Financial Institutions Research Articles

Mechanisms of international economic cooperation to support the global energy transition: ways to improve

The global energy transition is a complex process and requires deep international economic cooperation and joint efforts to achieve sustainable development. Effective interaction between countries, financial institutions and the private sector is essential to ensure sufficient investment in renewable energy and technological innovation. International financial mechanisms, such as green bonds, can significantly contribute to accelerating this process, but they must ensure access to resources for all participants, especially developing countries. Reforming the international financial system and developing new instruments for attracting private capital is an important step towards reducing risks and increasing the investment attractiveness of renewable energy projects. Removing barriers to investment in countries with unstable economies through the development of technical assistance programs and financial guarantees is also an important condition for success. The energy transition can only be successfully realized through a concerted international effort involving both developed and developing countries. A sustainable energy future and ambitious climate change targets can only be achieved through global cooperation, innovative financial solutions and technology sharing.

Growth and Performance of Self Help Group’s - An Exploratory Study

Today there are Mainstream Micro Finance Institutions like National Agricultural Bank for Rural Development (NABARD), Small Industries Development Bank of India (SIDBI) etc., which are creating micro credit, in rural areas by extending micro finance to Self Help Group (SHG’s) with the help of formal credit institutions, making it an effective poverty reducing strategy and simultaneously promoting rural development. SHG’s were initiated to empower the economically disadvantaged people, who were neglected by formal credit and savings institutions. Micro finance has shown that poor people, when given access to timely and responsive financial assistance, use the proceeds to not only increase their assets and income, but also repay their debts. NABARD, through its Micro Credit Innovations, is the facilitator and mentor of microfinance initiatives in the country. The model of Self help group- bank linage programme (SBLP) has evolved as a cost-effective mechanism for providing financial services to the unreached and deserving poor households. The initiative started as a pilot to link around 500 SHGs of poor to the formal financial institutions during the year 1992-93.The success of the pilot program has shown that Micro Finance can go beyond poverty reduction, to addresses issues of health, education, gender and over all development The present paper is a comparative study of the agency wise i.e. commercial banks, regional rural banks, and cooperative banks, performance in the SHG’s - bank linkage program in Telangana, from2014-18,. The paper also presents an in-depth, analysis of the number of self-help groups, Amount of savings outstanding, amount of loan outstanding of the SHGs, , and total loans disbursed to them through SBLP, generating micro credit to reduce poverty and boost rural development. Key words: NABARD, Micro Finance, SHG’s, SBLP, poverty reduction, rural Development,

TRANSFORMING BANKING SECURITY: THE ROLE OF DEEP LEARNING IN FRAUD DETECTION SYSTEMS

In the digital banking landscape, the increasing volume of online transactions has heightened the risk of fraudulent activities, necessitating the development of more effective detection systems. This study investigates the efficacy of various machine learning and deep learning algorithms in identifying fraudulent transactions, emphasizing Long Short-Term Memory (LSTM) networks. We implemented and evaluated multiple algorithms, including Logistic Regression, Random Forest, Gradient Boosting Machines (GBM), and XGBoost, on a large-scale credit card transaction dataset. Our results demonstrate that the LSTM model outperforms traditional machine learning algorithms, achieving an accuracy of 98.5%, precision of 87.2%, recall of 85.0%, and an Area Under the Curve (AUC) score of 0.94. These findings highlight the superior capability of LSTM networks to capture complex patterns in sequential transaction data, making them an asset for real-time fraud detection in banking. This research underscores the need for financial institutions to adopt advanced deep learning techniques to enhance their fraud detection systems, thereby minimizing financial losses and improving customer trust.

