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This research examines the relationship between bank branch expansion and ESG through the lens of geographical proximity. By utilizing panel data from Chinese listed firms during the 2010–2020 period, our results provide robust evidence that bank branch expansion promotes firms to fulfill ESG. Through increased bank competition and reduced distance between banks and enterprises, bank branch expansion contributes to ESG by strengthening financial support and optimizing informational efficiency. Additional analyses reveal that bank branch expansion enhances ESG in firms with financial backgrounds on boards, state-owned enterprises, firms with higher digital transformation and located in a superior legal environment. Overall, these findings reveal the influence of bank branch expansion on the non-financial behavior of firms and provide new empirical evidence for exploring the motivation for ESG. [ABSTRACT FROM AUTHOR]

Given the influence of climate change, there is an urgent need to discuss how commercial banks can strengthen their risk management strategies to cope with climate change. Using quarterly panel data of 16 listed commercial banks in China, this study examines the impact of climate change on banking systemic risk from both theoretical and empirical perspectives. It focuses on two path mechanisms through asset volatility and credit quality as well as the heterogeneity of effects. The study finds that (1) climate change will significantly increase the level of bank systemic risk spillovers and non-state-owned commercial banks are more affected than state-owned commercial banks; (2) asset volatility and credit quality mediate how climate change affects banks' systemic risk; and (3) the influence path of climate change on the systemic risk of state-owned commercial banks is "climate change—asset volatility – credit risk—systematic risk." However, the mediating effect of asset volatility on non-state-owned commercial banks is not significant, whereas credit quality shows a partial mediating effect. Accordingly, this study advances policy suggestions on strategic management, interventions, and the risk prevention system for banks' climate risk from the perspectives of macro-prudential regulation and micro-risk management. [ABSTRACT FROM AUTHOR]

Establishing sustainable and balanced development for green financing is critical for improving financial sustainability and banks' capability. Banks struggle to achieve economic sustainability in the current highly competitive business environment. This research examines the impact of income diversification on financial sustainability proxy by return on assets (ROA) by applying the quantile regression technique to the data from banks of ASEAN countries over the period 2008–2019. In addition, liquidity risk, bank size, interest and non-interest incomes, and market capitalization are studied as control variables. The empirical findings indicate that income diversification positively impacts return on assets at all countries' lower, middle, and upper quantiles, even though sizes can differ across countries and quantiles. Moreover, market capitalization, non-interest income, and banks' size favorably impact banks' performance. In contrast, liquidity risk and interest incomes are negatively linked to the performance of banks for all countries at each quantile. These results have significant strategic implications for managers, regulators, and policymakers who share a common interest in boosting financial sustainability and performance and significantly shaping green recovery. [ABSTRACT FROM AUTHOR]

The financial world, money markets, and the growth of an economy are all powered by banks. In India's financial sector, where cashless and paperless transactions are expanding quickly, bank fraud is also rising quickly, and fraudsters are now turning to cutting-edge techniques. Fighting financial crimes has always been a priority for the banking industry. Banks' fraud losses will diminish when they are able use analytics and emerging technologies for better fraud prevention. Data has evolved into more than simply an IT resource; it is a vital feature of the banking industry's digital transformation in the present world. The majority of contemporary risk monitoring systems, which are based on data science discipline such as big data, allow for surveillance and offer Early Warning Signals (EWS) about any kind of worsening. The study of this paper signifies that how big data as a service and application proven as a strong and innovative instrument that assists Indian banking sector not only in identifying security issues and fraudulent behavior, but also in their early prevention. The experimental results of bibliometric study conducted on Scopus and Web of Science databases using in Vos Viewer software, clearly discovered big data technology is a crucial approach to deploy in Indian banking system. Findings of this paper will contribute to knowledge and practice of researchers working in this filed by increasing their understanding related to numerous big data technologies and techniques deployment for handling banking financial risks and its role in operational banking workflow management. [ABSTRACT FROM AUTHOR]

Bank risk management is an important issue in the stability of financial system. How to select risk factors with great impact on banks has become a problem at present. Most of the previous studies have considered the disclosure frequency of the risk factors in the statements as the criterion of high-risk factors. This paper creatively constructs the two-dimensional risk matrix with frequency of risk factors and disclosure sentiment to divide these factors into three categories, high-risk category, mid-risk category and low-risk category. According to the financial statements of 95 American commercial banks from 2006 to 2020, 33 risk factors are determined, and three of them belong to high-risk category, called interest rate risk, regulatory risk and loan loss risk. These high-risk factors with more importance and more negative sentiment should be paid more attention in the future. management. [ABSTRACT FROM AUTHOR]

The study examines how mandatory disclosures impact banks' earnings management in India. The Reserve Bank of India (RBI) enforced disclosures fearing underdeclaration of non-performing assets (NPA) and attributable loan loss provision (LLP). In a way, such disclosure requirement was a "name and shame" strategy by the RBI. Our study hypothesizes disclosures to reduce information asymmetry and moral hazard - in a way reflected in the discretionary LLP. The results broadly support our hypothesis that regulatory enforcement through disclosures had the intended effect of hamstringing the banks' ability to manage earnings. Thus, mandatory disclosures positively affect discretionary LLP reduction, consequently minimizing the latitude that banks enjoy. • Regulatory enforcements requiring mandatory disclosures do have an impact on earnings management. • Disclosures reduce information asymmetry, thereby forbidding bank managers to use discretion in loan loss provision accrual. • Weaker banks do not play down their asset quality and related loan loss provision as they are under the supervisory radar. [ABSTRACT FROM AUTHOR]

