Potential Financial Risks Research Articles

The Economic Efficiency of Micro Biogas Plants: A Sustainable Energy Solution in Slovenia—Case Study

This paper presents a simulation model for determining the most suitable type of microbiogas plant for small local communities on Slovenian farms, focusing on the efficient processing of organic waste. This model uses various input parameters, including different types and quantities of slurry and corn silage. Four different scenarios were developed to represent potential plant sizes, each evaluated using key economic indicators: net present value, breakthrough price, and internal rate of return. A scenario sensitivity analysis was conducted to assess the effects of changes in investment costs, fluctuations in energy prices, and the addition of corn silage to the anaerobic digestion process. The results highlight significant differences in economic viability across the scenarios, with some demonstrating positive financial outcomes and shorter payback periods and others indicating potential financial risks and longer recovery times under certain conditions. The analysis suggests that smaller micro biogas plants may struggle to achieve profitability without optimizing input ratios or reducing costs, whereas larger plants show more favorable economic indicators, provided certain conditions are met. Furthermore, the economic efficiency improves when adding maize silage to the fermentor mixture.

The impact of predictive analytics on financial risk management in businesses

This paper examines the impact of predictive analytics on financial risk management in businesses. Predictive analytics involves the use of statistical algorithms, machine learning, and data mining techniques to analyze historical data and predict future outcomes. In the context of financial risk management, predictive analytics plays a critical role in identifying, assessing, and mitigating potential financial risks. This paper explores various machine learning algorithms, including neural networks, decision trees, and support vector machines, and their applications in risk management. It also discusses data sources, preprocessing techniques, and the challenges associated with data privacy, model interpretability, and prediction accuracy. The review highlights successful implementations of predictive analytics in financial risk management and provides recommendations for future research and practical applications. As predictive analytics continues to advance, its integration with emerging technologies such as artificial intelligence and blockchain promises to enhance financial risk management practices.

Potential financial loss and risk factors for hamstring muscle injuries in elite male Brazilian soccer players: a season-long prospective cohort pilot study.

The aim of this pilot study was to analyze the potential financial loss and a range of potential risk factors for hamstring muscle injuries in elite Brazilian soccer. Thirty-four male players (age: 25 ± 6 years; stature: 180 ± 8 cm; body mass: 78 ± 9 kg; minutes played in matches: 2243 ± 1423 min) from an elite professional soccer club were monitored during a 12-month season. Muscle injury was identified by magnetic resonance imaging and the severity was defined according to the number of days away: minimal (1-3 days), mild (4-7 days), moderate (8-28 days), severe (>28 days). Potential financial loss due to the team's under achievements was determined. Dorsiflexion range of motion, eccentric knee flexor strength and isokinetic tests were performed during the pre-season. Association between dependent variables and the occurrence of injury was evaluated. Nine hamstring muscle injuries with moderate severity were found in 8 athletes. Recovery time was 22 days off the field on average. Potential financial loss was $-43.2 million USD and earnings on merit money was 21%. Previous injury, increased flexor deficit 60° /sec and increased flexor fatigue index 300°/sec were all associated with a greater chance of hamstring muscle injury. Ankle dorsiflexion range of motion was significantly lower in the injured group (35.6 ± 3° vs. 39.1 ± 4.9°; p = 0.017, effect size = -0.74). High financial burden was found in elite Brazilian soccer during one full season. Injured athletes had high hamstring fatigue index, knee flexor strength deficit, ankle range of motion restriction and previous hamstring muscle injury when compared to non-injured athletes. Therefore, preventive approaches in professional soccer players with previous hamstring injuries should be a priority.

Research on machine learning and financial risk early warning of listed companies based on random forest model

This paper explores the application of machine learning technology based on random forest model in financial risk early warning of listed companies. By analyzing the data, we constructed a random forest model to identify potential financial risk factors. The study found that the random forest model showed high accuracy and stability in dealing with financial risk early warning issues. The model can effectively identify key indicators that affect the company's financial status and provide timely risk warning signals for investors and management. In addition, we also discussed the limitations of the model and the direction of future research, aiming to provide listed companies with a more accurate and effective financial risk early warning system, thereby helping companies to identify and respond to potential financial challenges as early as possible.

