Simplifying credit scoring rules using LVQ + PSO

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.Instituto de Investigación en Informátic

Towards a Comprehensible and Accurate Credit Management Model: Application of four Computational Intelligence Methodologies

The paper presents methods for classification of applicants into different categories of credit risk using four different computational intelligence techniques. The selected methodologies involved in the rule-based categorization task are (1) feedforward neural networks trained with second order methods (2) inductive machine learning, (3) hierarchical decision trees produced by grammar-guided genetic programming and (4) fuzzy rule based systems produced by grammar-guided genetic programming. The data used are both numerical and linguistic in nature and they represent a real-world problem, that of deciding whether a loan should be granted or not, in respect to financial details of customers applying for that loan, to a specific private EU bank. We examine the proposed classification models with a sample of enterprises that applied for a loan, each of which is described by financial decision variables (ratios), and classified to one of the four predetermined classes. Attention is given to the comprehensibility and the ease of use for the acquired decision models. Results show that the application of the proposed methods can make the classification task easier and - in some cases - may minimize significantly the amount of required credit data. We consider that these methodologies may also give the chance for the extraction of a comprehensible credit management model or even the incorporation of a related decision support system in bankin

Project Monitoring and Banking Competition under Adverse Selection

We develop an analysis of ex ante monitoring of risky projects in banking. If protected from competition, banks are more concerned about not catching good risk projects when the perceived state of the economy improves, while they are more concerned about being induced to finance bad risk projects when conditions deteriorate. A monopoly bank provides the socially optimal ex ante monitoring of good risks, but is too conservative with regard to bad risks. Competition between banks is shown to undermine the incentives to avoid decision errors regarding both good and bad risk projects providing too limited monitoring effort from society's point of view. ZUSAMMENFASSUNG - (Projektsteuerung und Bankenwettbewerb bei adverser Selektion) In dem Beitrag wird ein Modell zur Auswahl von risikobehafteten Projekten durch Banken entwickelt. Wenn Banken vor Wettbewerb geschützt werden, dann legen sie unter guten wirtschaftlichen Bedingungen mehr Wert darauf sich keine Projekte mit gutem Risiko entgehen zu lassen, wohingegen sie, wenn sich die Bedingungen verschlechtern eher darauf achten keine schlechten Projekte zu finanzieren. Eine monopolistische Bank leistet die sozial-optimale Auswahl guter Risiken, aber die Selektion ist zu konservativ im Hinblick auf schlechte Risiken. Es wird gezeigt, daß Wettbewerb zwischen den Banken dazu führt, daß die Anreize fehlerhafte Entscheidung zu vermeiden sowohl im Hinblick auf Projekte mit guten als auch für Projekte mit schlechten Risiken zu gering sind. Die gesellschaftliche Wohlfahrt sinkt, weil Banken zu wenig in Projektauswahl investieren.bank monitoring; adverse selection; banking competition; banking crisis

Data mining in computational finance

Computational finance is a relatively new discipline whose birth can be traced back to early 1950s. Its major objective is to develop and study practical models focusing on techniques that apply directly to financial analyses. The large number of decisions and computationally intensive problems involved in this discipline make data mining and machine learning models an integral part to improve, automate, and expand the current processes. One of the objectives of this research is to present a state-of-the-art of the data mining and machine learning techniques applied in the core areas of computational finance. Next, detailed analysis of public and private finance datasets is performed in an attempt to find interesting facts from data and draw conclusions regarding the usefulness of features within the datasets. Credit risk evaluation is one of the crucial modern concerns in this field. Credit scoring is essentially a classification problem where models are built using the information about past applicants to categorise new applicants as ‘creditworthy’ or ‘non-creditworthy’. We appraise the performance of a few classical machine learning algorithms for the problem of credit scoring. Typically, credit scoring databases are large and characterised by redundant and irrelevant features, making the classification task more computationally-demanding. Feature selection is the process of selecting an optimal subset of relevant features. We propose an improved information-gain directed wrapper feature selection method using genetic algorithms and successfully evaluate its effectiveness against baseline and generic wrapper methods using three benchmark datasets. One of the tasks of financial analysts is to estimate a company’s worth. In the last piece of work, this study predicts the growth rate for earnings of companies using three machine learning techniques. We employed the technique of lagged features, which allowed varying amounts of recent history to be brought into the prediction task, and transformed the time series forecasting problem into a supervised learning problem. This work was applied on a private time series dataset