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Financial institutions must meet international regulations to ensure not to provide services to criminals and terrorists. They also need to continuously monitor financial transactions to detect suspicious activities. Businesses have many operations that monitor and validate their customer's information against sources that either confirm their identities or disprove. Failing to detect unclean transaction(s) will result in harmful consequences on the financial institution responsible for that such as warnings or fines depending on the transaction severity level. The financial institutions use Anti-money laundering (AML) software sanctions screening and Watch-list filtering to monitor every transaction within the financial network to verify that none of the transactions can be used to do business with forbidden people. Lately, the financial industry and academia have agreed that machine learning (ML) may have a significant impact on monitoring money transaction tools to fight money laundering. Several research work and implementations have been done on Know Your Customer (KYC) systems, but there is no work on the watch-list filtering systems because of the compliance risk. Thus, we propose an innovative model to automate the process of checking blocked transactions in the watch-list filtering systems. To the best of our knowledge, this paper is the first research work on automating the watch-list filtering systems. We develop a Machine Learning - Component (ML-Component) that will be integrated with the current watch-list filtering systems. Our proposed ML-Component consists of three phases; monitoring, advising, and take action. Our model will handle a known critical issue, which is the false-positives (i.e., transactions that are blocked by a false alarm). Also, it will minimize the compliance officers' effort, and provide faster processing time. We performed several experiments using different ML algorithms (SVM, DT, and NB) and found that the SVM outperforms other algorithms. Because our dataset is nonlinear, we used the polynomial kernel and achieved higher accuracy for predicting the transactionś decision, and the correlation matrix to show the relationship between the numeric features.

We are enjoying the benefits brought by the accelerated development of computing systems and the Internet. However, we are also facing a few security and privacy vulnerabilities caused by the increasing system complexity, heterogeneity, dynamicity, and decentralized nature. These security and privacy vulnerabilities may prevent the wide adoption of information and communications technology (ICT). Therefore, trust management has become a crucial aspect of developing trustworthy systems with the preservation of security and privacy. The recent advances in blockchain technologies are bringing opportunities in fully realizing trustworthy systems. Blockchain technologies can enable anonymous and trustful transactions in decentralized and trustless environments. As a result, blockchain-enabled trust management must help to reduce system risks, mitigate financial fraud, and cut down the operational cost of computing systems. Blockchain-enabled trustworthy systems can apply to diverse areas, such as financial services, social management, the Internet of Things, and supply chain management.

Blockchain has recently attracted significant academic attention in research fields beyond the financial industry. In the Internet of Things (IoT), blockchain can be used to create a decentralized, reliable, and secure environment. The use of blockchain in IoT applications is still in its early stages, particularly at the low end of the computing spectrum. As a result, the future roadmap is hazy, and several challenges and questions must be addressed. Several articles combining blockchain technology with IoT have recently been released, but they are limited to shallow technological potential discussions, with very few providing an in- depth examination of the complexities of implementing blockchain technology for IoT. Therefore, this paper aims to coherently and comprehensively provide current cutting-edge efforts in this direction. It provides a literature review of IoT and blockchain integration by examining current research issues and trends in the applications of blockchain-related approaches and technologies within the IoT security context. We have surveyed published articles from 2017 to 2021 on blockchain-based solutions for IoT security, taking into consideration different security areas and then, we have organized the available articles according to these areas. The surveyed articles have been chronologically organized in tables for better clarity. In this paper, we try to investigate the vital issues and challenges to the integration of IoT and blockchain, and then investigate the research efforts that have been conducted so far to overcome these challenges.

Blockchain is a technology that ensures data security by verifying database of records established in a decentralized and distributed network. Blockchain-based approaches have been applied to secure data in the fields of the Internet of Things, software engineering, healthcare systems, financial services, and smart power grids. However, the security of the blockchain system is still a major concern. We took the initiative to present a systematic study which sheds light on what defensive strategies are used to secure the blockchain system effectively. Specifically, we focus on blockchain data security that aims to mitigate the two data consistency attacks: double-spend attack and selfish mining attack. We employed the systematic approach to analyze a total of 40 selected studies using the proposed taxonomy of defensive strategies: monitoring, alert forwarding, alert broadcasting, inform, detection, and conceptual research design. It presents a comparison framework for existing and future research on blockchain security. Finally, some recommendations are proposed for blockchain researchers and developers.