Analysis of Apple Financial Risk Management

In the dynamic financial environment of the 21st century, Apple Inc. is a model of innovation and market leadership. It is known for its innovative products and groundbreaking business strategies that have solidified its leadership position in consumer electronics, software and online services. In today's fast-paced and diverse financial environment, where technological advancements and regulatory changes are believed to be the norm, corporations like Apple Inc. are facing a growing number of financial threats, explaining the significance of effective financial risk management. This paper aims to investigate the numerous measures Apple Inc. has taken to reduce its financial risks especially market risks such as currency fluctuations, market volatility, and interest rate fluctuations. Particular emphasis is placed on how Apple strategically prevents the mentioned risks through internal controls and a diversified portfolio, highlighting the effectiveness of these measures. The article also analyzes how Apple manages liquidity, complies with regulatory requirements, and uses the latest advanced technology for analyzing and predicting potential financial risks. The results emphasize how crucial Apple's financial risk management is to uphold its financial standing and ensure the company's long-term survival and stability in an uncertain global economy.

The Advent of Generative AI and Financial Industry

Purpose: This paper explores the recent literature on Generative AI applications in the financial industry and delineates its role in the future. Methodology: Our paper follows secondary research analyzing current literature on Generative AI in finance. It is one of the essential tools for understanding background information, identifying research problems, and filling the literature gaps. This paper studies how Generative AI has potential financial benefits and risks, providing unique insights into the financial landscape in the coming years. Findings: The findings unveil that Generative AI can become a strategic tool to redefine financial services and operational effectiveness. It can substantially improve the services by reducing costs, bringing efficiency, and enhancing corporate performance. It has the enormous transformative power to revolutionize client product and service offerings, improving risk management assessments and bringing efficiency to operations. However, our study indicates that the financial service industry can get into practices and decisions that are potentially unethical and financial exclusion due to an embedded bias in its algorithm and design of Generative AI technologies. Since Generative AI continues to evolve, its role and effectiveness in decision-making are expected to shape the financial services landscape significantly. Unique Contribution to Theory, Practice, and Policy: Generative AI can be a game changer for the financial industry, fueling digital transformation across industries. The transformative potential of generative AI can optimize operations, revolutionize customer experiences, and drive innovation seamlessly in finance. Our paper suggests how policymakers can foresee the challenges ahead due to the Generative AI in finance services, which is challenging the existing regulatory landscape. To stay ahead in the competition, financial firms must balance data privacy and algorithmic bias and ensure the responsible use of AI.

Development of Financial Data Mart for Nonprofit Organization

Finance is a critical component of organizational long-term viability. Non-profit organizations must pay close attention to their financial health. This is a form of accountability for funds entrusted to the organization by its stakeholders. Additionally, organizations must prepare for potential future financial risks. As a non-profit organization, SATUNAMA Foundation currently administers its financial data separately for each funding source. This complicates financial analysis and decision making, particularly when attempting to comprehend the organization’s financial health holistically. A financial data mart was built in order to assist data analysis as well as store historical data based on a predetermined set of analysis criteria. The Kimball’s method and the starflake schema were used in the development of this data mart. The data mart was successfully implemented in accordance with the schema design and was able to fulfil the given analysis criteria. The data mart has fulfilled the four characteristics of data warehouse based on the data characteristic evaluation. From the operational perspective evaluation, data mart is proven faster than operational database thus making the data mart more efficient in providing information. Dashboard and pivot table are also successfully built to visualize the results.

Climate risks and corporate tax shields

ABSTRACT This study investigates the influence of climate risks on the tax shields of US-based firms. The findings show a significant negative correlation between climate risks and debt-related tax shields (DTS), indicating reduced reliance on debt financing due to potential financial risks and regulatory uncertainties. Conversely, non-debt tax shields (NDTS) demonstrate a positive relationship with climate risks, highlighting firms’ proactive efforts to seek tax benefits from alternative sources. Additionally, the study uncovers distinct dynamics for high and low-leveraged firms in utilizing tax advantages. The research contributes valuable insights into the intricate interplay of climate risks, tax benefits, and corporate financing decisions.

When uncertainties matter: The causal effect of cryptocurrency investment on retirement hardship withdrawals

Cryptocurrencies are invested in by approximately 15% of U.S. households. However, the high volatility of these assets poses substantial financial risks, particularly as 40% of U.S. households already face potential retirement shortages. Therefore, the present study aims at investigating the impact of cryptocurrency investment on retirement borrowing. Our causal analysis of 1,912 respondents revealed that cryptocurrency investors are 7.4% more likely to make a hardship withdrawal from their retirement accounts. This study provides insights for researchers and practitioners on managing retirement accounts by highlighting the potential financial risks associated with cryptocurrency investments.

