2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels

Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.

Access to Financial Services——An Analysis from the Human Rights Perspective

There is a broad connection between finance and human rights,with finance having both positive and negative impacts on human rights.Everyone has a need for access to financial services.Documents in both the international human rights and international finance fields address the relationship between financial services and human rights.Among financial services,microcredit and inclusive finance have the closest connection to human rights and potentially the greatest impact on human rights.Access to financial services promotes economic,social,and cultural rights as well as the rights of specific groups.The conditions for access to financial services to promote human rights require the state to assume obligations to recognize,respect,protect,and fulfill the need for individuals to access financial services,and to ensure the availability,accessibility,acceptability,and adaptability of basic financial services.Access to financial services has played a significant role in China’s comprehensive victory in the battle of poverty alleviation,providing valuable experience for the international community in poverty eradication,achieving sustainable development goals,and protecting and promoting human rights.

Research on the Optimization of Enterprise Financial Management under the Financial Sharing Model

With the advancement of globalization and information technology,the financial sharing mode has gradually emerged as a crucial means for enterprises to optimize their financial management.Particularly within the context of economic globalization,informatization,and digital transformation,enterprises find themselves navigating a rapidly evolving market environment by intensifying competition.To enhance efficiency and competitiveness,many enterprises have embraced the financial sharing model to streamline financial management processes,curtail costs,and bolster the execution of corporate strategies.This article aims to dissect the definition and essence of the financial sharing model and its significance in the realm of enterprise financial management.Drawing upon this analysis and aligning with the needs of enterprise financial management,the article proposes ideas for optimizing management practices,aspiring to foster reform and innovation in enterprise financial management while enhancing its level of financial management and ability to respond to financial risks.This contribution seeks to provide valuable insights for practitioners in the field.

Exploration of Financial Management Curriculum Reform in Vocational College

As the core course of finance and accounting major,the study of financial management knowledge plays a positive role in promoting students’understanding of the whole financial work and enriching financial knowledge.This paper analyzes the necessity of financial management curriculum reform,from the design ideas,teaching content,teaching mode,and teaching methods of several aspects of vocational college financial management curriculum reform work carried on the preliminary thinking,hoping to improve the teaching quality of this course will be helpful.

Innovative Strategies for Financial Investment Management Strategies of Enterprises in the New Era

With the in-depth development of economic globalization and the continuous emergence of new technologies,the importance of enterprise financial investment management strategy is becoming more and more prominent.The traditional financial investment management strategy can no longer meet the needs of enterprises in the new era and needs to be innovated with the times.For enterprises,the effectiveness and scientificity of financial investment management strategy will directly affect the competitiveness and long-term development of enterprises.Therefore,this study chooses the innovation based on the financial investment management strategy of enterprises in the new era as the research theme,aiming at exploring the innovative strategy to adapt to the needs of enterprises in the new era.

Financial Calculation Problems and Countermeasure Analysis of Large-Scale Engineering Construction Projects

The financial aspects of large-scale engineering construction projects profoundly influence their success.Strengthening cost control and establishing a scientific financial evaluation system can enhance the project’s economic benefits,minimize unnecessary costs,and provide decision-makers with a robust financial foundation.Additionally,implementing an effective cash flow control mechanism and conducting a comprehensive assessment of potential project risks can ensure financial stability and mitigate the risk of fund shortages.Developing a practical and feasible fundraising plan,along with stringent fund management practices,can prevent fund wastage and optimize fund utilization efficiency.These measures not only facilitate smooth project progression and improve project management efficiency but also enhance the project’s economic and social outcomes.

Feature Selection with Optimal Variational Auto Encoder for Financial Crisis Prediction