How volatility in the oil market and uncertainty shocks affect Saudi economy: a frequency approach

In this study, spectral Granger causality analysis is employed to investigate the spectral dynamics of uncertainty transmission and its impact on economic growth and financial development in Saudi Arabia from 1993 to 2020. We examine the relationships between crude oil volatility, geopolitical risk, global economic uncertainty, financial development (measured by market capitalisation, MCGDP, Financial Institutions Access Index, FIAIX) and economic growth. The empirical results show that negative shocks in crude oil volatility have a significant impact on financial development as measured by the MCGDP, with capital flight and reduced domestic investment playing key roles, despite the effective contribution of increased government spending to mitigate these effects. In this study, spectral Granger causality analysis is employed to investigate the spectral dynamics of uncertainty transmission and its impact on economic growth and financial development in Saudi Arabia from 1993 to 2020. We examine the relationships between crude oil volatility, geopolitical risk, global economic uncertainty, financial development (measured by market capitalisation, MCGDP, Financial Institutions Access Index, FIAIX) and economic growth. The empirical results show that negative shocks in crude oil volatility have a significant impact on financial development as measured by MCGDP, with capital flight and lower domestic investment playing a key role, despite the effective contribution of increased government spending in mitigating these effects. However, looking at the FIAIX, crude oil volatility has a different impact, with positive shocks having no significant impact on financial development. In contrast, negative shocks show long-term causal effects, which emphasises the vulnerability of the banking sector to oil price fluctuations. Geopolitical risk has a significant long-term impact on the MCGDP due to uncertainty shocks caused by regional and global geopolitical measures. For geopolitical risk, the results are mixed with a significant causality of negative shocks on the FIAIX over certain frequencies, emphasising the sensitivity of the banking sector to geopolitical tensions. In contrast, the world economic uncertainty has only a limited direct impact on the FIAIX, indicating the resilience of the Saudi financial sector to fluctuations in global uncertainty. In terms of economic growth, positive shocks have a greater impact than negative shocks due to the volatility of crude oil. However, shocks resulting from geopolitical risks, whether positive or negative, have little effect on economic growth. The industrial production index, a measure of the resilience of the Saudi economy, indicates that it is susceptible to positive shocks from fluctuations in oil prices over a range of time periods. Our study’s findings can assist Saudi Arabia’s authorities in fortifying the country’s financial and economic resilience against the spread of uncertainty. For strong financial development and sustainable economic growth, these uncertainty elements must be well monitored.

Unlocking sustainability: exploring the nexus of green banking, digital transformation, and financial performance with foreign ownership moderation

The purpose of this research is to empirically test and analyze the impact of green banking, digital transformation, and asset quality on the financial performance of commercial banks with foreign ownership as a moderator. Additionally, it aims to test and analyze the financial performance of sustainable business practices for commercial banks. This study was conducted on commercial banks, specifically focusing on bank groups categorized by core capital levels 3 and 4, listed on the Indonesia Stock Exchange, covering the period from 2017 to 2022. This research employed a judgment sampling technique, encompassing a total of 47 conventional commercial banks. Panel data regression analysis, a data analysis technique, used three variables: the dependent variable, the independent variable, and the moderation variable. The research unveiled that green banking does not appear to have a significant positive impact on financial performance, whereas digital transformation shows a clear positive effect. Non-performing loans, a measure of asset quality, also affect financial performance. Additionally, foreign ownership strengthens the impact of green banking on the financial performance of commercial banks in Indonesia. However, foreign ownership does not enhance the effect of digital transformation. Finally, foreign ownership plays a moderating role in the relationship between asset quality and financial performance in these banks. Ultimately, financial performance has a significant positive influence on sustainable business, suggesting that stronger financial performance contributes to the long-term sustainability of a bank’s operations. The implications of these findings are, first and foremost, that financial institutions should prioritize incorporating sustainable and environmentally friendly practices into their operations to improve their overall financial performance and emphasize the critical role of digital transformation in enhancing financial performance. Consequently, financial institutions are required to allocate resources towards information technology infrastructure and cultivate digital capabilities to adapt to evolving customer demands, such as offering 24/7 banking services from any location. Secondly, financial institutions should not stay far away from digital disruption, but rather embrace it and adapt their strategies accordingly, which includes allocating an adequate budget for IT infrastructure investment and accessing digital services that meet the banking industry’s demands, resulting in effective communication of sustainable practices.