Influence of Financial Toxicity on the Quality of Life in Lung Cancer Patients Undergoing Immunotherapy: The Mediating Effect of Self-Perceived Burden.

The purpose of this study is to understand the level of quality of life (QOL) of lung cancer patients receiving immunotherapy and to clarify the potential mediating role of self-perceived burden (SPB) in the relationship between financial toxicity (FT) and QOL. A convenience sample of 342 lung cancer patients receiving immunotherapy was recruited from a cancer hospital from October 2022 to April 2023 for this cross-sectional study. The participants were requested to complete the following structured questionnaires: a sociodemographic and clinical questionnaire, the Functional Assessment of Cancer Therapy-Lung (FACT-L), the Self-Perceived Burden Scale (SPBS) and the COmprehensive Score for Financial Toxicity (COST). The data were subjected to Pearson correlation analysis and bootstrapping analysis in structural equation modelling. The total FACT-L score was 79.90±15.84 points in 322 lung cancer patients receiving immunotherapy. FT (β = 0.37, P < 0.01) and SPB (β = -0.27, P < 0.01) had a direct effect on QOL. In addition, SPB partly mediated the association between FT and QOL, and the standardized indirect effect was 0.19, accounting for 33.9% of the total effect. The present study revealed that there is still much room for improvement in the QOL of lung cancer patients during immunotherapy. A greater financial burden resulted in a greater self-perceived burden and was thus associated with inferior QOL. It is imperative for oncology nurses to routinely assess QOL, FT or risk and SPB for lung cancer patients undergoing immunotherapy as well as to assist those patients in understanding the potential financial risk of each choice and help them take more active roles in their routine clinical care.

Bank Competition and Bank Risk: Evidence from an Emerging Economy

This paper investigates the relationship between banks’ competition and nonperforming loans (NPLs), considering GDP per capita, gross savings, and domestic bank credit to the private sector. The study utilizes comprehensive secondary data from 1994 to 2022, scrutinizing diverse banking environments and revealing significant findings. The analysis reveals a significant positive correlation between the number of banks and the NPL ratio, indicating that increased competition leads to higher NPL. Nonetheless, banks' domestic credit to the private sector has little effect on NPLs. The analysis also reveals a significant positive correlation between gross savings and the NPLs. Furthermore, higher NPLs ratio is linked to reduced commercial bank branches, lower capital adequacy ratios, and diminished bank liquidity reserves, posing potential financial stability risks. Even if large NPLs initially lower inflation rates, their long-term impacts depend on successful policy interventions. Additionally, NPLs significantly impact banks' return on assets and net interest margins, necessitating effective profitability and risk management strategies. The study offers crucial insights for regulators, politicians, and banking professionals to understand the complexities of risk management and competition in the banking industry and create plans to lower NPLs.

A novel framework for integrating environmental costs and carbon pricing in open-pit mine plans: Towards sustainable and green mining

As the worldwide need for mineral resources continues to grow, there is an increasing emphasis on mitigation of the environmental consequences of the mining sector, necessitating a shift towards sustainable and green mining practices. This paradigm requires the meticulous integration of environmental considerations throughout all stages of planning and operation in open-pit mining. Therefore, this research introduces an innovative integrated framework for long-term mine planning, strategically combining economic and environmental considerations in open-pit mining operations. The methodology integrates carbon pricing into the decision-making process, addressing potential financial risks and introducing two distinct scenarios: one focused on minimizing environmental costs and the other on maximizing adjusted Net Present Value (NPV). The application of this integrated framework to an open-pit iron mine in the Middle East region yielded insightful results. The integrated framework's impact on block sequencing, greenhouse gas emissions, and financial outcomes was assessed, highlighting the divergent strategies employed in the two scenarios. The minimization scenario demonstrated it was feasible to attain 90.54% of the global warming impact and 90.48% of environmental costs while achieving 96.25% of the adjusted NPV compared to the maximization scenario. Sensitivity analyses further underscored the adaptability of the framework to varying carbon pricing scenarios. This research validates a sustainable approach to mine planning, emphasizing a balance between economic returns and ecological responsibility, and provides valuable insights for decision-makers in the mining industry.