In this novel investigation of creditor governance in an emerging market, this paper examines the monitoring by Indian lenders of firms' earnings management and financial reporting quality through the appointment of a bank-affiliated director to the borrower's board. Specifically, we study the effect of having a bank-affiliated director on the board (a "BDB"), prior to the implementation of India's Insolvency and Bankruptcy Code (IBC). This setting represents a business environment characterized by underdeveloped financial institutions, a weak legal system, and inefficient bankruptcy resolutions. Unlike the U.S., where BDBs play a limited monitoring role, our evidence suggests bank-affiliated directors played an active role in firm governance and lowered earnings management as well as improving financial reporting quality in India during the period of our study. Specifically, we find lower discretionary accruals among firms with BDBs in cross-sectional, pre-post, performance matched, and propensity score matched analyses – signaling higher reporting quality and less earnings management. This finding holds after controlling for endogeneity and when measures of accounting conservatism or earnings persistence are utilized as additional measures of reporting quality. Further, the dampening effect of BDBs on discretionary accruals is more pronounced for companies subject to greater information asymmetry and those prone to agency problems. In addition, firms with BDBs on the board have less volatile ROAs and lower idiosyncratic risk, consistent with BDBs encouraging borrowers to pursue less risky investments to safeguard creditors' interests. As the first examination of the monitoring influence of BDBs on financial reporting quality – and earnings management in particular – in an emerging market, our results provide unique insights for policymakers and creditors seeking to enhance governance and reporting quality of firms in evolving business environments. • We study the impact of bank-affiliated directors on boards (BDB) of Indian firms. • Pre-IBC reform, BDB firms had lower discretionary accruals in multiple analyses. • Stronger effect among firms with greater information asymmetry or agency problems. • BDB firms also had more conservative accounting and more persistent earnings. • Results suggest monitoring by BDB enhanced financial reporting quality, pre-IBC. [ABSTRACT FROM AUTHOR]

This paper presents a detailed approach for an efficient blood bank database management system. The database is the single most useful setting for caching data, and it is also an ideal tool for contriving, managing, updating, and modifying data from different angles. The benefits of a well-structured blood bank database are limitless and yield the benefits of improving efficiency and saving time. Here, our motive is centred on this area. India faces a shortage when it comes to the amount of blood donated. The gap in demand and supply in widened due to mismanagement and inefficient databases. We have modelled a well-organized database to try and reduce this gap. Alongside, we have developed an application that reminds donors when they become eligible again, gives locations of nearby blood donation camps, makes requesting blood easier for blood recipients etc. as well as promoting a healthy community. IOT is used for interlinking the application to the server as well as for inter-application communication. With the help of IOT this collection and exchange of data becomes more efficient. [ABSTRACT FROM AUTHOR]

The allocation of financial risks among project stakeholders is an important decision affecting the project success after the unexpected political and economic changes took place in Egypt and changes from 2011-2016. Some financial risks in a project are uncontrollable and impacted project stakeholders. Therefore, the research focuses on the know -how of applying modelling financial risk management from different perspectives. This paper presents FRAM is a dynamic model to identify and assess financial factors of risks with reference to project stakeholders; an owner/developer , contractor, financial agencies/ banks and project management companies before construction phase takes place using multi analysis tools. [ABSTRACT FROM AUTHOR]

We investigate whether and how banks adjust their financial positions in the wake of labor strikes. Using hand-collected data at the bank-municipality level in Brazil during 2006–2016, we find that banks significantly increase loan loss provisions, reduce loans disbursed, and hold less liquidity just before the bank labor strikes. These results are consistent with a bargaining hypothesis, suggesting that banks account for fewer current earnings, reduce expected cash flows, and reveal less cash and cash equivalents to labor unions before the anticipated strikes to counter the rent-seeking behavior of unions and strengthen their bargaining power in upcoming wage negotiations. • Banks raise loan loss provisions to account fewer current earnings before strikes. • Banks reduce loans disbursed to reduce expected cash flows before strikes. • Banks hold less liquidity to reveal less cash to labor unions before strikes. • Banks strategically do so to strengthen bargaining power in coming negotiations. [ABSTRACT FROM AUTHOR]

One of the classical methods of clustering is kNN and it is a simple and widely used algorithm. But when it comes to the samples with unstructured and fuzzy values, it is not applicable enough course it is based on the Euclidean distance, which does not have practical significant for such data. But these kinds of data are much important in credit risk management of commercial banks, therefore we must do a deal with these important variables by fuzzy division, and calculate the fuzzy distance between every two samples within the lattice degree, then generate a fuzzy division matrix to replace the Euclidean Distance in kNN algorithm. [ABSTRACT FROM AUTHOR]

This study seeks to supply empirical evidence for how board characteristics influence corporate governance compliance in the Indonesian banking industry. Corporate governance compliance level represents a company's actions to fulfill regulatory obligations that aim to protect the public from potential investment losses in the banking industry. This research was conducted by analyzing the influence of board characteristics, specifically how a board of commissioners' institutions and their instruments affect corporate governance compliance. The entire banking industry, which was listed on the Indonesia Stock Exchange from 2010 to 2015, was employed as the population for this research. Purposive sampling was used as the sampling technique, resulting in 195 observations. To test this study's hypotheses, multiple regression was applied as the data analysis method. The results revealed that the size of the board of commissioners, the proportion of independent commissioners, the experience of commissioners, and the size of the audit committee were factors that encouraged management in the banking industry to improve their firms' corporate governance compliance. This indicates that monitoring from the board acts as an effective mechanism for reducing information asymmetry. This research also proves that open innovation following regulations can increase compliance with laws. [ABSTRACT FROM AUTHOR]

The aim of this study is to examine the behaviour of central banks in managing their foreign exchange reserves. This includes measuring the effects of macroeconomic fundamentals on intervention and the effectiveness of intervention in managing exchange rates. First, the paper focuses on measuring central bank reaction functions to assess the response of central banks to exchange rate volatility in both emerging (EE) and advanced economies (AE). Then, it proposes an alternative approach for the closest obtainable approximation for official intervention. Finally, the Propensity Score Matching (PSM) technique is applied to test whether the proposed foreign exchange intervention has causal effects on exchange rates. Results show that central banks in both EE and AE respond more aggressively to an exchange rate appreciation. Data from official foreign reserves provide enough evidence for detecting policy changes and reveal central bank intervention. The intervention model is improved once we include other central bank incentives such as sterilisation, trade balance and the growth of reserves in relation to the growth of output. PSM results show that the central bank interventions are meaningful, and they provide evidence of causal inference for the behaviour of exchange rates in EE. [ABSTRACT FROM AUTHOR]

