Low levels of financial literacy and poor financial management practices are still the main problems faced by micro, small and medium enterprises that are based on community and religious organizations. Problems with unorganized financial recording systems, in addition to the inability to separate business from personal finances, are obstacles to obtaining financing as well as making appropriate business decisions. The community service program is intended to improve the financial management capacity of micro, small and medium enterprises based at the Al-Muhajirat Mosque Taklim Assembly in Biring Romang Village, Makassar City through practical training on basic accounting. The method used is lectures, tutorials, and discussions tailored to the needs of partners with a participatory approach. The evaluation was carried out using participatory observation, case studies as well as post-training assessments to measure the effectiveness of the program. The results showed a significant increase in participants' ability to record transactions, implement a simple accounting cycle, and separate business from personal finances. Participants can also prepare basic financial reports for decision-making purposes. This program strengthens MSMEs' readiness for more accountable, transparent, and sustainable financial management.

Climate policy uncertainty (CPU) poses growing risks to macroeconomic stability, yet its transmission mechanisms remain understood. While the literature has examined general uncertainty, the state-dependent effects of transition risk through the financial system are underexplored. Using U.S. local projections and a two-sector New Keynesian model, this paper examines how CPU shocks propagate across the business cycle. I find that CPU shocks significantly contract investment and credit during economic expansions, whereas their effects are statistically muted during recessions. These dynamics are driven by a brown collateral channel, where uncertainty about the future value of carbon-intensive assets triggers a financial accelerator mechanism. The findings highlight that transition risk operates as a systemic financial shock, suggesting that macro-prudential frameworks must account for the volatility of brown asset valuations. • Climate policy uncertainty represents a distinct source of macro-financial risk. • CPU shocks are contractionary and propagate through financial channels. • The contractionary effects of CPU are concentrated in economic expansions. • Uncertainty about brown asset values drives the financial transmission mechanism. • Countercyclical implementation of climate policies minimizes macroeconomic costs.

Post-growth has emerged as an umbrella concept for various sustainability visions that prioritise the pursuit of environmental sustainability, social equity, and human wellbeing, while questioning the pursuit of economic growth. Although there are increasing calls to include post-growth scenarios in high-level assessments, there is no coherent framework that specifies what is required to model post-growth. This article addresses this gap by: (1) identifying the minimum requirements for post-growth models, and (2) establishing a set of model elements for representing post-growth policy themes. We survey post-growth modellers and draw on the post-growth literature to develop a framework of minimum requirements for post-growth models, integrating biophysical, economic, and social spheres and linking them to post-growth goals. Regarding the biophysical sphere, models should include resource use and pollution, environmental limits, and feedback mechanisms from the environment back to society, reflecting ecological embeddedness. Regarding the economic sphere, models should disaggregate households, incorporate limits to technological change and decoupling, include different types of government interventions, and calculate GDP or output endogenously. Regarding the social sphere, models should represent time use, material and non-material need satisfiers, and the affordability of essential goods and services. Specific policies and transformation scenarios require additional features, such as sectoral disaggregation or representation of the financial system. Our framework aims to guide the development of models that can simulate both post-growth and pro-growth scenarios. Such models are needed to inform policymakers and stakeholders about the full range of options for pursuing sustainability, equity, and wellbeing. • We surveyed 38 modellers to identify the elements of a post-growth model. • We propose a framework for models to adequately represent post-growth scenarios. • Post-growth models should represent the biophysical, economic, and social spheres. • Modellers should avoid embedding artificial growth dependencies. • Finance, environmental feedbacks, and non-monetary provisioning remain challenges.

Building entrepreneurial infrastructure: Discursive strategies and the development of China's venture financing system

Accounting fraud is a big problem that affects companies, investors, and the overall economy, particularly in Indian listed companies where money transactions are now more complex and done by online. If companies give wrong financial information, report incorrect income, and hide their liabilities it misleads stakeholders and reduce trust in financial systems. This research highlights on how artificial intelligence (AI) can make fraud detection in accounting faster and with better accuracy. Conventional fraud detection methods mainly depend on manual review and simple auditing techniques, which take a lot of time and may not to be determine hidden or sophisticated frauds. Compared to older methods, AI tools like machine learning, data mining, and predictive analytics can quickly study large amount of financial data and spot unusual patterns, trends, and irregularities that may suggest fraud. This system can also learn from previous information and become more accuracy over time. The study examines on company data from selected Indian listed companies to examine the effectiveness of AI-based models can detect fraud financial statements. It also shows the role of AI in helping auditors, improving risk management, and making company control systems. The findings highlights that AI increases the speed and accuracy of fraud detection but also helps in identify fraud early and prevent it. In overall study concludes that the benefits of artificial intelligence in accounting can strongly reduce the occurrence of fraud and improve company financial report quality, and strengthen trust among investors and stakeholders.

