Credit scoring is a very important task for lenders to evaluate the loan applications they receive from consumers as well as for insurance companies, which use scoring systems today to evaluate new policyholders and the risks these prospective customers might present to the insurer. Credit scoring systems are used to model the potential risk of loan applications, which have the advantage of being able to handle a large volume of credit applications quickly with minimal labour, thus reducing operating costs, and they may be an effective substitute for the use of judgment among inexperienced loan officers, thus helping to control bad debt losses. This study explores the performance of credit scoring models using traditional and artificial intelligence approaches: discriminant analysis, logistic regression, neural networks and classification and regression trees. Experimental studies using real world data sets have demonstrated that the classification and regression trees and neural networks outperform the traditional credit scoring models in terms of predictive accuracy and type II errors.

As time‐varying volatility has found applications in roughly all time series modelling in economics, it largely draws attention in the areas of financial markets. This study also examines the characteristics of conditional volatility in the Baltic Stock Markets (Estonia, Latvia and Lithuania) by using a broad range of GARCH volatility models. Correctly forecasting the volatility leads to better understanding and managing financial market risk. Daily returns from four Baltic stock indexes are used; Estonia (TALSE index), Latvia (RIGSE index), Lithuania (VILSE index) and synthetic BALTIC benchmark index. We test a large family of GARCH models, including; the basic GARCH model, GARCH‐in‐mean model, asymmetric exponential GARCH and GJR GARCH, power GARCH and component GARCH model. We find strong evidence that daily returns from Baltic Stock Markets can be successfully modelled by GARCH‐type models. For all Baltic markets, we conclude that increased risk will not necessarily lead to a rise in the returns. All of the analysed indexes exhibit complex time series characteristics involving asymmetry, long tails and complex autoregression in the returns. Results from this study are firmly recommended to financial officers and international investors. Santrauka Straipsnyje analizuojamas salyginis Baltijos vertybiniu popieriu rinku (Estijos, Latvijos ir Lietuvos) nepastovumas, taikant eile GARCH kintamumo modeliu. Pažymetina, kad tinkamai prognozuojant nepastovuma, galima geriau suvokti ir valdyti finansiniu rinku rizika. Straipsnyje remiamasi keturiu Baltijos šaliu kasdienemis akciju indeksu gražomis; Estijos (TALSE indeksu), Latvijos (RIGSE indeksu), Lietuvos (VILSE indeksu) ir sintetiniu palyginamuoju BALTIC indeksu. Pritaikius eile GARCH kintamumo modeliu, galima teigti, kad didejanti rizika Baltijos šaliu rinkose nebūtinai itakos vertybiniu popieriu gražos augima. Tyrimo metu gauti rezultatai rekomenduojami finansu specialistams ir investuotojams.

This paper analyses the effects of off-balance sheet (OBS) activities and various types of risks on the cost and profit efficiencies of banks in seven East Asian countries between 2001 and 2008. Cost and profit efficiency scores are estimated using the data envelopment analysis approach. The results of this analysis are then used to identify the impact of OBS activities and risk exposures on cost and profit efficiencies using a Tobit regression. Bank insolvency risk (as measured by z-scores) is positively related to profit efficiency, while interest sensitivity, size, equity to total assets and OBS exposures all impact on cost efficiency. The analysis of the impact of input and output slacks illustrates that in around 1 in 5 cases banks’ cost efficiency can be improved by adjusting the former variables, whereas in only around 1 in 100 cases a similar outcome is possible for profit efficiency.

Purpose: The purpose of this paper is twofold. The first and the most important is to examine the efficiency of Islamic banks relative to conventional banks operating in North African Arab countries, in terms of cost and revenue efficiency. The second objective is to assess more evidence regarding the banking system efficiency trend and dynamics in each single country, and to compare such trends among countries included in the study.Design/methodology/approach: The non‐parametric data envelopment analysis (DEA) was used to estimate cost and revenue efficiency scores assuming variable returns to scale (VRS). The sample consists of nine Islamic banks and 11 conventional banks.Findings: The results indicated that Islamic banks achieved higher average revenue efficiency scores over conventional banks in this region, while the growth rate of revenue efficiency score of Islamic bank was less than conventional banks. In terms of cost efficiency, the results varied from country to another. The results also showed that both groups of banks were close to each other, with an advantage to conventional banks, which suffer less cost efficiency loss over time compared to Islamic banks.Research limitations/implications: The very limited data sources (banks' web sites) was was the main limitation faced during preparing for this research. Another limitation was the non‐regularity of annual reports.Practical implications: Islamic banks are highly challenged in finding investment opportunities/avenues that comply with Islamic regulations, unlike conventional banks that can invest in fixed income securities. There is a serious need for some countries to deregulate their banking systems more, in order to enhance the compatibility and the efficiency of their banking, such as the case of Sudan.Originality/value: Given the previously mentioned difficulties, decent data set were collected. The value of this paper is the use of nonparametric DEA to analyse cost and revenue efficiences in the countries of this region.

This paper analyzes the performance of Islamic banks operating in Pakistan according to their financial results of the year 2015. CAMELS rating model is applied in this research. This model is based on certain financial ratios which are excerpt from values in the financial statements of banks. The authors conduct the research under the umbrella of quantitative paradigm. The authors found that 2 of the Islamic banks are showing satisfactory results, while others are on fair position. There is a need to develop financial markets for treasury operations for these banks. Results help in development of growth strategy for Islamic banks in Pakistan, as well as they might be useful to create a fair snapshot for regulators to develop growth strategy for this stream of banking. Keywords: Islamic banking, performance, growth analysis, CAMELS. JEL Classification: G02, G21, G32