Credit Risk Management Practices and Asset Quality of Deposit Taking Savings and Credit Co-operative Societies in Kenya

Despite the key role DT-SACCOs play towards the growth of Kenyan economy, there is reported increasing occurrence of unsustainable levels of NPLs among these financial institutions. The increasing occurrence of NPLs are significantly obstructing asset quality; highly threatening their survival. This study closed this gap by assessing; risk identification, risk measurement, risk monitoring, and risk governance.as determinants of Kenya DT-SACCOs. The study used descriptive research design with the credit managers of Kenya's 176 licensed DT-SACCOs as the target population. The study employed census because the sample size was modest, easily accessible, and controllable. The data for the study was gathered from primary sources utilizing questionnaire. To create descriptive statistics for the study, the data was analyzed using quantitative analysis. Inferential statistics was then used to obtain a model. The study found that, at 5% significance level, risk identification has positive significant effect on asset quality of DT-SACCOS in Kenya in Kenya (p=0.015; β= 0.337; r= 0.394). risk measurement has a statistically significant and positive effect on asset quality of DT-SACCOS in Kenya (p=0.023; β=0.212; r= 0.358), risk monitoring has positive significant effect on asset quality of DT-SACCOS in Kenya (p= 0.049; β= 0.182; r= 0467) and risk governance positively and significantly affects asset quality of DT-SACCOS in Kenya (p= 0.034; β= 0.236; r= 0.470). The study recommends that the DT-SACCOS in Kenya should; enhance their risk identification policies by ensuring that their procedure authenticity of business proposal and business plan are effective to bale to identify credit risks and net worth of the borrowers, enhance their risk measurement procedure to ensure that there is thorough analysis and appraisal of the borrower before approving loan. enhance their risk monitoring polices and procedures to ensure approval authorities are well specified and empowered, and ensure their risk governance guidelines correctly address credit procedures and policies on review and recommendations and clearly spell out the requirements concerning checks on collateral of the borrower during credit review.

The impact of micro finance institutions on the development of female entrepreneurship evidence from Tunisia

The impact of micro finance institutions on the development of female entrepreneurship evidence from Tunisia

Remittance investment climate analysis: Ascertaining the local development potential of overseas remittances in Philippine rural hometowns

This paper analyzed the potential of remittances in developing the rural hometowns of migrants and their families. Researchers employed a mixed methods tool, the Remittance Investment Climate Analysis in Rural Hometowns (RICART), for over eight years in six municipalities in the Philippines. We found that there is no common trend among municipalities that combining a sound investment climate, an ample number of financial institutions, high levels of residents’ financial literacy and an active program for migrants and their families all ensure remittances-induced hometown investing. Improving rural residents’ financial knowledge and instituting investment climate reforms may help direct remittances to local development.

Advancing Fraud Detection in Banking: Integration of Data Pipelines, Machine Learning, and Cloud Computing

This article examines the transformation of fraud detection in the banking sector through the integration of advanced data pipelines, machine learning (ML), artificial intelligence (AI), and cloud computing technologies. We analyze how modern data pipelines enable real-time processing of vast transactional datasets, significantly improving the timeliness and accuracy of fraud detection. The article explores the application of ML models in identifying suspicious patterns and the role of AI-driven systems in continuously adapting to evolving fraud schemes while reducing false positives. We evaluate the impact of cloud platforms such as AWS, Azure, and Google Cloud in providing scalable, cost-efficient infrastructures for processing massive datasets and supporting seamless integration with ML models. The article presents case studies of successful implementations by major banks, demonstrating substantial reductions in processing times and improved detection efficiency. Additionally, we address key challenges including data quality maintenance, model interpretability, and false positive mitigation. The article concludes by discussing future innovations such as Federated Learning and Explainable AI (XAI), which promise to enhance cross-institutional collaboration and decision-making transparency in fraud detection. This comprehensive analysis provides valuable insights for financial institutions seeking to enhance their fraud detection capabilities in an increasingly complex digital landscape.

Does it pay to be Green? The impact of equator principles on project loans

Some financial institutions voluntarily adopt the Equator Principles, requiring them to monitor the environmental and social (ESG) impacts of projects they finance. We investigate the incidence of these costs on corporate borrowers, as well as evidence of benefits in the form of improved loan terms. Using detailed international loan data, we find that borrowers dealing with green banks derive several economic benefits including lower loan spreads. We argue that dealing with ‘green banks’ allows firms to signal their ESG commitment and can help them manage ex post ESG-related risk. The empirical approach also addresses endogeneity concerns. We document other benefits including greater lender participation and the support of Multilateral Development Banks. Our counterfactual analysis, however, shows that if regulatory changes were to encourage working with green banks, firms that only deal with them as a result of the policy would not obtain lower loan spreads.

