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The recent financial crises in the world have brought attention to the need for a new international financial architecture which rests on crisis prevention, crisis prediction and crisis management. It is therefore both desirable and vital to explore new predictive techniques for providing early warnings to regulatory agencies. The purpose of this study is to propose a new technique to prevent future crises, with reference to the last banking crises in Turkey.

ANN is utilized as an inductive algorithm in discovering predictive knowledge structures in financial data and used to explain previous bank failures in the Turkish banking sector as a special case of EFMs (emerging financial markets).

The empirical results indicate that ANN is proved to differentiate patterns or trends in financial data. Most of the bank failures could be predicted long before, with the utilization of an ANN classification approach, but more importantly it could be proposed to detect early warning signals of potential failures, as in the case of the Turkish banking sector.

The regulatory agencies could use ANN as an alternative method to predict and prevent future systemic banking crises in order to minimize the cost to the economy.

This paper reveals that the ANN approach can be proposed as a promising method of evaluating financial conditions in terms of predictive accuracy, adaptability and robustness, and as an alternative early warning method that can be used along with the most common alternatives such as CAMEL, financial ratio and peer group analysis, comprehensive bank risk assessment, and econometric models.

An efficient asset‐liability management requires maximizing banks' profit as well as controlling and lowering various risks. This multi‐objective decision problem aims to reach goals such as maximization of liquidity, revenue, capital adequacy, and market share subject to financial, legal requirements and institutional policies. This paper models asset and liability management (ALM) in order to show how different managerial strategies affect the financial wellbeing of banks during crisis.

A goal programming model is developed and applied to two medium‐scale Turkish commercial banks with distinct risk‐taking behavior. This article brings new evidence on the performance of emerging market banks with different managerial philosophies by comparing asset‐liability management in crisis.

The study has shown how shifts in market perceptions can create trouble during crisis, even if objective conditions have not changed.

The proposed model can provide optimal forecasts of asset‐liability components and banks' financial standing for different risk‐taking strategies under various economic scenarios. This may facilitate the preparation of contingency plans and create a competitive advantage for bank decision makers.

Purpose – This study investigates the management of asset structures in Malaysia’s commercial banking sector.

Methodology/approach – The study uses unbalanced panel data from 17 Islamic banks in Malaysia, covering the period 1997 to 2012. All significant data have been taken into account in analysing whether there is a relationship between asset structure management and certain factors involving bank-specific financial conditions and macroeconomic features. These include financing, deposits, profits, money supply, gross domestic product (GDP) and composite indices.

Findings – The results reveal that asset management structure is significant for total financing, total deposits, money supply, GDP and composite indices. In conclusion, the management of asset structures acts to efficiently prevent any unexpected crises that may affect banking operations.

Originality/value – The structure of asset management in Islamic banking is influenced by internal and external factors which have the most impact on asset management by Islamic banks. Islamic banking also provides more financing to reduce their risk and at the same time they attempt to increase deposits and investment in due to interest rate volatility in conventional banking.

This paper aims to investigate the efficiency and effectiveness of alternative credit‐scoring models for consumer loans in the banking sector. In particular, the focus is upon the financial risks associated with both the efficiency of alternative models in terms of correct classification rates, and their effectiveness in terms of misclassification costs (MCs).

A data set of 630 loan applicants was provided by an Egyptian private bank. A two‐thirds training sample was selected for building the proposed models, leaving a one‐third testing sample to evaluate the predictive ability of the models. In this paper, an investigation is conducted into both neural nets (NNs), such as probabilistic and multi‐layer feed‐forward neural nets, and conventional techniques, such as the weight of evidence measure, discriminant analysis and logistic regression.

The results revealed that a best net search, which selected a multi‐layer feed‐forward net with five nodes, generated both the most efficient classification rate and the most effective MC. In general, NNs gave better average correct classification rates and lower MCs than traditional techniques.

By reducing the financial risks associated with loan defaults, banks can achieve a more effective management of such a crucial component of their operations, namely, the provision of consumer loans.

The use of NNs and conventional techniques in evaluating consumer loans within the Egyptian private banking sector utilizes rigorous techniques in an environment which merits investigation.

The purpose of this paper is to transfer the concept of market‐consistent embedded value (MCEV) from life to non‐life insurance. This is an important undertaking since differences in management techniques between life and non‐life insurance make management at the group level very difficult. The purpose of this paper is to offer a solution to this problem.