In this paper, we compare two different approaches to estimate the credit risk for small- and mid-sized businesses (SMBs), namely a classic parametric approach, by fitting an ordered probit model, and a non-parametric approach, calibrating a machine learning historical random forest (HRF) model. The models are applied to a unique and proprietary dataset comprising granular firm-level quarterly data collected from a European investment bank and an international insurance company on a sample of 464 Italian SMBs over the period 2015–2017. Results show that the HRF approach outperforms the traditional ordered probit model, highlighting how advanced estimation methodologies that use machine learning techniques can be successfully implemented to predict SMB credit risk, i.e. when facing high asymmetries of information. Moreover, by using Shapley values, we are able to assess the relevance of each variable in predicting SMB credit risk. • We estimate and compare credit rating accuracy using machine learning and traditional models. • We use a unique proprietary dataset comprising balance sheet and credit rating data for SMBs. • We find ML methods outperform traditional methods especially when historical data is not accessible. [ABSTRACT FROM AUTHOR]

• Consultants' are intermediaries bridging the gaps between actors and knowledge in risk regulation. • Consultants' contribute to regulatory compliance on the part of their clients. • Consultants' influence decision makers beyond simple compliance. • Consultants' play an important role in risk management, often without being included as a part of the sociotechnical system. Most risk management research focuses on owner/operators and regulators, and ignores other actors. Preventing major accidents in the offshore oil and gas sector is a key responsibility of the companies that own and operate the infrastructure. Regulatory oversight of their activities comes in the form of industry-specific goal-based regulation administered by a specialist regulatory agency. In this paper we focus on a third actor in the system – the consultants who provide specialist services regarding safety and risk. Our study draws on interviews with experienced consultants in the Norwegian oil and gas sector (n = 11, average experience 20+ years). Power's concept of riskwork is used to examine how consultants interact with their clients and how they see their role in relation to risk management. The analysis shows that the role of experienced consultants goes well beyond metaphor of the fox guarding the hen house. Rather, consultants contribute to regulatory compliance on the part of their clients but, further, they try to positively influence decision makers beyond simple compliance in order to promote what they believe to be the best safety decision making. The paper argues that consultants' role in the system is under examined and under theorized. Alongside earlier research, our study indicates that consultants play a big role in risk management, which should be both recognized and scrutinized. [ABSTRACT FROM AUTHOR]

This study presents the findings from a rigorous performance evaluation of a land registry strengthening project in Mongolia. The paper contributes to a small but growing body of systematic evaluations on land administration interventions—and particularly on efforts to build the national capacity of state agencies through institutional strengthening initiatives. The research highlights a methodological approach that utilizes mixed method data sources to explore outcomes in a more rigorous way and through a gendered lens. We rely on a unique set of primary quantitative data sources, including extensive land administration and bank loan data. In addition to ex- ploring outcomes associated with credit access and lending, our extensive data sources enable us to delve into transaction cost, efficiency, and volume. We complement the quantitative data with comprehensive qualitative data from interviews and focus group discussions. This study finds that the project improved Mongolia's registration process and land administration system. There are notable increases in transaction efficiency; digitization and strengthening of information systems; and improved transparency and access to land records. In contrast, there is more limited evidence about the program's effect on facilitating improvements in the accuracy of land records, land markets, access to credit, and gender equality in land rights. While the project helped to reduce some instances of corruption, there remain broad challenges to land governance reform. • Improvements in customer service due to investments in digitization and information systems. • Value of follow-up 5–7 years post-project to explore sustainability of improvements. • Limited evidence on improvements in land markets, access to credit, and women's empowerment. • Reductions in corruption, but there remain broad challenges to land governance reform. • Registration process for citizens is complicated by dual agency (property registry and land office) model. [ABSTRACT FROM AUTHOR]

This paper presents the research of approaches to numerically compute the solution to an optimal control problem with phase constraints. Such models are typical for economic modeling, but can also be found in a variety of applications. The formal description of planning the assets and liabilities by the management of a bank within internal goals and regulatory requirements may be formalized as an optimal control problem with constraints. Solving such models analytically is rarely possible, but numerical experiments give the idea of the properties of the solution and the prospective dynamics of the bank's balance sheet. Two types of methods are available to solve this problem numerically: indirect and direct methods. We present both approaches in this paper and test them on the example where the analytical solution is found explicitly. The indirect method in the form of the maximum principle, is used for the version of the model with penalty functions and for the version of the problem with one phase constraint. The corresponding system of ordinary differential equations combined with specific equations for the dual variables are solved by the shooting method. The direct method is applied to the model formulated in the discrete time. It is shown that such version of the model has the global solution if we reformulate it as the optimal control problem with mixed constraints. This paper provides the results of computation using both the direct and indirect methods and the results of numerical experiments. • A model of assets and liabilities management of a bank. • The ALM model as the optimal control problem with phase constraints. • Approaches to solve the optimal control problem with phase constraints. [ABSTRACT FROM AUTHOR]

This paper describes how Machine Learning and Robust Optimization techniques can greatly improve cash logistics operations. Specifically, we seek to optimize the logistics followed by the different branches of a given bank. Machine Learning is used to forecast cash demands for each of the branches, taking into account past demands and calendar effects. These demand predictions are forwarded to a Robust Optimization model, whose outputs are the cash transports that each branch should request. These transports guarantee that demand is fulfilled up to the desired confidence level, while also satisfying additional constraints arising in this particular domain. [ABSTRACT FROM AUTHOR]