Financial crisis prediction(FCP)received significant attention in the financial sector for decision-making.Proper forecasting of the number of firms possible to fail is important to determine the growth index and strength of a nation’s economy.Conventionally,numerous approaches have been developed in the design of accurate FCP processes.At the same time,classifier efficacy and predictive accuracy are inadequate for real-time applications.In addition,several established techniques carry out well to any of the specific datasets but are not adjustable to distinct datasets.Thus,there is a necessity for developing an effectual prediction technique for optimum classifier performance and adjustable to various datasets.This paper presents a novel multi-vs.optimization(MVO)based feature selection(FS)with an optimal variational auto encoder(OVAE)model for FCP.The proposed multi-vs.optimization based feature selection with optimal variational auto encoder(MVOFS-OVAE)model mainly aims to accomplish forecasting the financial crisis.For achieving this,the proposed MVOFS-OVAE model primarily pre-processes the financial data using min-max normalization.In addition,the MVOFS-OVAE model designs a feature subset selection process using the MVOFS approach.Followed by,the variational auto encoder(VAE)model is applied for the categorization of financial data into financial crisis or non-financial crisis.Finally,the differential evolution(DE)algorithm is utilized for the parameter tuning of the VAE model.A series of simulations on the benchmark dataset reported the betterment of the MVOFS-OVAE approach over the recent state of art approaches.

Exploring the moderating role of financial development in environmental Kuznets curve for South Africa:fresh evidence from the novel dynamic ARDL simulations approach

The extant literature has produced mixed evidence on the relationship between finan-cial development and ecological sustainability.This work addresses this conundrum by investigating financial development’s direct and indirect consequences on ecologi-cal quality utilizing the environmental Kuznets curve(EKC)methodological approach.Our empirical analysis is based on the novel dynamic autoregressive distributed lag simulations approach for South Africa between 1960 and 2020.The results,which used five distinct financial development measures,demonstrate that financial develop-ment boosts ecological integrity and environmental sustainability over the long and short terms.In the instance of South Africa,we additionally confirm the validity of the EKC theory.More importantly,the outcomes of the indirect channels demonstrate that financial development increases energy usage’s role in causing pollution while attenuating the detrimental impacts of economic growth,trade openness,and foreign direct investment on ecological quality.Moreover,the presence of an inadequate financial system is a requirement for the basis of the pollution haven hypothesis(PHH),which we examine using trade openness and foreign direct investment variables.PHH for both of these variables disappears when financial development crosses specified thresholds.Finally,industrial value addition destroys ecological quality while tech-nological innovation enhances it.This research provides some crucial policy recom-mendations and fresh perspectives for South Africa as it develops national initiatives to support ecological sustainability and reach its net zero emissions goal.

Social–financial approach for analyzing financial transitions

This study proposes a social-financial approach(SFA)to fill the methodological research gap in strategic policy design for managing financial transitions during social changes.The SFA seeks to characterize inclusive transitions in response to innovation and analyze financial management in social changes.Using a multilevel perspective,we combine evolutionary finance and inclusive growth analytics into this framework.We contend that the interaction between the different levels can be summarized as spontaneous adjustments and the alignment of financial elements with the indicators.Actors who attempt to achieve their goals based on past performance evaluations and other forms of bounded rationality strive to cope with adjustments and further trigger a reorientation of the existing regime.We also developed a new configuration tool called the three-axis description to describe the evolution of financial transitions at different stages.These methods allow us to analyze the evolution of financial transition and efficiency,and we argue that market efficiency evolves in stages with the finan-cial transition.Finally,to demonstrate the capability of SFA to identify diverse financial transition pathways,we examined an example case:the establishment of the Bretton Woods System.

Editorial to special issue“Hidden market linkages between Bitcoin,cryptocurrencies and financial markets:Evidence from high‑frequency data and higher‑order moments”in financial innovation

The cryptomarket has evolved into a complex system of different types of cryptoassets,each playing an important role within the system.With specific features,opportunities,and risks.Studying their apparent and hidden linkages and general connectedness not only inside the system but also the linkages to the outer markets,being it either the traditional financial markets or the macroeconomic and monetary indicators and variables,plays a crucial role in understanding the market,managing risks,and aiming for profitable opportunities.The cryptomarkets are far from being simply Bitcoin or even just the most popular and capitalised cryptocurrencies and tokens which might have been the case just a few years back.

Gazing through the bubble:an experimental investigation into financial risk‑taking using eye‑tracking

Eye tracking can facilitate understanding irrational decision-making in contexts such as financial risk-taking.For this purpose,we develop an experimental framework in which participants trade a risky asset in a simulated bubble market to maximize individual returns while their eye movements are recorded.Returns are sensitive to eye movement dynamics,depending on the presented visual stimuli.Using eye-tracking data,we investigated the effects of arousal,attention,and disengagement on individual payoffs using linear and nonlinear approaches.By estimating a nonlinear model using attention as a threshold variable,our results suggest that arousal positively influences trading returns,but its effect becomes smaller when attention exceeds a certain threshold,whereas disengagement has a higher negative impact on reduced attention levels and becomes almost irrelevant when attention increases.Hence,we provide a neurobehavioral metric as a function of attention that predicts financial gains in boomand-bust scenarios.This study serves as a proof-of-concept for developing future psychometric measures to enhance decision-making.