The rapid development of information technologies like Internet of Things, Big Data, Artificial Intelligence, Blockchain, etc., has profoundly affected people’s consumption behaviors and changed the development model of the financial industry. The financial services on Internet and IoT with new technologies has provided convenience and efficiency for consumers, but new hidden fraud risks are generated also. Fraud, arbitrage, vicious collection, etc., have caused bad effects and huge losses to the development of finance on Internet and IoT. However, as the scale of financial data continues to increase dramatically, it is more and more difficult for existing rule-based expert systems and traditional machine learning model systems to detect financial frauds from large-scale historical data. In the meantime, as the degree of specialization of financial fraud continues to increase, fraudsters can evade fraud detection by frequently changing their fraud methods. In this article, an intelligent and distributed Big Data approach for Internet financial fraud detections is proposed to implement graph embedding algorithm Node2Vec to learn and represent the topological features in the financial network graph into low-dimensional dense vectors, so as to intelligently and efficiently classify and predict the data samples of the large-scale dataset with the deep neural network. The approach is distributedly performed on the clusters of Apache Spark GraphX and Hadoop to process the large dataset in parallel. The groups of experimental results demonstrate that the proposed approach can improve the efficiency of Internet financial fraud detections with better precision rate, recall rate, F1-Score and F2-Score.

While Korean financial companies are currently providing electronic financial services by establishing the high-level information technology and security system in accordance with the Electronic Financial Supervision Regulations (EFSR), they are rarely equipped with digital forensic readiness (DFR) to maximize the capability to collect critical digital evidence (DE). So, there is a limit to identifying the root cause of financial incidents and securing admissible DE. In this paper, we present Financial Forensic Readiness as a Service in Korea (K-FFRaaS), as DFR of financial companies to acquire an admissible DE. Based on ISO/IEC 27043:2015 international standard, K-FFRaaS consists of 3 main processes groups, namely: Planning processes group, Implementation processes group, and Assessment processes group. The purpose of planning processes group is to prepare the organization to be forensically ready before potential incidents happen. The main task of implementation processes group is to carry out the processes defined in the planning processes group. The goal of assessment processes group is to evaluate whether the result of the implementation processes group is consistent with the objective of K-FFRaaS. The contribution of this research is to present that financial companies can adopt the systematic management procedure for identifying causes of incidents, storing potential DE, and presenting scientific evidence to a court of law through K-FFRaaS.

The quality and accessibility of modern financial service have been quickly and dramatically improved, which benefits from the fast development of information technology. It has also witnessed the trend for applying artificial intelligence related technology, especially machine learning to the finance and security industry ranging from face recognition to fraud detection. In particular, deep neural networks have proven to be far superior to traditional algorithms in various application scenarios of computer vision. In this paper, we propose a deep learning-based video analysis system for automated compliance audit in stock brokerage, which in general consists of five modules here: 1) Video tampering and integrity detection; 2) Objects of interest localization and association; 3) Analysis of presence and departure of personnel in a video; 4) Face image quality assessment; and 5) Signature action positioning. To the best of our knowledge, this is the first work that introduces remote automated compliance audit system for the dual-recorded video in finance and security industry. The experimental results suggest our system can identify most of the potential non-compliant videos and has greatly improved the working efficiency of the auditors and reduced human labor costs. The collected dataset in our experiment will be released with this paper.

Financial Technology (FinTech) has attracted a wide range of attention and is rapidly proliferating. As a result of its consistent growth new terms have been introduced in this domain. The term ‘FinTech’ is one such terminology. This term is used for describing various operations that are being frequently employed in the financial technology sector. These operations are usually practiced in enterprises or organizations and provide requested services by using Information Technology based applications. The term does take into account various other sensitive issues, like, security, privacy, threats, cyber-attacks, etc. This is important to note that the development of FinTech is indebted to the mutual integration of different state of the art technologies, for example, technologies related to a mobile embedded system, mobile networks, mobile cloud computing, big data, data analytics techniques, and cloud computing etc. However, this technology is facing several security and privacy issues that are much needed to be addressed in order to improve the acceptability of this new technology among its users. In an effort to secure FinTech, this article provides a comprehensive survey of FinTech by reviewing the most recent as well as anticipated financial industry privacy and security issues. It provides a comprehensive analysis of current security issues, detection mechanisms and security solutions proposed for FinTech. Finally, it discusses future challenges to ensure the security and privacy of financial technology applications.

Internet speed plays a critical role today in the business and public services arena. High speed internet has taken industry to the new heights specifically in the field of information technology, online education, financial services, entertainment, health care and many more. Currently wireless fidelity (Wi-Fi) is commonly used at homes, organizations, airports and institutions to access the high speed internet. Researchers felt that enhancing the existing speed to the next level will further unlock a plethora of opportunities for the betterment of human life. This technology can be used to enhance the defense technique by replacing it with the radio frequency mechanism and can help to have a secure transmission of data. This paper will help readers to make aware of the new technology being explored called Light Fidelity (Li-Fi). Li-Fi is the next level of technology based on wireless optical communication which uses visible light for data transmission whereas presently Wi-Fi uses radio waves.