The rapid expansion of global financial markets and the increasing participation of retail investors have created a strong demand for accessible and interpretable stock market analytics tools. Despite the availability of large volumes of financial data, extracting meaningful insights from raw market information remains challenging for students, novice investors, and small-scale market participants due to the complexity, technical requirements, and high costs associated with many professional trading platforms. To address this issue, this paper presents INVESTO, a web-based interactive stock market analytics and visualization platform designed to simplify financial data exploration and support informed investment decision-making. The platform is developed using Python and the Streamlit framework and provides an intuitive interface that enables users to access both historical and real-time stock market data without requiring programming knowledge or advanced financial expertise. INVESTO integrates financial data acquisition through external APIs, preprocessing and normalization of time-series data, and computation of key technical indicators such as Simple Moving Average (SMA), Exponential Moving Average (EMA), and volatility metrics. These analytical outputs are presented through interactive visualizations built using Plotly, allowing users to analyze price movements, detect trends, and compare stock performance dynamically. The system architecture follows a modular three-layer design consisting of the Data Acquisition Layer, Processing Layer, and Presentation Layer, ensuring scalability, maintainability, and efficient data flow. Cloud-based deployment through Streamlit Cloud enables platform accessibility through web browsers while maintaining efficient resource utilization and responsive performance. Performance optimizations such as session-based caching, vectorized computations, and selective rendering improve system responsiveness and reduce computational overhead. Security considerations focus on maintaining data integrity, validating external API responses, and preserving user privacy through a read-only analytical design that avoids storage of sensitive user information. Experimental evaluation and usability observations indicate that the platform improves comprehension of market trends, supports exploratory financial analysis, and enhances financial literacy for students and beginner investors. While INVESTO prioritizes interpretability and visualization rather than predictive modelling, the modular architecture allows future integration of advanced features such as machine learning-based forecasting, sentiment analysis, risk metrics, and portfolio simulation. Overall, the platform demonstrates the effectiveness of combining modern web technologies, cloud deployment, and interactive visualization techniques to bridge the gap between raw financial data and actionable analytical insights in educational financial technology systems.

Credit risk assessment is a critical component of financial decision-making systems. While complex machine learning models often achieve superior predictive performance, they lack interpretability, which is essential for regulatory compliance and stakeholder trust in financial institutions. This study investigates the performance and explainability trade-offs among lightweight machine learning models, including Logistic Regression, Decision Trees, Random Forest, and XGBoost, for credit risk prediction. The models are evaluated using standard classification metrics such as Accuracy, Precision, Recall, F1-Score, and ROC-AUC. Additionally, model interpretability is examined using feature importance analysis and SHAP (SHapley Additive exPlanations). Experimental results demonstrate that while XGBoost achieves the highest predictive accuracy, Logistic Regression provides superior interpretability. Random Forest offers a balanced trade-off between performance and transparency. This study highlights the importance of explainable AI in financial risk modeling and provides practical insights for deploying transparent and efficient machine learning systems in regulated environments.

The rapid growth of online transactions and digital banking has significantly increased the use of credit cards for financial activities. However, this convenience has also led to a rise in credit card fraud, causing major financial losses for banks and customers. Traditional fraud detection systems mainly rely on rule-based methods, which are often inefficient in identifying new and complex fraud patterns. To address this challenge, this project proposes a Machine Learning Based Credit Card Fraud Detection System that automatically analyzes transaction data and identifies fraudulent activities with high accuracy.The system utilizes modern technologies such as Python for backend development and machine learning algorithms to analyze transaction patterns and detect anomalies. Transaction data is first preprocessed to remove noise and extract important features such as transaction amount, time, and location. Machine learning algorithms like Logistic Regression, Random Forest, and Isolation Forest are then applied to classify transactions as legitimate or fraudulent. The proposed system aims to improve fraud detection accuracy, reduce financial losses, and enhance the security of online transactions. By leveraging data-driven techniques and intelligent algorithms, the system can identify suspicious activities in real time and assist financial institutions in preventing fraudulent transactions. Experimental results show that the machine learning model can effectively detect fraudulent behavior and improve the efficiency of fraud detection systems.Furthermore, the system provides a scalable architecture for handling large volumes of transaction data and supports faster decision-making in financial security systems. By integrating machine learning with modern data processing techniques, the proposed system offers a reliable and efficient solution for credit card fraud detection in digital payment environments.