How Can Machine Learning Contribute to the Credit Scoring Process in SME Lending?

This research investigates the potential of machine learning to enhance the credit scoring process in small and medium-sized enterprise (SME) lending. SMEs are vital to global economies, accounting for over 90\% of businesses and more than half of total employment worldwide, according to The World Bank (2023). In emerging economies, SME lending is particularly crucial, providing access to capital that supports economic growth and development. However, assessing the credit risk of SMEs poses unique challenges due to limited financial histories and variable cash flows, making accurate credit scoring essential for lenders. Machine learning offers promising solutions for the risk assessment process in SME lending, leveraging algorithms and large datasets to identify patterns and improve decision-making. By applying techniques such as artificial neural networks, decision tree learning, gradient boosting, logistic regression, random forest, and support vector machines, machine learning can enhance the accuracy and efficiency of credit scoring models. This research explores how these machine learning methods can reduce biases, improve risk prediction, and ultimately support sustainable lending practices by enabling lenders to make more informed credit decisions. The findings aim to illustrate the transformative potential of machine learning in SME credit scoring, offering a framework for financial institutions seeking to integrate advanced data-driven technologies in the credit assessment process.

Financial Literacy in India

The Financial literacy is very important and plays a crucial role in individuals economic well-being and overall societal development. Financial Literacy is also important to learn money management. This research paper aims to provide a comprehensive analysis and information of the current state of financial literacy in India. This paper explores various terms and factors which influencing financial literacy levels, such as education, income, money management. It covers the why India need financial literacy into different areas. This research paper investigates the critical role of financial literacy in India. By examining the current state of financial literacy among Indian citizens, the study explores its impact on personal finance decisions, economic well-being, and overall quality of life. The paper analyzes the factors influencing financial literacy levels, including education, income, and access to financial services. Furthermore, it discusses the challenges and opportunities in promoting financial literacy in India and proposes recommendations for policymakers, educators, and financial institutions to enhance financial education

Neobanks in India: Innovation, Challenges and Opportunities in the Banking Industry

Neobanks, also known as digital banks, is a new form of financial institutions that operate exclusively online, without the presence of physical branches. These banks leverage advanced technology for offering innovative and customer-centric banking services. This paper provides a comprehensive review of the evolution, growth, and current landscape of neobanks, highlighting their key differences compared to traditional banks and some of the specific challenges they face in India. The study also assesses the scope and future prospects of neobanks in India, analyzing opportunities for growth amidst regulatory challenges and market situations. This paper also focuses on the global performance of neobanks, particularly in Europe and Korea, and draw comparisons with the Indian context. This paper aims to explore the transformative potential of neobanks in the financial sector. The findings suggest that despite significant regulatory and cybersecurity challenges, neobanks have the potential to redefine the banking experience by offering seamless, user-friendly services tailored to the modern, tech-savvy consumer.

Data Organization Patterns in Data Mesh: Optimizing Data Management for the Modern Enterprise

This article explores the implementation of Data Mesh principles through a structured layered approach to data organization, addressing the challenges of managing large-scale, diverse data ecosystems in modern enterprises. As global data creation surges towards 181 zettabytes by 2025, traditional centralized data architectures are proving inadequate. We propose a four-layer methodology—Raw, Work, Gold, and Publish—that aligns with Data Mesh philosophy while providing clear delineation of data processing stages. Through case studies across various industries, we demonstrate how this approach leads to significant improvements in data quality, time-to-insight, and incident resolution. A large financial institution achieved a 40% reduction in time-to-insight, while a global manufacturer reported a 30% improvement in data quality. The article also discusses the challenges of implementation, including cultural shifts and skill gaps. Our findings suggest that combining Data Mesh principles with a layered organizational structure can transform enterprise data landscapes, enhancing agility, cross-functional collaboration, and alignment of data assets with business objectives. This article contributes to the evolving field of decentralized data architectures and provides practical insights for organizations seeking to optimize their data management strategies in the face of exponential data growth.

