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1 – 10 of over 1000
Article
Publication date: 12 December 2023

Bhavya Srivastava, Shveta Singh and Sonali Jain

The present study assesses the commercial bank profit efficiency and its relationship to banking sector competition in a rapidly growing emerging economy, India from 2009 to 2019…

Abstract

Purpose

The present study assesses the commercial bank profit efficiency and its relationship to banking sector competition in a rapidly growing emerging economy, India from 2009 to 2019 using stochastic frontier analysis (SFA).

Design/methodology/approach

Lerner indices, conventional and efficiency-adjusted, quantify competition. Two SFA models are employed to calculate alternative profit efficiency (inefficiency) scores: the two-step time-decay approach proposed by Battese and Coelli (1992) and the recently developed single-step pairwise difference estimator (PDE) by Belotti and Ilardi (2018). In the first step of the BC92 framework, profit inefficiency is calculated, and in the second step, Tobit and Fractional Regression Model (FRM) are utilized to evaluate profit inefficiency correlates. PDE concurrently solves the frontier and inefficiency equations using the maximum likelihood process.

Findings

The results suggest that foreign banks are less profit efficient than domestic equivalents, supporting the “home-field advantage” hypothesis in India. Further, increasing competition drives bank managers to make riskier lending and investment choices, decreasing bank profit efficiency. However, this effect varies depending on bank ownership and size.

Originality/value

Literature on the competition bank efficiency link is conspicuously scant, with a focus on technical and cost efficiency. Less is known regarding the influence of competition on bank profit efficiency. The article is one of the first to examine commercial bank profit efficiency and its relationship to banking sector competition. Additionally, the study work represents one of the first applications of the FRM presented by Papke and Wooldridge (1996) and the PDE provided by Belotti and Ilardi (2018).

Details

Managerial Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 3 November 2022

Bharti Kumari, Jaspreet Kaur and Sanjeev Swami

A crucial contemporary policy question for financial service organizations of being resilient across the globe calls for rethinking and renovating by adopting and adapting to the…

Abstract

Purpose

A crucial contemporary policy question for financial service organizations of being resilient across the globe calls for rethinking and renovating by adopting and adapting to the technologies of artificial intelligence (AI). The purpose of this study is to propose a policy framework for adoption of AI in the finance sector by exploring the driving factors through systems approach.

Design/methodology/approach

Based on literature review and discussions with experts from both industry and academia, nine enablers were shortlisted, which were used in the questionnaire survey to determine ranks of enablers. Further, the study developed the interpretive structural model (ISM) with the help of experts.

Findings

The ISM digraph developed with the help of the experts, resulted in the enablers like anticipated profitability, contactless solutions, credit risk management and software vendor support as dependent factors and stood at the top of the ISM. On the other hand, factors like availability of the data, technical infrastructure and funds are the most driving factors, which lie on the bottom of the ISM.

Research limitations/implications

The study provides implications and policy recommendations for the practicing managers and government agencies approaching the digital transformation towards the adoption of AI in the finance ecosystem.

Originality/value

The paper uses the systems approach for the development of the ISM of the enabling factors for the adoption of AI technology. On the basis of the results, the study proposes a policy framework to accelerate the functioning of the finance ecosystem with AI technology.

Details

Journal of Science and Technology Policy Management, vol. 15 no. 2
Type: Research Article
ISSN: 2053-4620

Keywords

Article
Publication date: 22 June 2022

Gang Yao, Xiaojian Hu, Liangcheng Xu and Zhening Wu

Social media data from financial websites contain information related to enterprise credit risk. Mining valuable new features in social media data helps to improve prediction…

Abstract

Purpose

Social media data from financial websites contain information related to enterprise credit risk. Mining valuable new features in social media data helps to improve prediction performance. This paper proposes a credit risk prediction framework that integrates social media information to improve listed enterprise credit risk prediction in the supply chain.