The increasing adoption of digital payment systems has significantly reduced transactional friction, often leading to diminished financial awareness and impulsive spending behavior. Existing personal finance applications primarily provide expense tracking and retrospective visualization, offering limited real-time behavioral intervention. This paper presents the design and implementation of an explainable artificial intelligence-based financial nudging system that transforms passive budgeting tools into proactive behavioral coaching platforms. The proposed framework integrates structured rule-based decision modelling with context-aware conversational feedback to deliver categorized, timely, and personalized nudges aligned with behavioral economic principles. A full-stack web architecture is implemented to support transaction processing, budget evaluation, and dynamic nudge generation while preserving data privacy through secure authentication and controlled storage mechanisms. The study focuses on system design, decision formulation, and functional validation rather than longitudinal behavioral experimentation. The results demonstrate the feasibility of operationalizing explainable AI techniques for financial habit intervention within a deployable application framework, establishing a foundation for future empirical evaluation and adaptive optimization

Subject. Channels of crediting and financing in the Russian economy. Objectives. To identify the role and assess changes in lending and financing channels in the Russian economy, as well as identify risks to the financial system. Methods. The research is based on a comprehensive time series analysis using econometric analysis, linear trend construction and quality assessment. Results. A stable structural asymmetry of lending and financing channels in the Russian economy has been identified, which has consolidated its role as the main, most stable and dominant channel – bank lending. The capital market performs a complementary function – corporate bonds serve as an important complement to loans, while the stock market is not a significant source of financing due to high volatility. A significant structural shift has been the strengthening of the role of the Bank of Russia as a lender of last resort, which indicates liquidity imbalances in the banking sector. The contradiction between the growing need for borrowed resources and the tightening of monetary policy creates risks for investment activity and consumption. Conclusions. The results of the study will be useful in developing measures to ensure financial stability, as well as for commercial banks and issuing companies when forming lending and financing strategies.

Purpose This study aims to investigate how renewable energy adoption, digital transformation, financial inclusion and institutional quality contribute to economic growth in six Australian territories: New South Wales, Queensland, South Australia, Tasmania, Victoria and Western Australia. It seeks to clarify whether these factors, individually and collectively, foster sustainable economic development and advance progress toward key Sustainable Development Goals (SDGs). Design/methodology/approach Using annual data from 1990 to 2024, the analysis applies advanced econometric techniques, including the common correlated effects mean group, mean group and augmented mean group estimators. These methods allow for heterogeneity across regions while capturing the long-run relationships among renewable energy, digitalization, financial inclusion, institutional quality and economic growth. Findings Results demonstrate that renewable energy adoption has a significant positive effect on economic growth, with estimated contributions ranging between 0.246% and 1.240%. Digitalization and financial inclusion further enhance growth, contributing increases of 1.227%–2.231% and 1.025%–1.033%, respectively. The combined effects of renewable energy and digitalization improve infrastructure efficiency, reduce production costs and strengthen regional competitiveness. Financial inclusion broadens participation in the economy, ensuring more equitable growth. Institutional quality amplifies these outcomes, reinforcing long-term sustainability and resilience. Practical implications The findings suggest that policymakers should prioritize policies that expand renewable energy deployment alongside digital infrastructure and inclusive financial systems. Strengthening institutional frameworks will be crucial for maximizing the benefits of these factors, enabling Australia’s territories to achieve multiple SDGs simultaneously, particularly SDG 7 (Affordable and Clean Energy), SDG 8 (Decent Work and Economic Growth), SDG 10 (Reduced Inequalities) and SDG 11 (Sustainable Cities and Communities). Originality/value This research provides one of the first integrated assessments of how renewable energy, digitalization, financial inclusion and institutional quality jointly shape economic development within the Australian context. By offering region-specific evidence, it contributes to the broader debate on sustainable growth strategies and provides actionable insights for policymakers in both developed and developing economies.

This study aims to analyze the Islamic economic perspective on riba and its implications in the modern financial system. Riba is strictly prohibited in Islam as it involves elements of injustice and economic exploitation. This research employs a qualitative approach with a normative method through library research. The data used are secondary data obtained from the Qur’an, Hadith, books, and scientific journals related to Islamic economics and riba. The findings indicate that riba has substantial similarities with interest in the modern financial system, particularly due to the predetermined additional payment without risk-sharing. Furthermore, riba has negative impacts on economic and social structures, such as increasing wealth inequality, debt burdens, and the potential for financial crises. As an alternative, Islamic economics offers a profit and loss sharing system that is more just and sustainable. Therefore, the prohibition of riba is not only normative but also relevant in creating a stable and equitable economic system.