Climate-Driven Sustainable Energy Investments: Key Decision Factors for a Low-Carbon Transition Using a Multi-Criteria Approach

Currently, the need for a clean transition has made the upscaling of sustainable energy investment projects imperative. This paper addresses the increasing importance of sustainable energy investment projects in the context of climate change and the urgent need for a global energy transition. Given the complexity of decision-making in this field, a multi-criteria decision-making (MCDM) approach is employed to assess the main criteria considered by project developers and financial institutions. Using the Analytic Hierarchy Process (AHP) method, eight criteria are identified and evaluated. Results highlight differing priorities between project developers and investors, emphasizing the need for adaptable approaches to accelerate sustainable energy investments. The study underscores the importance of understanding diverse stakeholder preferences and priorities in formulating effective strategies and managing associated risks to effectively promote sustainable energy projects. Future research should focus on real-life case studies and policy assessments to further enhance the understanding of sustainable energy investment dynamics.

Credit Risk Prediction Using Machine Learning and Deep Learning: A Study on Credit Card Customers

The increasing population and emerging business opportunities have led to a rise in consumer spending. Consequently, global credit card companies, including banks and financial institutions, face the challenge of managing the associated credit risks. It is crucial for these institutions to accurately classify credit card customers as “good” or “bad” to minimize capital loss. This research investigates the approaches for predicting the default status of credit card customer via the application of various machine-learning models, including neural networks, logistic regression, AdaBoost, XGBoost, and LightGBM. Performance metrics such as accuracy, precision, recall, F1 score, ROC, and MCC for all these models are employed to compare the efficiency of the algorithms. The results indicate that XGBoost outperforms other models, achieving an accuracy of 99.4%. The outcomes from this study suggest that effective credit risk analysis would aid in informed lending decisions, and the application of machine-learning and deep-learning algorithms has significantly improved predictive accuracy in this domain.

Концептуальні рамки управління борговим фінансуванням публічних бюджетів

Introduction. Overcoming the consequences of the COVID-19 pandemic and full-scale Russian aggression against Ukraine caused the destruction of a stable system of managing financial flows not only in our country, but also in many states. Market and regulatory mechanisms for mobilization and the use of financial resources were undermined. Fiscal consolidation measures were changed in favor of expansion. The need to increase the resource base on the basis of state debt has intensified. The use of standardized approaches in the field of fiscal policy did not make it possible to limit risks under new conditions. The task of implementing new conceptual approaches to the management of public budget financing based on the use of debt instruments has arisen. Problem Statement. An increase in debt financing of public budgets helps to expand the volume of resources for the implementation of state plans. At the same time, the pressure on the financial system is growing, which requires making significant changes to the formation of the structure of debt instruments, their placement on domestic and foreign financial markets, attraction of resources on preferential terms from foreign partners, fulfillment of repayment and servicing obligations, refinancing and restructuring of loans. Determining rational management decisions on these issues involves the development of a conceptual framework for public debt financing. The purpose is to study the formation of a conceptual framework for managing debt financing of public budgets and their use in the practice of public administration. Methods. Research methods are used: historical, logical, abstraction, description, generalization, comparison, grouping, analysis and synthesis. Results. The adoption of the debt framework guides the definition of debt strategy, debt management goals, indicators of its volume and structure, debt management tasks, as well as mechanisms and tools for their implementation. The debt strategy reflects the orientation of the debt policy, the goals of public debt management for long and medium-term periods, its indicators, the use of tools and mechanisms for achieving the adopted goals, the formation of the debt portfolio for the specified terms, the classification of risks of government debt management taking into account their impact on the performance of state budget, the application of refinancing and restructuring mechanisms for state liabilities. Conclusions. The conceptual framework for managing debt financing of public budget is an important guiding document aimed at taking into account the conditions for placing debt obligations among investors (state institutions, non-state depositors, non-residents), access to international financial markets, cooperation with international organizations and financial institutions, acceptance of debt portfolio (the structure of state budget financing), built according to types of debt instruments, sources of financing, currencies of loan repayment and servicing, repayment terms, loan servicing rates and debt refinancing. The development and implementation of the conceptual framework for the management of public debt transactions is important for Ukraine. In the conditions of overcoming the consequences of the war and post-war reconstruction, it is necessary to apply a comprehensive approach to the formation, distribution and use of resources raised on the basis of the use of public debt instruments.

