Abdullah Ash-Shu’ayree Al-Khawaldeh

Machine learning techniques have been used successfully in several areas such as banking and finance. These techniques are used mainly for prediction, classification and partitioning data into different groups according to a certain common characteri

Credit scoring (CS) is an important process in both banking and finance. Lenders or creditors have to use CS to predict the probability that a borrower will default or become delinquent. CS is usually based on variables related to the applicant such as: his age, his historical payments, his behavior, etc. This paper first proposes a new method for variable selection. The proposed method (VS-VNS) is based on the variable neighborhood search meta-heuristic. VS-VNS allows us to select a set of significant variables for the data classification task. The VS-VNS is combined then with a Bayesian network (BN) to build models for CS and select counterparties. Further, six search methods are studied for BN on different sets of variables. The different techniques and combinations are evaluated on some well-known financial datasets. The numerical results are promising and show the benefits of the new proposed approach (VS-VNS) for data classification and credit scoring.

The turbulent start of the new century has brought new challenges for firms and countries. Survival and success in this period increasingly depends on competitiveness. Competitiveness is described in different ways by researchers. The focus of this paper extends earlier works on the role of credit scoring, cost of insurance and product discrimination in improving competitiveness in insurance companies to increase the demand of insurance policies. The paper strength is that the competitive advantage of insurance companies is measured from perspective of the insured. In the result show there is a significant effect of credit scoring, cost and product discrimination on competition in insurance companies. Also, there is a lack of understanding of the concept of insurance in Jordanian companies because they lack people who are specialist in insurance. OLS and ANOVA test are used in this paper.

The primary focus of this paper is to address the determinants of profitability on commercial banks in Jordanand to examine to what extent the performance of commercial banks operating in Jordan are affected by internaland external factors of companies listed on the Amman Stock Exchange for Jordanian Banks between 2007 and2012. Previous studies focused only on internal factors, namely, the banks’ specific characteristics in Jordan.This study includes not only the internal factors, but also external factors, namely, macroeconomic and financialmarket structures. The question is whether there are significant impacts that can be gained from internal andexternal factors on ROAA. The internal factors of capital adequacy, liquidity ratio, and size are found to besignificant as well as all the external factors in these models. A third multivariate model which includes bothinternal and external factors is included in this study but not in previous studies. This model is found to besignificant. As a result, this research gives deeper insights into determinants influencing the profitability of Jordaniancommercial banks within the Jordanian environment.

This study enrich the research in the area of credit rating by adding supportive power to the explanation of using factors which have an influence on the CR. Such a power can increase the awareness of the current situation of CR as a subject in one of the developing countries that has unique political and economic characteristics. The results in summary confirm that firm characteristic variables have a significant impact on CR. Profitability is positively associated with CR for all models, while leverage and loss propensity are associated negatively with CR for all (or nearly all regarding loss propensity) models. However, capital intensity is not important. Only size and growth potential (Tobin’s q) are very strongly positively associated with CR. By contrast, type of sector and audit are not related to CR.