Background: The Tanzanian informal economy, driven by "Wamachinga" (street vendors), constitutes over 60% of the urban workforce yet remains digitally invisible to formal financial systems. As Tanzania pursues the strategic milestones of the Tanzania Development Vision 2050 (Dira 2050), traditional financing models—including Foreign Direct Investment (FDI) and Official Development Assistance (ODA)—are proving insufficient for domestic resource mobilization. Objective: This research introduces the "Wamachinga Protocol," a decentralized socio-technical framework designed to bridge the gap between informal micro-transactions and formal capital markets. The protocol aims to transition vendors from "digital invisibility" to "investment readiness." Methodology: Utilizing a Design-Based Research (DBR) approach, the study evolved through three iterative phases: (1) capturing peer-to-peer "Undugu" trust logic via smartphone interfaces, (2) prototyping Blockchain-based Decentralized Identity (DID) for immutable record-keeping, and (3) deploying AI-driven heuristic scoring to assess risk in low-bandwidth (USSD) environments. Results: The findings demonstrate that street-level transaction velocity can be mathematically converted into "Trust Capital" using the heuristic equation $TS = \alpha(V) + \beta(P) + \gamma(C) - \delta(D)$. This Trust Capital enables vendors to bypass traditional collateral requirements, facilitating direct access to sovereign investment vehicles such as UTT AMIS and Treasury Bonds. Conclusion: The Wamachinga Protocol provides a scalable model for domestic resource mobilization in Sub-Saharan Africa. It suggests that by digitizing communal trust into verifiable credentials, emerging economies can achieve financial sovereignty and meet UN Sustainable Development Goals (SDGs) without total reliance on external debt.

This study investigates how financial and non-financial compensation influence employee performance at PT Urchindize Madura, Indonesia, with job satisfaction as a mediating factor. Using a quantitative explanatory design, data were collected from all 46 employees and analyzed through partial least squares structural equation modeling (PLS-SEM). Results indicate that financial compensation significantly enhances job satisfaction, which in turn improves employee performance. Job satisfaction partially mediates the link between financial compensation and performance, while non-financial compensation shows no significant effect. These findings suggest that organizations should focus on competitive financial reward systems and re-evaluate non-financial benefits to better meet employee expectations. By strengthening compensation strategies grounded in employee satisfaction, companies can foster greater motivation, satisfaction, and productivity.

This study investigates the impact of digital economy development on tax revenue mobilization in 38 emerging economies from 2000 to 2023. Employing panel autoregressive distributed lag estimators and addressing cross-sectional dependence through the common correlated effects estimators, we examined both the direct effect of the digital economy and its interaction with institutional quality and financial development. The baseline results reveal a negative and significant association between the digital economy and tax revenue mobilization, suggesting that the rapid expansion of digital economic activities may erode tax bases, enable profit shifting, and exacerbate enforcement gaps in settings where tax systems and regulatory frameworks have not adapted to the digital environment. However, when interaction terms are introduced, the moderating roles of institutional quality and financial development emerge as positive and statistically significant, while the direct effect of the digital economy becomes insignificant. Subsample analysis confirms this heterogeneity; the digital economy exerts a positive and significant impact in countries with higher institutional quality and deeper financial development, but remains negative and significant in countries with weaker institutional and financial systems. These findings underscore the importance of complementary institutional and financial reforms to ensure that digital transformation translates into stronger fiscal capacity in emerging economies. JEL Classification C33, H20, O33

The rapid advancement of information technology has triggered significant transformations in financial transaction systems, particularly through the implementation of mobile banking as a digital platform that facilitates various payment processes. This study aims to analyze the impact of mobile banking usage on the effectiveness of English Proficiency Test (EPT) payment transactions among students at Universitas Muhammadiyah Gresik. A quantitative research design was employed using a survey method, involving 120 respondents selected via purposive sampling specifically, students who have conducted EPT payments through mobile banking. Data collection was performed using a questionnaire with a five-point Likert scale, measuring ease of use, transaction speed, and security as independent variables, and transaction effectiveness as the dependent variable. Data analysis utilized SPSS, encompassing validity and reliability tests, t-tests, and coefficient of determination. The results indicate that all questionnaire items are valid and reliable. The t-test confirms the positive and significant influence of ease of use, speed, and security on transaction effectiveness. The coefficient of determination value (R²) of 0.890 means that 89% of the variation in transaction effectiveness is explained by these three variables, while the remaining 11% stems from other factors outside this study. Among these variables, security emerges as the most dominant factor influencing transaction effectiveness. These findings underscore the crucial role of user trust in the system for mobile banking adoption. Thus, mobile banking proves effective in enhancing the efficiency, speed, and accuracy of EPT payment transactions in the higher education environment.