Analysing Risk and Returns: A Study on Selected Crypto Currencies

The present study focuses on "Crypto currency" also called digital money existing on the block chain. It refers to the transactions related to computer-generated currencies which are secured by crypto currencies. The data considered in the project is from 2017 to 2023. The objective of this research is to compute and compare the risk and return performance of the selected crypto. In India, crypto currency investment carries a high degree of risk due to its uncertain legal status and lack of regulation. The Reserve Bank of India has issued warnings about the potential financial, operational, legal, customer protection, and security related risks associated with dealing in crypto currencies. However, the potential for high returns has also attracted many investors. To make informed decisions, Indian investors should conduct thorough research on the market, stay up-to-date on regulatory changes, and diversify their investment portfolios. It is also essential to have a clear understanding of one's risk tolerance and not invest more than one can afford to lose. In conclusion, by taking a balanced and well-informed approach to invest in crypto currencies, Indian investors can make effective decisions that weigh both the potential risks and returns.

The procedure for implementing the compliance system in the bank

In today's conditions of globalization and increased competition in the financial sector, the introduction of a compliance system in the bank is becoming an extremely urgent task. As banking institutions are leading participants in the financial system, their stability is important for the economy of the state. The activities of banking institutions are aimed at preventing financial crimes, such as money laundering and terrorist financing, through compliance, which consists in checking customers, monitoring financial transactions and identifying suspicious transactions, preventing crises and financial failures. With the growing requirements of regulatory authorities, as well as taking into account potential financial risks and threats from unscrupulous customers or criminal groups, banks need to improve their risk management system and ensure compliance of all operations with legislation and standards. This creates the need to develop and implement an effective compliance system that ensures compliance with all legal and ethical norms in the bank's financial activities. In this context, the study and analysis of the process of implementation of the compliance system becomes an important task that requires careful research and determination of the optimal course of action to achieve successful results in ensuring internal control and compliance in the bank. The compliance system, or internal control, is defined as a set of procedures, policies, and standards that are aimed at ensuring the fulfillment of all internal and legal requirements in the bank, as well as at identifying, evaluating, and managing various risks associated with the bank's activities. In this scientific study, we will consider the process of implementing a compliance system in a bank in the context of regulatory requirements and international standards, as well as investigate the practical aspects of its implementation, including the structure of the compliance team, monitoring and reporting tools, as well as the impact of the compliance system on the bank's business strategy and reputation. Our study will emphasize the importance of properly organizing the process of implementing the compliance system to ensure customer trust and maintain the bank's stability in the modern financial environment.

Financial development, institutional quality and economic growth in countries in sub-Saharan Africa (SSA)

Over the years, there have been huge variations in the growth performances of the economies in SSA. Whilst these disparities can be attributed to a wide range of factors, it is generally understood that differences in their economic structures explain the capacity for generating growth and absorbing shocks during a recession. This study contributes to literature by examining the impact of institutional quality and financial development on economic growth in low- and middle-income countries. Data was obtained from the World Bank and covered the period from 1975 to 2020. The study employed an ARDL model to establish both short and long run dynamics in the two sub samples. The study confirmed both short and long run causality from financial and institutional quality to growth. Institutional quality enhances growth in both low- and middle-income countries in the long run. Whilst the direction of the impact is the same, the magnitude of the impact of institutional quality is different within the two categories. Institutional quality plays a more significant role in low-income countries compared to middle income economies. The study brings out potential financial fragility and systemic risks in financial institutions among countries in SSA. Policies that enhance the quality, consistency, and uniformity of legal reasoning in judicial decisions are ideal. Reducing the risk profile for investments may enhance the flow of credit to the private sector by financial institutions.

Predicting financial distress using current reports: A novel deep learning method based on user-response-guided attention

Effective financial distress prediction (FDP) can discover a company's potential financial risks and support relevant decisions in a timely manner. Previous studies on FDP have mostly focused on using financial indicators and periodic reports. Compared with periodic reports, current reports disclose major events in a timelier manner. But leveraging the information in current reports involves the critical challenges of capturing the complex semantics and measuring the importance of heterogeneous events. To this end, we propose a novel deep learning method, a user-response-guided deep attention network (URGDAN), to predict financial distress using current reports. In the proposed method, we construct a deep learning architecture to integrate financial indicators, current report texts, and user responses. URGDAN leverages the user responses to current reports to guide the semantic feature representation of the reports, it also identifies event information that has a significant correlation with company financial distress. Empirical evaluation shows that URGDAN significantly improves predictive performance and can accurately determine the importance of different current reports. Our work provides practical implications for creditors and investors.