This study examines the challenges of healthcare systems toward sustainable inventory management of blood products. In this regard, three main goals are pursued; First, promoting social equity in providing medical services to various patients and reducing the risk to lives. Second, the optimal management of medical products in a way that minimizes economic costs. Third, optimal management of biological waste due to inventory corruption and reduction of greenhouse gas emissions caused by transportation, to the least environmental pollution. To achieve these purposes, a practical demand-driven multi-objective inventory model is presented by utilizing hybrid policies in an uncertain environment. In the proposed model, the demands are in two types, including elective and non-elective. These demands are classified according to their medical urgency, substitution allowance, and product freshness. A hybrid robust fuzzy stochastic programming approach is applied to capture real-world uncertainties. The proposed model is implemented for blood platelet. The solution is obtained using a combined metaheuristic technique established on genetic algorithms and simulated annealing according to global and local search paradigms. To create a proper perspective for decision-makers, sensitivity analysis is performed. Besides, the performance of the model is proved by the realization. The results show that the performance of the proposed RFSP model is better than the Nominal model. Also, it performs well in minimizing the overall system costs and environmental degradation, besides reducing shortage and wastage. It is also effective for taking steps toward equity in health and suggests a proper strategy for dealing with emergencies. • Presenting a sustainable blood banking model with priority-differentiated demands. • Implementing health equity in demand satisfaction pragmatically. • Considering hybrid uncertainties with a robust fuzzy stochastic approach. • Suggesting appropriate strategies to encounter non-elective and emergency demands. • Proposing a combined global-local metaheuristic solution method for blood banking. [ABSTRACT FROM AUTHOR]

Indian banking is witnessing a dynamic phase due to the continuous change in polices of RBI and the government of India. In 2015, RBI issued licenses for the first time to 11 entities to start payment banksin order to improve banking penetration. Of these 11, three entities have started their payment banks and are trying to woo customers through technology driven services. Riding on technology to achieve competitive advantage, these banks will aid to make banking more convenient for the customers. The paper focuses on assessing customers’ preference in using services of payment banks over universal banks by using bivariate correlation analysis. The paper talks about the services that the customers prefer from the payment banks. It also captures how convenience/ease is helping payment banks to expand customer base. The role of educational background, occupation and age group as deciding factors for customers in choosing payment banks is studied in this paper. The paper further studies the effect of reference groups on the customer’s decision to choose a bank. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate the existence of the interrelationship between Latvian commercial banks’ operations on the economy, based on economic theory and the analysis of banks’ retained earnings, credit growth and economic growth trends. The existence of this interrelationship was tested using Granger causality and Johansen co integration tests. The analysis was based on quarterly data from 2001 to 2015. The study reviewed several indicators for banking developments to establish their relevance for GDP growth: credit to non-banks, non-bank deposits and bank retained earnings. This paper finds that the empirical link between bank retained earnings and GDP growth is more robust that between credit growth and GDP growth, although this does not mean that credit growth is not important. The relationship is bidirectional – GDP growth has a significant effect of bank retained earnings and vice versa. The implication for banks is to continue optimizing their asset and liability structure and adjust to both current unprecedented monetary accommodation and its eventual unwinding. [ABSTRACT FROM AUTHOR]

Automatic credit scoring, a crucial risk management tool for banks and financial institutes, has attracted much attention in the past few decades. As such, various approaches have been developed to accurately and efficiently estimate defaults in loan applicants and seamlessly improve and facilitate decision-making in the lending process. However, the imbalanced nature of credit scoring datasets, as well as the heterogeneous nature of features in credit scoring task pose many challenges in developing and implementing effective credit scoring models, targeting the generalization power of classification models on unseen data. To mitigate these challenges, in this paper, we propose the Bagging Supervised Autoencoder Classifier (BSAC). BSAC is a learning model which simultaneously leverages the superior power of supervised autoencoders and representation learning in classification, as well as the Bagging mechanism to handle the irregularities in feature space. Supervised autoencoder has been exploited to learn an optimal latent space from heterogeneous features and perform classification on top of the learned latent space. In particular, the Bagging mechanism has been employed in the learning process to construct various samples of original data to tackle the problem that arises from imbalanced data and irregularities of features in latent space. Extensive experiments on various real-world and benchmark datasets validate the superiority and robustness of the proposed method in predicting the outcome of loan applications. • A novel credit scoring model using representation, ensemble, and multi-task learning. • In BSAC, the learned representations are guided by the label information of samples. • BSAC outperforms state-of-art baseline models in credit scoring imbalanced data. • BSAC performs significantly better than the best base classifier in the pool. • The model shows a balanced performance in classifying positive and negative samples. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of food banks' demand from food insecurity is essential in optimizing operational costs and equitable distribution of food, especially when demand is uncertain. Hence, Gaussian Mixture Model (GMM) clustering is selected to extract patterns. The novelty is that GMM clustering is applied to identify the possible causes of food insecurity in a given region, understanding the characteristics and structure of the food assistance network in a particular region, and the clustering result is further utilized to explore the patterns of uncertain food demand behavior and its significant importance in inventory management and redistribution of surplus food thereby developing a two-stage hybrid food demand estimation model. Data obtained from a food bank network in Cleveland, Ohio, is used, and the clusters developed are studied and visualized. The results reveal that this proposed framework can make an in-depth identification of food accessibility and assistance patterns and provides better prediction accuracies of the leveraged statistical and machine learning algorithms by utilizing the GMM clustering results. Also, implementing the proposed framework for case studies based on different levels of planning led to practical results with remarkable ease and comfort intended for the respective planning team. • Demand uncertainty of a food bank is analyzed and forecasted. • We propose a two-stage hybrid demand estimation model to study the demand uncertainty. • A soft clustering method is implements and machine learning methods are compared to determine best predictive results. • Develop predictive framework that can be easily implemented by food bank management. • Bayesian Additive Regression Trees best captures the complex structure of the food demand datasets. [ABSTRACT FROM AUTHOR]