The Role of Sharia-Compliant Financial Institutions in Promoting Economic Stability: Insights from Recent Scholarly Contributions

This research examines the role of Islamic financial institutions in promoting economic stability through a literature study of recent scholarly contributions. Islamic financial institutions are seen as alternative financial instruments that operate under Islamic principles, which prohibit usury, excessive speculation, and non-transparent activities. This research aims to understand the mechanisms used by Islamic financial institutions in creating economic stability and evaluate the latest empirical evidence supporting such contributions. The research method used is a literature study with a qualitative approach, where various journals and research reports on Islamic finance, economic stability, and monetary policy are reviewed and analysed. The results show that Islamic financial institutions play an important role in economic stability through asset-based financing, portfolio diversification, as well as the application of risk management in accordance with Islamic principles. This research contributes to providing policy makers, academics, and financial practitioners with a deeper understanding of the potential of Islamic financial institutions as instruments that can strengthen economic stability.

Evolving Transparent Credit Risk Models: A Symbolic Regression Approach Using Genetic Programming

Credit scoring is a cornerstone of financial risk management, enabling financial institutions to assess the likelihood of loan default. However, widely recognized contemporary credit risk metrics, like FICO (Fair Isaac Corporation) or Vantage scores, remain proprietary and inaccessible to the public. This study aims to devise an alternative credit scoring metric that mirrors the FICO score, using an extensive dataset from Lending Club. The challenge lies in the limited available insights into both the precise analytical formula and the comprehensive suite of credit-specific attributes integral to the FICO score’s calculation. Our proposed metric leverages basic information provided by potential borrowers, eliminating the need for extensive historical credit data. We aim to articulate this credit risk metric in a closed analytical form with variable complexity. To achieve this, we employ a symbolic regression method anchored in genetic programming (GP). Here, the Occam’s razor principle guides evolutionary bias toward simpler, more interpretable models. To ascertain our method’s efficacy, we juxtapose the approximation capabilities of GP-based symbolic regression with established machine learning regression models, such as Gaussian Support Vector Machines (GSVMs), Multilayer Perceptrons (MLPs), Regression Trees, and Radial Basis Function Networks (RBFNs). Our experiments indicate that GP-based symbolic regression offers accuracy comparable to these benchmark methodologies. Moreover, the resultant analytical model offers invaluable insights into credit risk evaluation mechanisms, enabling stakeholders to make informed credit risk assessments. This study contributes to the growing demand for transparent machine learning models by demonstrating the value of interpretable, data-driven credit scoring models.

Post-Extractivist Horizons in Latin America: Between Utopias and Struggles for Re-Existence Against Neo-Extractivism

The triumph of neoliberal globalisation has been presented as the end of history, with no viable alternatives available. In Latin America, however, the structural adjustment programmes imposed by international financial institutions and the increased role of transnational corporations were resisted by social and popular movements that eventually translated into progressive politics and policies. Nevertheless, despite anti-capitalist and pro-environment rhetoric, extraction of natural resources has continued to expand under progressive governments, with devastating effects on environmental justice and Indigenous peoples’ rights. Focusing on socio-territorial struggles for re-existence in Bolivia through the cases of peasant struggle in Tariquía and the establishment of the National Coordinator for the Defence of Indigenous Territories and Protected Areas (CONTIOCAP), we investigate how Indigenous and peasant organisations resist extractive projects and organise collective life in alternative ways. We show how social actors, especially women, defend, propose and imagine post-extractivist alternatives as a societal horizon – both utopian and possible – to the current socio-ecological crisis, in a context of disillusionment with the government’s rhetoric of vivir bien as a state-led ecological and Indigenous policy. We argue that by defending life, women try to heal the human and more-than-human relationality in the territories impacted by progressive neo-extractivism.