banks dedicate special efforts to classify customers according to their risk. The usual decision-making process consists of gathering personal and financial information about the borrower. Processing this information can be time-consuming, and presents some difficulties because of the heterogeneous structure of data.




Design/methodology/approach




This paper presents an alternative method that is able to generate rules that work not only on numerical attributes but also on nominal ones. The key feature of this method, called learning vector quantization and particle swarm optimization (LVQ + PSO), is the finding of a reduced set of classifying rules. This is possible because of the combination of a competitive neural network with an optimization technique.




Findings




These rules constitute a predictive model for credit risk approval. The reduced quantity of rules makes this method useful for credit officers aiming to make decisions about granting a credit. It also could act as an orientation for borrower’s self evaluation about her/his creditworthiness.




Research limitations/implications




In spite of the fact that conducted tests showed no evidence of dependence between results and the initial size of the LVQ network, it is considered desirable to repeat the measurements using an LVQ network of minimum size and a version of variable population PSO to adequately explore the solution space in the future.




Practical implications




In the past decades, there has been an increase in consumer credit. Retail banking is a growing industry. Not only has there been a boom in credit card memberships, specially in emerging economies, but also an increase in small consumption credits. For example, it is very common in emerging economies that families buy home appliances on installments. In those countries, the association of a home appliance shop with a financial institution is usual, to provide customers with quick-decision credit line facilities. The existence of such a financial instrument aids to boost sales. This association generates conflict of interests. On one hand, the home appliance shop wants to sell products to all customers. Therefore, it is in its best interest to promote a generous credit policy. On the other hand, the financial institution wants to maximize the revenue from credits, leading to a strict surveillance of loan losses. Having a fair and transparent credit-granting policy favors a good business relationship between home appliances shops and financial institutions. One way of developing such a policy is to construct objective rules to decide to grant or deny a credit application.




Social implications




Better credit decision rules generate enhanced risk sharing. In addition, it improves transparency in credit acceptance decisions, giving less room to arbitrary decisions.




Originality/value




This study develops a new method that combines a competitive neural network and an optimization technique. It was applied to a real database of a financial institution in a developing country.

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Simplifying credit scoring rules using LVQ + PSO

One of the key elements in the banking industry rely on the appropriate selection of customers. In order to manage credit risk, banks dedicate special efforts in order to classify customers according to their risk. The usual decision making process consists in gathering personal and financial information about the borrower. Processing this information can be time consuming, and presents some difficulties due to the heterogeneous structure of data. We offer in this paper an alternative method that is able to classify customers' profiles from numerical and nominal attributes. The key feature of our method, called LVQ+PSO, is the finding of a reduced set of classifying rules. This is possible, due to the combination of a competitive neural network with an optimization technique. These rules constitute a predictive model for credit risk approval. The reduced quantity of rules makes this method not only useful for credit officers aiming to make quick decisions about granting a credit, but also could act as borrower's self selection. Our method was applied to an actual database of a credit consumer financial institution in Ecuador. We obtain very satisfactory results. Future research lines are exposed.

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Simplifying credit scoring rules using LVQ+PSO

Credit risk is defined as the probability of loss due to non-compliance by the borrower with the required payments in relation to any type of debt. When financial institutions select their customers correctly, they can reduce their credit risk. To achieve this, they use various classification methodologies to sort customers based on their risk, analyzing a set of variables such as reputation, leverage, income and so forth. The extensive analysis and processing of these variables is quite time-consuming, partly because the data to be analyzed are not homogeneous. In this paper, we present an alternative method that operates on nominal and numeric attributes, which allows obtaining a predictive model that uses a reduced set of classification rules aimed at reducing credit risk. When the number of rules used decreases, credit analysts need less time to make their decisions, which will also result in better customer service. The methodology proposed here was applied to two databases of the UCI repository and two real databases of Ecuadorian banks that grant various types of credit. The results obtained have been satisfactory. Finally, our conclusions are discussed and future research lines are suggested.

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Analysis of Methods for Generating Classification Rules Applicable to Credit Risk

There is consensus that the best way for reducing insolvency situations in financial institutions is through good risk management, which involves a good client selection process. In the market, the...

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Patricia Jimbo Santana

Papers

Redes neuronales artificiales

Simplifying credit scoring rules using LVQ + PSO

Simplifying credit scoring rules using LVQ+PSO

Analysis of Methods for Generating Classification Rules Applicable to Credit Risk

Fuzzy credit risk scoring rules using FRvarPSO