Emissions trading within the Emissions Trading Scheme of the European Union (EU ETS) strongly influences European industrial companies. The companies must choose their strategy of reduction the costs of emissions allowances as possible. The changing system's conditions and volatile prices of allowances make this decision challenging. The main aim of this study is to compare different ways of risk management: banking (i.e., buying the allowances in forward) and using derivatives: futures and options. Despite several studies devoted to the relationship between the EU ETS and companies have already been published, there is still a gap in this field. Namely, the published studies have been substantially simplified so far by ignoring the risk of driving parameters. We construct a realistic large-scale stochastic optimization model, which avoids the mentioned simplifications. We use the Markov Stochastic Dual Dynamic Programming algorithm (MSDDP) to find the optimal solution. We apply the model to the data of a real-life industrial company. We find that banking is the most costly way of risk reduction, while using derivatives is efficient in risk reduction. Surprisingly, out of the derivatives, it is always optimal to use futures and not to use options. These results are confirmed by a thorough sensitivity analysis. The preference of the futures over options is mainly due to the less price of futures in comparison to options reducing risk equivalently. • Novel dynamic model of EU emissions management with continuously distributed prices. • Using EUA futures and banking brings significant savings. • EUA options are never used by a company. • These findings are robust given parameter perturbations. [ABSTRACT FROM AUTHOR]

It is not easy to get a decent housing for low-income people (LIP) in the cities of Indonesia due to the limitation of land available for development in the urban areas allows the speculators to retain the benefits of land in an unregulated commodity business. This study aims to examine and analyze the establishment of land bank for ensuring the implementation of more prosperous, fair sharing and sustainable land management. The observational method of reviewing the land use issues, land bank models and legal basis was performed to understand the eligibility of land bank in management of land use to serve public interest of infrastructure construction and development of affordable housing for LIP in urban areas. The establishment of land bank institution in Indonesia is considered important to accommodate the need of lands in urban areas for a wide array of purposes. The analysis of landless housing prices in the Jabodetabek region for the development of five-story residential building can save more than 50% of income toward the necessity of affordable housing for LIP. The finding of this study may provide a contribution to get better understanding on the decision making process of sustainable land management at all levels of government in Indonesia particularly by the provincial government of Special Capital Territory of Jakarta. • The issues of land and housing prices in Indonesia were reviewed. • The establishment of land bank institutions was proposed to manage the lands. • Various types of land supply can be transferred to the land bank management system. • Landless housing prices in Jabodetabek were simulated to support decision making process. [ABSTRACT FROM AUTHOR]

Credit risk assessment is critical for loan approval and risk management of banks. However, the problem of missing credit risk data may greatly reduce the effectiveness of the assessment model. Therefore, constructing a data imputation method for accurate missing data prediction is quite beneficial. Typically, building an effective imputation model is very challenging due to the high missing rate and complex arbitrary missing pattern of datasets in credit risk assessment. In this paper, a novel imputation method named as Multiple Generative Adversarial Imputation Networks (MGAIN) is proposed. Specifically, we first randomly select multiple attribute subsets instead of the whole attributes such that more complete samples can be generated. Then, the missing data in each attribute are imputed by using generative adversarial imputation networks (GAIN) which fully considers the relationships among missing values by combining neural network and adversarial learning. The proposed subset selection and multiple imputation strategy not only simplify the network structure of GAIN but also reduce the demand for data. Finally, a weighted average method is presented to synthesize multiple results of each missing attribute value to further improve the accuracy. The experimental results on real-world data demonstrate that the proposed method is superior to other popular imputation methods. • A multiple imputation method based on GAN is proposed to predict missing data. • A weighted fusion method is presented to synthesize multiple imputation results. • GAN is applied to impute missing credit risk assessment data for the first time. [ABSTRACT FROM AUTHOR]

This paper analyzes recent literature in the search for trends in business intelligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in order to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelligence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining procedure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or denial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of articles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research. [ABSTRACT FROM AUTHOR]

The effectiveness of agri-environment schemes (AES) in enhancing biodiversity on farmland and creating a long-lasting change in farmers’ motivation towards a more environmental-friendly practice is still strongly debated. Applying a regional approach has been advocated widely to make AES more ecologically and socially sustainable. In the Netherlands, some AES are performed collectively by large regional groups of farmers called Environmental Cooperatives (EC). We hypothesise that these cooperatives enhance farmers’ intention to participate by facilitating the application of AES, but also by generating group pressure. In the study at hand, we used an extended version of the Theory of Planned Behaviour (TPB) to investigate which factors are associated with farmers’ intention to participate in two kinds of collective AES (ditch bank management and the protection of meadow birds). Our results demonstrate that attitude and perceived personal ability to participate in these AES are associated with the intention of farmers to participate in ditch bank management. However, for the protection of meadow birds, social pressure, self-identity and facilitation by the EC also relate to the intention of farmers. We conclude that the facilitation undertaken by ECs positively relates to farmers’ intention to participate in collective AES. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2010.06.007 Byline: Patricia Langsch Tecles (a), Benjamin M. Tabak (b)(c) Keywords: Stochastic frontier; Bayesian methods; Banking system; Emerging markets Abstract: This paper analyzes the efficiency of the Brazilian banking sector over the post-privatization period of 2000-2007. We employ a Bayesian stochastic frontier approach, which provides exact efficiency estimates and confidence intervals and thus, allows an accurate comparison across institutions and bank groups. The results suggest that large banks are the most cost and profit efficient, supporting the concentration process observed in recent years. Foreign banks have achieved a good performance through either the establishment of new affiliates and the acquisition of local banks. The remaining public banks have had improvements in cost efficiency, but are relatively profit inefficient. Finally, we observe a positive impact of capitalization on efficiency. Author Affiliation: (a) Departamento de Economia, Universidade de Brasilia, Campus Universitario - Asa Norte, 70910-900 Brasilia, DF, Brazil (b) Banco Central do Brasil, DEPEP, SBS Quadra 3, Bloco B, Ed. Sede, 9 andar, 70074-900 Brasilia, DF, Brazil (c) Graduate Programa in Economics, Campus II, SGAN 916 Modulo A, CEP 70790-160 / Brasilia, DF, Brazil Article History: Received 18 May 2009; Accepted 9 June 2010