After explaining MCEV, the authors derive differences between life and non‐life insurance and develop a MCEV model for non‐life business. The model framework is applied to a German non‐life insurance company to illustrate its usefulness in different applications.

The authors show an MCEV calculation based on empirical data and set up an economic balance sheet. The value implications of varying loss ratios, cancellation rates, and costs within a sensitivity analysis are analyzed. The usefulness of the model is illustrated within a value‐added analysis. The authors also embed the MCEV concept in a simplified model for an insurance group, to derive group MCEV and outline differences between local GAAP, IFRS and MCEV.

The analysis provides new and relevant information to the stakeholders of an insurance company. The model provides information comparable to that provided by embedded value models currently used in the life insurance industry and fills a gap in the literature. The authors reveal significant valuation difference between MCEV and IFRS and argue that there is a need for a consistent MCEV approach at the insurance‐group level.

The paper presents a new valuation technique for non‐life insurance that is easy to use, simple to interpret, and directly comparable to life insurance. Despite the growing policy interest in embedded value, not much academic attention has been given to this methodology. The authors hope that this work will encourage further discussion on this topic in academia and practice.

Drawing on the related insights from the upper echelon perspective, modern cognitive theory and path dependence theory, this paper aims to first integrate top management teams (TMTs) overseas experiences, research and development (R&D) strategic decision-making and innovation performance into a uniform theoretical framework and try to understand TMTs’ overseas experiences accounting for both the direct and indirect mechanisms of the variables involved within the transition economy of China.

The paper adopts research sample from the listed companies on the Growth Enterprises Market Board (GEMB) of Shenzhen Stock Exchange of China due to their stronger innovation consciousness. The research data are mainly from the WIND database of China, as the data issued in this database must be checked and approved by China’s legal institutions including China Securities Regulatory Commission and its authorized agencies. The samples cover different types of ownership and the vast majority of industries of China, which makes the objects a wide range of coverage and representativeness. In addition, according to suggestions of Podsakoff et al. (2003), the authors design the controlling measures from two aspects of data collection and statistical analysis to reduce the homologous error as much as possible.

Empirical results show that innovation performance is positively affected by the centrality overseas functional experience and industrial experience but negatively affected by the heterogeneity of overseas functional experience of TMT. Meanwhile, R&D intensity and modes play partially mediating effect in the relationship between TMTs’ overseas functional experience centrality and industrial experience heterogeneity and innovation performance, but for the relationship between overseas functional experience heterogeneity and innovation performance, R&D intensity leads to fully mediating effect.

This study contributes toward filling the gaps by elucidating the effect of TMTs’ overseas experiences on the innovation performance, identifying the mediating role of R&D strategic decision-makings in this relationship and empirically examining the acting mechanisms and paths of the variables involved in the Chinese context. In addition, practitioners could use these findings to improve their selection and training processes regarding both the top management members and the designing of the R&D strategies.

This study aims to examine and compare the credit risk management (CRM) scenario of Islamic banks (IBs) and conventional banks (CBs) in Pakistan, keeping in view the phenomenal growth of Islamic banking and its future implications.

A sample of five CBs and four IBs was chosen out of the whole banking industry for the study. Secondary data obtained from the banks’ annual financial reports for 13 years, starting from 2004 to 2016, were analyzed. Multiple regression, correlation and descriptive analysis were used in the examination of the data.

The results show that loan quality (LQ) has a positive and significant impact on CRM for both IBs and CBs. Asset quality (AQ), on the other hand, has a negative impact on CRM in the case of IBs, but has a significantly positive relation with CRM in the case of CBs. The impact of 16 ratios measuring LQ and AQ have also been individually checked on CRM, by making use of a regression model using a dummy variable of financial crises for robust comparison among CBs and IBs. The model proved significant, and CRM performance of IBs was observed to be better than that of CBs. Moreover, the mean average value of financial ratios used as a measuring tool for these variables shows that the CRM performance of IBs operating in Pakistan was better than that of CBs over the period of the study.

The research findings are expected to facilitate bankers, investors, academics and policy makers to build a better understanding of CRM practices as adopted by CBs and IBs. The findings would be useful in formulating policy measures for the progress of the banking industry in Pakistan.

This research is unique in terms of its approach toward analyzing and comparing CRM performance of CBs and IBs. Such work has not been carried out before in the Pakistani banking industry.