Financial literacy,behavioral traits,and ePayment adoption and usage in Japan

This study investigates how financial literacy and behavioral traits affect the adoption of electronic payment(ePayment)services in Japan.We construct a financial literacy index using a representative sample of 25,000 individuals from the Bank of Japan’s 2019 Financial Literacy Survey.We then analyze the relationship between this index and the extensive and intensive usage of two types of payment services:electronic money(e-money)and mobile payment apps.Using an instrumental variable approach,we find that higher financial literacy is positively associated with a higher likelihood of adopting ePayment services.The empirical results suggest that individuals with higher financial literacy use payment services more frequently.We also find that risk-averse people are less likely to adopt and use ePayment services,whereas people with herd behavior tend to adopt and use ePayment services more.Our empirical results also suggest that the effects of financial literacy on the adoption and use of ePayment differ among people with different behavioral traits.

Spatial-temporal Evolution and Influencing Factors of Digital Financial Inclusion: County-level Evidence from China

The vigorous development of information and communications technology has accelerated reshaping of the financial industry. The COVID-19 pandemic has further catalyzed the demand for digital financial services. Digital financial inclusion relies on information technology to overcome spatial limitations. In this case, the research question is whether it adheres to the spatial laws governing conventional financial activities. This study uses exploratory spatial data analysis and a geographical detector to elucidate the spatiotemporal characteristics and factors influencing digital financial inclusion at the county level in China(Data don’t include that of Hong Kong, Macao and Taiwan of China) from 2014 to 2020. The research findings indicate: first, China’s county-level digital financial inclusion is generally increasing and exhibits significant spatial autocorrelation. Second, population density, level of traditional financial development, government regulation, and education level are key determinants of China’s county-level digital financial inclusion. Third,policies should be differentiated by region to narrow the spatial gap in digital financial inclusion. The results provide a reference for other developing countries on using digital technology to develop financial inclusion.

Analysis of financial development and open innovation oriented fintech potential for emerging economies using an integrated decision‑making approach of MF‑X‑DMA and golden cut bipolar q‑ROFSs

The purpose of the paper is to identify the factors of financial development that have the greatest impact on open innovation in 7 emerging countries.The analysis was performed featuring the MF-X-DMA method,as well as its further verification for auto-correlation and heteroscedasticity.The time period covers years from 2002 to 2020.The article states that the main indicators to improve financial development should enhance the process of bank lending and equity market development.An important area is the development of competition by providing equal access to information to all market participants in a continuously refining technical infrastructure.Regression analysis with the MF-X-DMA method confirms the statistical significance of this influ-ence.The article fills the knowledge gap into the link between open innovations and the relatively low capitalization of the modern emerging countries’financial market,low liquidity in small cap stocks at the financial market and concentration of the banking sector,as well as risks arising in the process of globalization.Another analysis has also been conducted by generating a novel fuzzy decision-making model.In the first stage,the determinants of open innovation-based fintech potential are weighted for the emerging economies.For this purpose,M-SWARA methodology is taken into consideration based on bipolar q-ROFSs and golden cut.The second stage of the analysis includes evaluating the emerging economies with the determinants of open innovation-based fintech potential.In this context,emerging seven countries are examined with ELECTRE methodology.It found the most significant factor is the open innovation-based fintech potential.