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.eeh.2009.08.006 Byline: Juan Pedro Sanchez-Ballesta, Mercedes Bernal Llorens Keywords: Issuing banks; Disclosure; Reputation; Financial history Abstract: This paper analyses the determinants of issuing banks' disclosure in the mid-nineteenth century in Spain. Our theses are that the State was the main promoter of accountability because of the public interest in the financial sector, and that banks used disclosure to gain and maintain reputation among their stakeholders in order to facilitate the circulation of their notes. This led to a high level of compliance with regulation, except in the crisis period of 1866-68, and to an increase in voluntary disclosure in bigger banks and in those that received security deposits, which were considered at this time to be a sign of reputation. Author Affiliation: Accounting Department, Campus Espinardo 30100 Murcia, University of Murcia, Spain Article History: Received 5 February 2009

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2009.09.034 Byline: Sophocles N. Brissimis (a)(b), Manthos D. Delis (c), Efthymios G. Tsionas (d) Keywords: Technical and allocative efficiency; Translog cost function; Maximum likelihood; European banking Abstract: This paper specifies an empirical framework for estimating both technical and allocative efficiency using the translog cost-share system, thus providing a solution to the issue known in the literature as the Greene problem. The model is applied to a large panel of European banks and the results are compared with those of a model that does not include an allocative inefficiency term and also with those obtained by using a consistent Bayesian technique. Our results suggest that models which include only technical efficiency, significantly overestimate it, while parameter estimates from the model that includes both technical and allocative efficiency are very close to the ones obtained from the Bayesian approach, thus enhancing our confidence in the proposed methodology. Finally, the fact that allocative efficiency of banks in our sample improves more than technical efficiency underlines the importance of including an allocative inefficiency component in parametric efficiency models. Author Affiliation: (a) Economic Research Department, Bank of Greece, 21 E. Venizelos Ave., Athens 10250, Greece (b) Department of Economics, University of Piraeus, 80 Karaoli & Dimitriou St., Piraeus 18534, Greece (c) Department of Economics, University of Ioannina, Ioannina 45110, Greece (d) Department of Economics, Athens University of Economics and Business, 76 Patission St., Athens 10434, Greece Article History: Received 26 January 2009; Accepted 25 September 2009

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2009.04.025 Byline: Roberta B. Staub (a), Geraldo da Silva e Souza (b), Benjamin M. Tabak (a)(c) Keywords: DEA; Bank efficiency; Emerging markets; Bank consolidation; Ownership Abstract: This paper investigates cost, technical and allocative efficiencies for Brazilian banks in the recent period (2000-2007). We use Data Envelopment Analysis (DEA) to compute efficiency scores. Brazilian banks were found to have low levels of economic (cost) efficiency compared to banks in Europe and in the US. For the period with high macroeconomic volatility (2000-2002) the economic inefficiency in Brazilian banks can be attributed mainly to technical inefficiency rather than allocative inefficiency. State-owned banks are significantly more cost efficient than foreign, private domestic and private with foreign participation. There is no evidence of differences in economic efficiency due to type of activity and bank size. These results may provide some useful guidance for financial regulators and bank managers. Author Affiliation: (a) Banco Central do Brasil, Brazil (b) University of Brasilia, Brazil (c) Universidade Catolica de Brasilia, 70074-900 Brasilia, DF, Brazil Article History: Received 19 March 2008; Accepted 20 April 2009

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2009.02.030 Byline: Subhash C. Ray (a), Abhiman Das (b) Keywords: Bank; Data envelopment analysis; Efficiency; Distribution Abstract: This paper uses the nonparametric DEA methodology to estimate cost and profit efficiency of Indian banks during the post-reform period. The results show considerable variation in average levels of profit efficiency across various ownership categories of banks. In general, state owned banks are found to be more efficient than their private counter parts. Further, efficiency tends to be low among the small banks (assets up to Rs. 50 billion), indicating that at the existing scale of operations, these banks are operating far below the efficient frontier. We also examine the distribution of efficiency using nonparametric kernel density estimates. The analysis reveals a rightward-shift of the efficiency distribution over the years. A major part of this shift comes from the state owned banks. Based on the conditional distribution, the study finds strong evidence of ownership explaining the efficiency differential of banks. Additionally, bank size and product-mix are also found to be important, although to a lesser extent. Author Affiliation: (a) University of Connecticut, Storrs, CT 06269, USA (b) Reserve Bank of India, Mumbai, Maharashtra 400 051, India Article History: Received 21 March 2007; Accepted 13 February 2009 Article Note: (footnote) [star] Views expressed in this paper are the sole responsibility of the authors and do not necessarily reflect the position of the institutions with which they are affiliated. The authors are grateful to three anonymous referees for their insightful comments and suggestions on the earlier drafts, which significantly improved the exposition of the paper.