Design/methodology/approach

The prediction framework includes four stages. First, social media information is obtained through web crawler technology. Second, text sentiment in social media information is mined through natural language processing. Third, text sentiment features are constructed. Finally, the new features are integrated with traditional features as input for models for credit risk prediction. This paper takes Chinese pharmaceutical enterprises as an example to test the prediction framework and obtain relevant management enlightenment.

Findings

The prediction framework can improve enterprise credit risk prediction performance. The prediction performance of text sentiment features in social media data is better than that of most traditional features. The time-weighted text sentiment feature has the best prediction performance in mining social media information.

Practical implications

The prediction framework is helpful for the credit decision-making of credit departments and the policy regulation of regulatory departments and is conducive to the sustainable development of enterprises.

Originality/value

The prediction framework can effectively mine social media information and obtain an excellent prediction effect of listed enterprise credit risk in the supply chain.

Open Access
Article
Publication date: 27 September 2023

Deepak Kumar, B.V. Phani, Naveen Chilamkurti, Suman Saurabh and Vanessa Ratten

The review examines the existing literature on blockchain-based small and medium enterprise (SME) finance and highlights its trend, themes, opportunities and challenges. Based on…

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Abstract

Purpose

The review examines the existing literature on blockchain-based small and medium enterprise (SME) finance and highlights its trend, themes, opportunities and challenges. Based on these factors, the authors create a framework for the existing literature on blockchain-based SME financing and lay down future research paths.

Design/methodology/approach

The review follows a systematic approach. It includes 53 articles encompassing multiple dimensions of blockchain-based SME finance, including peer-to-peer lending platforms, supply chain finance (SCF), decentralized lending protocols and tokenization of assets. The review critically evaluates these approaches' theoretical underpinnings, empirical evidence and practical implementations.

Findings

The review demonstrates that blockchain-based SME finance holds significant promise in addressing the credit gap by leveraging blockchain technology's decentralized and transparent nature. Benefits identified include reduced information asymmetry, improved access to financing, enhanced credit assessment processes and increased financial inclusion. However, the literature acknowledges several challenges and limitations, such as regulatory uncertainties, scalability issues, operational complexities and potential security risks.

Originality/value

The article contributes to the growing knowledge of blockchain-based SME finance by synthesizing and evaluating the existing literature. It also provides a framework for the existing literature in the area and future research paths. The study offers insights for researchers, policymakers and practitioners seeking to understand the potential of blockchain technology in filling the SME credit gap and fostering economic development through improved access to finance for SMEs.

Details

Journal of Trade Science, vol. 11 no. 2/3
Type: Research Article
ISSN: 2815-5793

Keywords

Article
Publication date: 3 October 2023

Jie Lu, Desheng Wu, Junran Dong and Alexandre Dolgui

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely…

Abstract

Purpose

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.

Design/methodology/approach

First, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.

Findings

Compared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.

Originality/value

The authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

Details

Industrial Management & Data Systems, vol. 123 no. 12
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 13 November 2023

Jamil Jaber, Rami S. Alkhawaldeh and Ibrahim N. Khatatbeh

This study aims to develop a novel approach for predicting default risk in bancassurance, which plays a crucial role in the relationship between interest rates in banks and…

Abstract

Purpose

This study aims to develop a novel approach for predicting default risk in bancassurance, which plays a crucial role in the relationship between interest rates in banks and premium rates in insurance companies. The proposed method aims to improve default risk predictions and assist with client segmentation in the banking system.

Design/methodology/approach

This research introduces the group method of data handling (GMDH) technique and a diversified classifier ensemble based on GMDH (dce-GMDH) for predicting default risk. The data set comprises information from 30,000 credit card clients of a large bank in Taiwan, with the output variable being a dummy variable distinguishing between default risk (0) and non-default risk (1), whereas the input variables comprise 23 distinct features characterizing each customer.

Findings

The results of this study show promising outcomes, highlighting the usefulness of the proposed technique for bancassurance and client segmentation. Remarkably, the dce-GMDH model consistently outperforms the conventional GMDH model, demonstrating its superiority in predicting default risk based on various error criteria.