2008 Financial Crisis: Analysis and Guidance

The 2008 financial crisis in the United States severely impacted the global economy. Amid an escalating crisis, the US government implemented a series of bailout policies, which stabilized the domestic market and contributed to global economic recovery. This paper aims to investigate the reasons for the crisis and analyze the advantages and disadvantages of the policies implemented to derive recommendations for preventing and managing financial risks. Studies have indicated that effective government intervention through regulatory policies can prevent bank failures and the deterioration of financial crises. This provides valuable insight for dealing with potential financial risks posed by the Ukraine crisis and the COVID-19 pandemic. The findings of this research highlight the importance of implementing effective regulatory measures to safeguard the financial sector's stability.

Potential Illicit Financial Flow Risks in Ghana’s Gold-for-Oil Transaction

This chapter presents multidisciplinary perspectives on a unique barter trade arrangement—the Gold-for-Oil (G4O) policy initiated by the government of Ghana in November 2022. First, it uses econometric analysis to examine the economic motivation for the policy, that oil importation is the major driver of the depreciation of the domestic currency against the US dollar. Second, it provides a political economy overview of the policy, highlighting the governance issues surrounding the policy’s formulation and implementation processes. Third, it examines existing legal and regulatory frameworks, asking if due process was followed in these processes. The econometric analysis shows that although there is a positive and statistically significant long-run relationship between Ghana’s domestic currency depreciation and oil imports, the effect size is not large (the long-run oil import elasticity of the exchange rate is about 0.20), suggesting that even under the best case scenario of policy implementation within the right legal regime, the G4O initiative will not be a panacea for the perennial exchange rate volatility problem. The political economy and legal analyses highlight issues of insufficient consultations, disregard for legal foundations that might facilitate illicit financial flows (IFFs) through smuggling and illegal gold trade, the lack of transparency in the implementation of the policy and the pricing mechanisms that could increase the risk of IFFs through mispricing, and insufficient operational clarity. To enhance the policy’s effectiveness, it would be necessary to establish a comprehensive legal framework, foster stakeholder engagement, ensure transparency, and coordinate efforts among all parties. There should also be a general focus on reducing unnecessary importations and boosting exports. All these could reduce the risk of IFFs and ensure that Ghana’s natural resources are optimally utilised for the benefit of the population.

Construction and validation of a financial risk prediction model for e-commerce enterprises oriented to financial sharing

Abstract Relying on advanced technology, the financial shared management model realizes a breakthrough for the enhancement of enterprise financial management capability, and provides effective support for enterprise decision makers while mining financial risks. In this paper, the support vector machine algorithm (SVM) is used to construct a financial risk prediction model for e-commerce enterprises, and the radial basis kernel function (RBF) is selected among various kernel functions to enhance the discriminative ability of SVM. At the same time, the particle swarm algorithm (PSO) is introduced to optimize parameter γ and penalty factor C of the RBF kernel function, and the PSO-SVM fusion model is constructed, so as to improve the prediction accuracy of the e-commerce enterprise financial risk prediction model. After the experimental analysis of this paper on the various capabilities of enterprise financial management, it is found that the operating cash flow ratio of the e-commerce enterprise is less than 40% in 2017-2021, and reaches a minimum of 3% in 2020, which indicates that the Z e-commerce enterprise has insufficient ability to raise debt to operate, which may cause problems such as a decrease in the rate of return on assets. In addition, the cash flow of the e-commerce enterprise is highly variable, and the cash outflow from operating activities is significantly higher than the cash inflow in 2020, resulting in a negative net inflow of −111,451,575,000 yuan. It indicates that the Z e-commerce enterprise has a low degree of control over cash flow, and there is a financial risk of a sudden break in the capital chain. Therefore, the risk prediction model constructed in this paper can uncover potential hidden financial dangers and risks by analyzing the financial data of e-commerce enterprises.

Construction of Financial Risk Analysis and Early Warning Model for Information Technology Enterprises Based on Complex Networks and LightGBM

Against the backdrop of the rapid rise of the information technology industry and the era of big data, enterprises are facing increasingly complex and diversified financial challenges. These challenges cover the acceleration of technological updates, the increase in financial pressure, as well as the increasingly fierce market competition and uncertainty in financial markets. These factors collectively drive a deeper and more refined demand for financial risk management. In this context, building a model that can accurately predict financial risks has become crucial. By integrating complex network theory and lightGBM algorithm, it is possible to gain a more comprehensive insight into the interrelationships and risk factors between enterprises, thereby improving the accuracy and timeliness of financial risk prediction. This model not only helps enterprises detect potential financial risks in a timely manner, but also provides effective warning and response strategies, thereby enhancing the risk control ability and operational stability of the enterprise. This study is of great significance for risk management in information technology enterprises and the entire industry, and also provides useful ideas and methods for further optimizing future financial risk prediction models.