• The main research work is focused on blockchain technology for secure storage of data. • The proposed system showcased on decentralized autonomous system for banking sector. • The security for financial transactions are addressed in this research. The present security systems encounter vulnerable surveillance and protection breaches in financial domains day by day. These systems compromise the privacy of the users in such a way that their personal data are collected and controlled by the third-parties. In this regard, Bitcoin has been implemented in the financial space. It has made possible the trusted and auditable computing via decentralized network associated with a public ledger. This work proposes a decentralized personal data management system for banking sector using block chain which makes sure that the user can control their personal data in their own.It presents a Privacy-Protected Blockchain network in which all data is encoded within a controlled time period. Despite the fact that the data is available in the past, this design can successfully preserve user privacy and protect from deceivers, providing the data more stable and healthier. Blockchain has its advantages over features such as transparency, decentralization and immutability. In the proposed model, a protocol is implemented which converts the blockchain into automated access control manager. There is no need for a third party to be trusted for the implementation of this model. In the proposed system, the transactions are not limited to financial transactions like Bitcoin, it also includes data storing, sharing and querying. Hence, the use of blockchain could reduce the overly concern about securing and compartmentalizing the data. [Display omitted] [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2008.01.019 Byline: J.W.B. Bos (a), M. Koetter (b)(c), J.W. Kolari (d), C.J.M. Kool (a) Keywords: Bank production; Heterogeneity; X-Efficiency; Benchmarking; Distress Abstract: Bank efficiency estimates often serve as a proxy of managerial skill since they quantify sub-optimal production choices. But such deviations can also be due to omitted systematic differences among banks. In this study, we examine the effects of heterogeneity on bank efficiency scores. We compare different specifications of a stochastic cost and alternative profit frontier model with a baseline specification. After conducting a specification test, we discuss heterogeneity effects on efficiency levels, ranks and the tails of the efficiency distribution. We find that heterogeneity controls influence both banks' optimal costs and profits and their ability to be efficient. Differences in efficiency scores are important for more than only methodological reasons. First, different ways of accounting for heterogeneity result in estimates of foregone profits and additional costs that are significantly different from what we infer from our general specification. Second, banks are significantly re-ranked when their efficiency is estimated with a specification other than the preferred, general specification. Third, the general specification gives the most reliable estimates of the probability of distress, although differences to the other specifications are low. Author Affiliation: (a) Utrecht School of Economics, Utrecht University, Janskerkhof 12, 3512 BL, Utrecht, The Netherlands (b) University of Groningen, Faculty of Economics & Business and CIBIF, P.O. Box 800, 9700 AV Groningen, The Netherlands (c) Deutsche Bundesbank, P.O. Box 100602, 60006 Frankfurt, Germany (d) Mays Business School, Texas A&M University, 310R Wehner Building, 4218 TAMU, College Station, TX 77843-4218, USA Article History: Received 15 July 2006; Accepted 7 January 2008

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jpolmod.2008.12.005 Byline: Valentina Bosetti (a), Carlo Carraro (b), Emanuele Massetti (c) Keywords: Emission trading; Banking Author Affiliation: (a) Fondazione Eni Enrico Mattei, Corso Magenta 63, 20121 Milano, Italy (b) Fondazione Eni Enrico Mattei, University of Venice, CEPR, CESifo and CEPS, Italy (c) Fondazione Eni Enrico Mattei and Catholic University, Milan, Italy Article History: Received 23 April 2008; Revised 8 September 2008; Accepted 2 December 2008

Using discriminant analysis, we trace the US bank failures during the period from 2007 to 2010 to poor investment decisions and large exposure to systemic risk channels. Specifically, we find that the proportion of illiquid loans in their books and the exposure to the interbank funding markets are the main predictors of bank failures. There are indicators that distinguish surviving banks from their failed peers, and these indicators serve as the early warning signals that predict banking failures. This study provides regulators and bank management forecast signals of financial exigency. [ABSTRACT FROM AUTHOR]

Highlights: [•] Support vector machine (SVM) is made transparent by hybridizing it with NBTree. [•] Its effectiveness is tested on a medium scale unbalanced dataset related to bank credit card customer churn in analytical CRM. [•] Feature selection is carried out by SVM-RFE algorithm. [•] Proposed hybrid outperformed other techniques. Feature selection resulted in rules with short lengths and better comprehensibility of the system. [•] The generated rules act as an early warning expert system to the bank management. [ABSTRACT FROM AUTHOR]

Landslide failures near water can generate impulse waves that can cause fatalities and property damage. These hazards are extremely prevalent in reservoir areas, such as the 1963 Vajont landslide in Italy and the 2003 Qiangjiangping landslide in China. This study introduces the Gapa landslide which has been reactivated by the impoundment of the Jinping reservoir, Southwest China. According to the geological investigation, the movement of the landslide was based on a deep sliding surface with a displaced body of 13 million m3. However, the landslide did not receive much attention until it exhibited huge displacements and extensive cracking in the upper part of the landslide, which was caused by the rapid drawdown of the reservoir water after rising to its highest level (1880 m asl). At present, the landslide has posed significant threats both to the Wali village on the opposite river bank and the downstream dam site during the subsequent reservoir water fluctuations. Therefore, the landslide's deformation behaviour and mechanism, and its failure mode need to be analyzed, Also, the assessment of the landslide-generated impulse waves is of great necessity. The displacement history of the landslide is presented using surface and underground monitoring data pre-, in-impoundment and subsequent five times fluctuations of the reservoir water. The analysis of daily displacement velocity and inter-annual displacement interprets the comprehensive spatio-temporal displacement characteristics between 2007 and 2020. Based on the interpretation of displacement behaviours, the deformation mechanism is revealed mainly using the hydrostatic and hydrodynamic forces. Additionally, the potential impulse waves have been predicted based on the inferred failure mode of the Gapa landslide. The maximum wave height on the river and the run-up wave height on the opposite bank are estimated to be 45.5 m and 35 m using the empirical and numerical methods. The Wali village below 1915 m asl will be destroyed in a hundred seconds if the landslide collapses overall, thus, a permanent warning line at 1915 m asl is required and residents below the line need to be relocated. The assessment of potential impulse waves related to the Gapa landslide can serve the risk management of the reservoir bank and the hydropower station. We anticipate that this study will provide some important insights into the interpretation of landslide hazard risk in the Jinping Reservoir area and other parts of the world. • The large scale and fully developed Gapa landslide with great threats are introduced. • The valuable monitoring data for 14 years from underground, surface and satellite-based is presented. • The spatiotemporal displacement behaviour is interpreted and the deformation mechanism is revealed. • The impact of landslide-generated impulse waves on the opposite bank and river chancel has been evaluated based on the inferred failure mode. [ABSTRACT FROM AUTHOR]

Abstract: Although the economic literature on the optimal management of bank excess reserves is age-old and large, here we suggest a fresh, more practical approach based on queuing theory. [Copyright &y& Elsevier]

Abstract: This paper focuses on improving the queuing system of bank based on BPR. Firstly, the bottleneck problems of bank queuing is analyzed as well as the concept, classification and methodologies of BPR (business process reengineering). Secondly, the bank businesses are investigated and analyzed. Thirdly, the queuing system of certain bank is optimized based on BPR by enterprise dynamic simulation. Finally, the simulated results are discussed and the optimized result is concluded. [Copyright &y& Elsevier]