Originality/value

This study presents a unique approach to predicting default risk in bancassurance by using the GMDH and dce-GMDH neural network models. The proposed method offers a valuable contribution to the field by showcasing improved accuracy and enhanced applicability within the banking sector, offering valuable insights and potential avenues for further exploration.

Details

Competitiveness Review: An International Business Journal , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1059-5422

Keywords

Book part
Publication date: 6 December 2023

Zou Yanting and Muhammad Ali

Artificial intelligence (AI) has already changed the financial industry by increasing the accessibility and inclusiveness of financial services. While acknowledging the challenges…

Abstract

Artificial intelligence (AI) has already changed the financial industry by increasing the accessibility and inclusiveness of financial services. While acknowledging the challenges posed by AI, this chapter provides insights into the positive impact of AI in promoting financial inclusion. AI has greatly enhanced credit scoring and risk assessment through the use of non-traditional data sources, enabling individuals with limited credit histories and low incomes to access loans and financial products. In addition, the implementation of AI-powered customer identification and verification systems has enhanced security measures while reducing the risk of fraudulent activity. However, the digital divide still remains a challenge to achieve wide financial inclusion. Limited access to technology and digital skills keeps some people from fully benefiting from AI-powered financial services. Access to loans through AI systems may seem convenient, but it also raises concerns about excess borrowing and the resulting unsustainable debt levels. In the age of digital finance, privacy and data security are still key issues. The chapter concludes by highlighting that more research is needed to address these challenges. By fully understanding the potential of AI, as well as its limitations, the power of technology can be harnessed to create more inclusive economic opportunities for everyone, especially those living in poorer areas.

Details

Financial Inclusion Across Asia: Bringing Opportunities for Businesses
Type: Book
ISBN: 978-1-83753-305-3

Keywords

Article
Publication date: 28 April 2023

Syed Alamdar Ali Shah, Bayu Arie Fianto, Asad Ejaz Sheikh, Raditya Sukmana, Umar Nawaz Kayani and Abdul Rahim Bin Ridzuan

The purpose of this study aims to examine the effect of fintech on pre- and post-financing credit risks faced by the Islamic banks.

Abstract

Purpose

The purpose of this study aims to examine the effect of fintech on pre- and post-financing credit risks faced by the Islamic banks.

Design/methodology/approach

This research uses primary data for fintech awareness and adoption and secondary data of various financial and economic variables from 2009 to 2021. It uses baseline regression to identify moderation of fintech controlling gross domestic products, size, return on assets and leverage. The findings are confirmed using robustness against key variable bias. It also uses a dynamic panel two-stage generalized method of moments for endogeneity.

Findings

The study finds that the fintech awareness and adoption are not the same across all Islamic countries. The Asia Pacific region is far ahead of the other two regions where Indonesia is ahead in terms of fintech awareness and adoption, and Malaysia is ahead in terms of reaping its benefits in credit risk management. Fintech affects prefinancing credit risk significantly more than postfinancing credit risk. Also, the study finds that Islamic banks suffer from the problem of “Adverse selection under Shariah compliance.”

Practical implications

This research invites regulators to introduce fintech in Islamic banks on war footing. Similar studies can be conducted on the role of other risks such as operational and market risks. Fintech will also help in improving the risk profile and stability of Islamic banks against systemic risks and financial crises.

Originality/value

This research has variety of originalities. First, it is the pioneering study that addresses the effect of fintech pre- and post-financing credit risks in Islamic banks. Second, it identifies “Adverse selection under Shariah compliance” for Islamic banks. Third, it helps identify how fintech can be useful in reducing credit risk that will help in reducing capital charge for regulatory capital.