Financial planning and expense management have become increasingly essential as individuals struggle to monitor daily spending, interpret financial behavior, and plan future investments effectively. With the rapid growth of digital transactions and diverse expenditure categories, manual financial tracking becomes complex, time-consuming, and prone to errors. Traditional financial management systems, such as spreadsheets and basic budgeting tools, allow users to store transaction data but lack advanced analytical capabilities, making accurate prediction of future expenses difficult and leading to inefficient financial decisions. To address these limitations, the proposed system introduces an intelligent financial prediction and analysis platform developed using the Django framework with a MySQL (Structured Query Language-based relational database management system) database for secure data storage and management. The system incorporates email-based OneTime Password (OTP) authentication using the Simple Mail Transfer Protocol (SMTP) to ensure secure and verified user access. A Long Short-Term Memory (LSTM) deep learning model is utilized to analyze historical financial data and predict future expenses by capturing temporal patterns and trends. Additionally, K-Means clustering is applied to group expenses into different categories, enabling users to better understand their spending behavior and identify financial patterns. The system further integrates sentiment analysis using VADER (Valence Aware Dictionary and Sentiment Reasoner) to evaluate user feedback and classify it as positive, negative, or neutral, supporting continuous system improvement. By combining predictive modeling, clustering techniques, sentiment evaluation, and secure authentication mechanisms, the system provides automated financial insights, personalized recommendations, and enhanced decision-making support, helping users manage finances more efficiently.

This study aims to analyze the responsibility of financial sector business actors (PUSK) in providing inclusive financial services for persons with disabilities. The main focus of this study is the SETARA Guidelines on Accessible Financial Services for Empowered Persons with Disabilities, issued by the Financial Services Authority (OJK) as a reference. The method used is normative juridical with a regulatory approach, through a literature study of laws, regulations, and related policy documents, including Law Number 8 of 2016 concerning Persons with Disabilities. The study’s results show that the SETARA Guidelines have comprehensively formulated accessibility principles across six service aspects: physical infrastructure, digital infrastructure, service sensitivity, document accessibility, complaint mechanisms, and assistance. However, the implementation of these guidelines still depends on the commitment of each PUSK. This may potentially create access gaps for consumers with disabilities and does not yet guarantee the fulfillment of their rights to equal financial services. The study recommends integrating the principles of the SETARA Guidelines into mandatory regulations and strengthening the role of the OJK in compliance supervision. These efforts are necessary as part of the transformation towards an inclusive and equitable national financial system.

ABSTRACT The rapid development of green finance has reshaped the structure of China’s financial system. Understanding how green finance interacts with traditional financial markets is essential for maintaining market stability and supporting low-carbon transition. This study examines the time-varying causal relationship between green finance and traditional financial markets in China across multiple frequency scales. Methodologically, we apply Ensemble Empirical Mode Decomposition (EEMD) and a time-varying Granger causality framework to capture dynamic and scale-dependent effects. Empirical results show that the overall causal connections are relatively weak. However, bond prices exert a significant influence on both carbon and green bond markets. The carbon market mainly affects stock prices during periods of policy adjustment, while bond market movements consistently shape green bond dynamics. Multi-scale analysis further shows that short-term causal effects are temporary and largely driven by event shocks. In contrast, medium- and long-term causal effects between green finance and traditional financial markets are more persistent and stable. These findings suggest that strengthening coordination between green finance and traditional financial markets is crucial for enhancing financial stability and promoting sustainable development in China.

This research explores the consequences and functions of Foreign Direct Investment (FDI) on the banking and finance sector within the years 2014 to 2024. It is noted that FDI is a major catalyst towards globalization and economic integration, alonside emerging economies poising great importance to financial systems. Stepping into the previous decade, the greater integration of global markets, advancement in technology, and liberalization of the FDI policies made it possible for international investors to gain prominent control over domestic banking and financial systems. In this paper, I focus on examining the trends regarding FDI inflows into the financial sector, the changing regulatory frameworks, and the consequences for financial institutions including the better capital access, greater operational effectiveness, proper level of risk management, and the proliferation of new financial services like digital banking and innovative financial products.