A comparison of financial and operational efficiency between smart and traditional insufflation for laparoscopic surgery: A granular analysis

Objective:Smart insufflation(SI)techniques relying on valve and membrane-free insufflation are increasing in usage.Although considerable literature exists demonstrating the benefits of SI on procedural ease and patient outcomes,there remains a paucity describing the financial impact of these devices.The purpose of this study was to determine the financial and efficiency impact of these devices on the operating room and inpatient wards of a hospital.Methods:A discrete event simulation model representing a typical mid-sized North American hospital comparing SI to traditional insufflation(TI)was generated.The National Surgical Quality Improvement database from 2015 to 2019 was used to populate the model with data supplemented from the literature.Outcomes included length of stay(LOS),duration of surgery(DOS),annual procedure volume,profit,return on investment(ROI),and gross profit margin(GPM).From the literature review,DOS savings were 10e32 minutes/case,while LOS savings were 0e3 days/case.Results:Implementation of an SI led to an increase in annual throughput of 42e346(4.4%e36.6%)cases for all procedures and 38 to 297(4.3%e33.3%)cases for complex procedures.LOS was found to be decreased by 175e614(18.3%e64.2%)days for all procedures and 231 to 614(35.6%e77.9%)cases for complex procedures with the implementation of an SI.Together,this resulted in an increase in net profit of$104,685 per annum.The ROI of SI over the TI device was>1000%,and the GPM for the TI was 90.0%,while the GPM for the SI was 71.7%.Conclusion:Despite the initial financial investment being greater,the implementation of SI offsets these expenses and yields significant financial benefits.Our study demonstrates the financial benefits of SI over TI and illustrates how granular operational and financial analyses of technologies are essential to aid in sound healthcare procurement decision making.

The role of natural resources in financial expansion:evidence from Central Asia

Central Asian States(CAS)have diverse natural resources.This research aims to shed light on the finance–natural resource’s association in the context of CAS namely Kazakhstan,Kyrgyzstan,Tajikistan,Turkmenistan,and Uzbekistan in 1996–2020 using the cross-sectionally augmented autoregressive distributed lag technique.It seeks to examine the research question,“What is the effect of natural resource wealth on the financial expansion(FE)of Central Asia?”The results demonstrated an inverted U-shaped association between financial growth and natural resource rents.It implies that natural resources are initially a“resource blessing”that later becomes a“resource curse.”Additionally,the effect of institutional quality(INQ)and human development(HD)on financial growth is examined.The results reveal that INQ and HD positively affect FE.Moreover,a bidirectional causal relationship exists between FE and INQ.Finally,all variables contribute to a long term FE.Based on these outcomes,the major policy recommendations are that the CAS authorities diversify their financial services and products and direct the proceeds from natural resource rents to effective invest-ments particularly in HD.In addition,the social and political infrastructures in CAS must be restructured to achieve a high-quality institutional environment,which is necessary to increase the role of the private sector.

Effects of financial development and capital accumulation on labor productivity in sub‑Saharan Africa:new insight from cross sectional autoregressive lag approach

This study aims to shed light on the effects of financial development and accumula-tion of capital on the productivity of labor in the sub-Sahara African region within the period of 1990–2018.In this work,we used the(dynamic)common correlated effects estimator-mean group and additional techniques such as cross-section autoregressive distributed lag to calibrate the sample into the African subregion to ensure robustness.The findings reveal that financial progress in the region over time leads to an increase in productivity of labor and also the accumulation of capital.Furthermore,financial markets have a progressive impact on the productivity of labor within sub-Saharan African regions.We extend the very limited literature on the nexus between financial development and labor productivity by incorporating capital accumulation into our model which has not been previously studied.

Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial products.The stockholders focused on the prediction of return rate and risk rate of financial products.Therefore,an automatic return rate bitcoin prediction model becomes essential for BC financial products.The newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive method.This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial products.The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products.Besides,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results.The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates.The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects.The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

Reduced differential transform and Sumudu transform methods for solving fractional financial models of awareness

In that paper,we new study has been carried out on previous studies of one of the most important mathematical models that describe the global economic movement,and that is described as a non-linear fractional financial model of awareness,where the studies are represented at the steps following:One:The schematic of the model is suggested.Two:The disease-free equilibrium point(DFE)and the stability of the equilibrium point are discussed.Three:The stability of the model is fulfilled by drawing the Lyapunov exponents and Poincare map.Fourth:The existence of uniformly stable solutions have discussed.Five:The Caputo is described as the fractional derivative.Six:Fractional optimal control for NFFMA is discussed by clarifying the fractional optimal control through drawing before and after control.Seven:Reduced differential transform method(RDTM)and Sumudu Decomposition Method(SDM)are used to take the resolution of an NFFMA.Finally,we display that SDM and RDTM are highly identical.