Details

Journal of Science and Technology Policy Management, vol. 14 no. 6
Type: Research Article
ISSN: 2053-4620

Keywords

Article
Publication date: 1 March 2024

Marya Tabassum, Muhammad Mustafa Raziq and Naukhez Sarwar

Agile project teams are self-managing and self-organizing teams, and these two characteristics are pivotal attributes of emergent leadership. Emergent leadership is thus common in…

Abstract

Purpose

Agile project teams are self-managing and self-organizing teams, and these two characteristics are pivotal attributes of emergent leadership. Emergent leadership is thus common in agile teams – however, how these (informal) emergent leaders can be identified in teams remains far from understood. The purpose of this research is to uncover techniques that enable top management to identify emergent agile leaders.

Methodology/design

We approached six agile teams from four organizations. We employ social network analysis (SNA) and aggregation approaches to identify emergent agile leaders.

Design/methodology/approach

We approached six agile teams from four organizations. We employ SNA and aggregation approaches to identify emergent agile leaders.

Findings

Seven emergent leaders are identified using the SNA and aggregation approaches. The same leaders are also identified using the KeyPlayer algorithms. One emergent leader is identified from each of the five teams, for a total of five emergent leaders from the five teams. However, two emergent leaders are identified for the remaining sixth team.

Originality/value

Emergent leadership is a relatively new phenomenon where leaders emerge from within teams without having a formal leadership assigned role. A challenge remains as to how such leaders can be identified without any formal leadership status. We contribute by showing how network analysis and aggregation approaches are suitable for the identification of emergent leadership talent within teams. In addition, we help advance leadership research by describing the network behaviors of emergent leaders and offering a way forward to identify more than one emergent leader in a team. We also show some limitations of the approaches used and offer some useful insights.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 11 September 2023

Camillus Abawiera Wongnaa, Alhassan Abudu, Awal Abdul-Rahaman, Ernest Amegawovor Akey and Stephen Prah

This study examined the impact of the Input Credit Scheme (ICS) by the Integrated Water Management and Agriculture Development (IWAD) on the productivity and food security of…

Abstract

Purpose

This study examined the impact of the Input Credit Scheme (ICS) by the Integrated Water Management and Agriculture Development (IWAD) on the productivity and food security of smallholder rice farmers in Ghana.

Design/methodology/approach

Cross-sectional data from 250 rice farming households in the Mamprugu Moagduri district of the North East Region obtained from a multi-stage sampling technique were used for the study. Inverse Probability Weighted Regression Adjustment (IPWRA), Propensity Score Matching (PSM) and Kendall's coefficient of concordance were the methods of analysis employed.

Findings

Empirical results show that education, rice farming experience, dependency ratio, FBO membership, farm size and farm age were the significant factors influencing participation in the input credit scheme (ICS). Also, participants had an average rice productivity of 1,476.83 kg/ha, whereas non-participants had 1,131.81 kg/ha implying that participants increased their productivity by about 30%. In addition, the study revealed that participant households increased their household dietary diversity (HDDS) by 0.45 points amounting to about 8% diversity in their diets. High-interest rates associated with credit received, the short periods of credit repayment and the high cost of inputs provided under the scheme were the most challenging constraints associated with partaking in the ICS.

Practical implications

The available literature on agricultural interventions have predominantly emphasized input credit as a key factor for improving cropt productivity and food security of smallholders. This study provides compelling evidence that participation in ICSs can result in substantial benefits for agricultural development, as evidenced by increased productivity leading to improved food security. The significance of these findings is highlighted by the fact that, through participation in input credit schemes, smallholder rice farmers in many developing countries see substantial improvement in their capacity to access productive resources, thereby improving their productivity, while simultaneously reducing food insecurity.

Social implications

Leveraging on the improved productivity of participants in the ICS, this study advocates that such input credit schemes should scale up to more food-insecure farming communities in Ghana.

Originality/value

The study uses a doubly robust econometric approach to evaluate the impact of ICS on smallholder rice farmers' productivity and food security in Ghana, making it the first of its kind. The findings offer a solid basis for future research and provide guidance for policymakers looking to boost agricultural development in Ghana.

Details

Agricultural Finance Review, vol. 83 no. 4/5
Type: Research Article
ISSN: 0002-1466

Keywords

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