Abstract: We study the optimal joint design of prudential supervision and deposit guarantee regulations in a multi-country, integrated banking market, where policy-makers have preferences regarding profitability and stability of the banking sector. Non-coordinated policies will tend to yield too little supervision and too much deposit insurance. The paper concludes with recommendations on policy priorities in this area. [Copyright &y& Elsevier]

Abstract: This study analyses the technical efficiency of Saudi banks using a two-stage DEA-data envelopment analysis approach. In the first stage, we use a bootstrapped DEA–VRS model to identify the efficiency scores, and in the second stage, we use a bootstrapped truncated regression model to identify the covariates that explain technical efficiency. Policy implications are derived. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents data on 76 partial credit guarantee schemes across 46 developed and developing countries. Based on theory, we discuss different organizational features of credit guarantee schemes and their variation across countries. We focus on the respective role of government and private sector and different pricing and risk reduction tools and how they are correlated across countries. We find that government has an important role to play in funding and management, but less so in risk assessment and recovery. There is a surprisingly low use of risk-based pricing and limited use of risk management mechanisms. [Copyright &y& Elsevier]

Abstract: We analyze financial support for the entrepreneurial sector. State support can raise welfare by relaxing financial constraints, but it can also reduce lending standards if entrepreneurs substitute public sources of collateral for their own assets, if it encourages excessive entrepreneurial entry, or if it undermines bank monitoring incentives. We derive a “pecking order” for support schemes: support funds should be channeled first to credit guarantee schemes and then, when entrepreneurs start to substitute public for private collateral, to co-funding entrepreneurial projects. The optimal level of credit guarantee is diminishing in the costs of incentivising bank monitoring. We show in an extension that the long-term effect of public subsidies may be to impair the private sector’s initiative to uncover cost savings. [Copyright &y& Elsevier]

Abstract: With the improvement of mobile technologies and devices, banking users are able to conduct banking services at anyplace and at anytime. Recently, many banks in the world have provided mobile access to financial information. The reason to understand what factors contribute to users’ intention to use mobile banking is important issue of research. The purpose of this research is to examine and validate determinants of users’ intention to mobile banking. This research used a structural equation modeling (SEM) to test the causalities in the proposed model. The results indicated strong support for the validity of proposed model with 72.2% of the variance in behavioral intention to mobile banking. This study found that self-efficiency was the strongest antecedent of perceived ease-of-use, which directly and indirectly affected behavioral intention through perceived usefulness in mobile banking. Structural assurances are the strongest antecedent of trust, which could increase behavioral intention of mobile banking. This research verified the effect of perceived usefulness, trust and perceived ease-of-use on behavioral intention in mobile banking. The results have several implications for mobile banking managers. [Copyright &y& Elsevier]

Abstract: One of the fields most affected by the crisis is the financial sector. Nevertheless, Spain''s banks have so far held up relatively well, and this article''s examination of one of the leaders, Banco Santander, shows. The authors argue that the Bank''s current strong position is not just attributable to Spanish regulations for conservative lending practices, or to the fact that Spanish banks did not invest in U.S. subprime mortgage products, but also to their well-established Reputational Risk Management programs. The article illustrates Banco Santander''s management of reputation and proposes the bank as a paradigmatic case for the importance of dialogue and reputation in these times of turbulence. Because of this focus, the approach is largely uncritical, rather than balanced, in concentrating on how Banco Santander offers evidence of a paradigm shift in action that offers a model for effectively addressing the economic downturn. The article concludes by suggesting the key role for public relations in managing reputational risk under contemporary conditions. [Copyright &y& Elsevier]

Abstract: Financial innovation and greater information availability have increased the tradability of bank assets and have reduced banks’ dependence on individual bank managers. We show that this can have two opposing consequences for banking stability. First, the hold-up problem between bank managers and shareholders becomes less severe. Consequently, banks’ capital structure needs to be less concerned with disciplining the management. Deposits – the most effective disciplining device – can be reduced, increasing banks’ resilience to adverse return shocks. However, limiting the hold-up problem also diminishes bank managers’ rents, reducing their incentives to properly monitor and screen borrowers, with adverse implications for asset quality. Thus, the default risk of banks does not necessarily decline. We argue that this delivers a novel explanation for the origin of the recent subprime crisis. [Copyright &y& Elsevier]

Abstract: We use experimental methods to investigate what factors contribute to breakdowns in coordination among a bank’s depositors. Subjects in our experiment decide whether to leave their money deposited in a bank or withdraw it early; a bank run occurs when there are too many early withdrawals. We explore the effects of adding uncertainty about fundamental withdrawal demand and of changing the number of opportunities subjects have to withdraw. Our results show that (i) bank runs are rare when fundamental withdrawal demand is known but occur frequently when it is stochastic, and (ii) subjects are more likely to withdraw when given multiple opportunities to do so than when presented with a single decision. For the multiple-opportunity case, we evaluate individual withdrawal decisions according to a set of simple cutoff rules. We find that the cutoff rule corresponding to the payoff-dominant equilibrium of the game, which involves Bayesian updating of probabilities, explains subject behavior better than other rules. [Copyright &y& Elsevier]

Abstract: This article takes 117 branches of a certain bank in Taiwan in 2006 as the research subject and introduces data envelopment analysis (DEA) to evaluate the operating performances of business units of this bank to provide the reference for a bank’s managers in determining operation strategies. The result indicates that, in overall technical efficiency, the case bank has many inefficient branches distinctly; the average overall technical efficiency of branches is 54.8% and the average pure technical efficiency of branches is 67%, which is probably because of lower loan-to-deposit ratio, leading to excessive input waste. The average scale efficiency of the case bank during the sample period is 82%. The ratio of resource waste due to technical inefficiency is 45.2%, of which 55.03% is due to pure technical inefficiency. [Copyright &y& Elsevier]