As a residential financial platform, commercial banks need to innovate mechanisms at this time. By optimizing the composition of assets, more investment fields and investment channels will be provided to residents. In the existing network platform, strengthen customer communication, from a more professional perspective for residents financial services. In this paper, the MRS theory is introduced into IFOIS, and the MRS model is continuously expanded. This can not only enrich and perfect the theory of MRS, but also provide powerful technical support for fuzzy information processing.

The healthcare industry is a conjunction of monolithic applications based on neutral and time-stamped diagnostics. Hence, it is particularly interested in decentralised technologies to achieve global and social-driven electronic health records. Blockchain is a decentralised networking system where multiple copies of immutable data records are distributed and validated among different independent nodes. While traditionally used to create currently famous digital cryptocurrencies like Bitcoin, its worldwide fever helped to extend its applications beyond the financial industry. However, given how fast completely new technologies evolve, healthcare stakeholders are struggling to find the correct usage of blockchain ecosystem developments. Our goal is to provide a summary of the state-of-the-art efforts in the development of decentralised blockchain-based solutions for electronic healthcare records, in which social networking and wearable technology data is considered in order to provide patient-driven diagnostics - proven to be critical in the current SARS-CoV-2 pandemic. In this paper, we present a comprehensive guide on blockchain technologies competing in the healthcare market, including emerging blockchain-based initiatives and trends. Additionally, we discuss the main caveats of blockchain applications development in this industry based on practical experience.

Information and communication technologies changed the ways of provision of banking and financial services in recent decades. The educational process in these fields must reflect recent development. This paper briefly describes the course of Electronic Services in Banking, which uses methods of eLearning to provide students experience with electronic services in banking and finance. The course is also aimed at the security and safe usage of these services. In 2020, the pandemic of Covid-19 affected virtually all areas of human activity. Due to anti-pandemic measures, the course of Electronic Services in Banking took place only in the online environment. This paper further provides a comparison of the efficiency of the education process in the course in the pandemic year of 2020 and previous years. The efficiency of learning in the course was measured by the comparison of pre-course and post-course tests' results using absolute gain, relative gain, and class average normalized gain over four years' period. The impacts on the effectiveness of the education process were not detected in the pandemic year in comparison with the average of non-pandemic years. The feedback of the students on the pandemic iteration of the course is also presented.

In the 21st century, the stock is a high-risk, high-yield common financial products, in order to better analyze the stock investment, effectively and accurately predict the stock price trend has become the current research hotspot in recent years. In this paper, we put forward a LSTM prediction model to predict the closing stock prices, taking Microsoft as an example. Experimental results indicate that this method displays better prediction performance than other machine learning methods. Specifically, the RMSE value is 24.43% and 16.69% lower than ridge algorithm and neural network algorithm respectively. In addition, we make a detailed data analysis of the data set, including the stock price changes over time, the average daily return and the moving average of the stock, and give how much risk to invest in a specific stock, which can provide some guidance for our future work.

Different people from different sectors and different organizations need financing for various purposes. Financial services provide financing from various financial institutions which are divided into the capital market and money market. Timeliness of customer credit payments is very important for finance companies. Bad credit payments can make finance companies unhealthy and can even lead to bankruptcy. To be able to avoid this, a good selection of prospective customers is needed so that prospective customers who will receive credit can pay their credit on time. Assessment of the acceptance of prospective customers in receiving credit uses the 4 C principles, namely Character, Capacity, Capital, and Condition. Data mining is often used to predict an event such as a potential customer who can pay his credit on time or not. One of the algorithms that can be used in data mining to predict the timeliness of customer credit payments is a decision tree algorithm. A decision tree is a tree structure like a flowchart, in which each node has an association with other nodes. The result of this research is the creation of a model that can be used to predict prospective customers in receiving credit or not.

Fintech is an industry that uses a series of technological innovations such as cloud computing and big data to allow technology to serve finance and greatly improve financial efficiency. However, the Internet of Things technology connects all things to machines to realize information exchange. The effective integration of Fintech and AIoT will not only help improve finance and improve the efficiency of the financial industry, but also enable finance to better serve the real economy. This article aims to discuss the development status, prospects and development strategies of financial technology combined with Internet of Things technology. First, take "Industrial Bank, iFLYTEK and JD Finance jointly constructing AIoT finance" as an example to explore how financial technology companies use AIoT technologies to promote financial efficiency and service. Including the development status and development direction of two aspects of intelligent marketing and intelligent monitoring. Secondly, through situation analysis and political, economic, social, and technical analysis, explore the beneficial changes and difficulties faced by fintech companies after combining AIoT technology.

Customer churn analysis is the process of predicting customers who tend to cancel the service (subscription) they receive for various reasons, especially in sectors such as telecommunications, finance and insurance, and determining the necessary operational steps to prevent this cancellation. The study used two separate datasets from kaggle.com to identify customers who tend to unsubscribe in the telecommunications industry. The analysis process was carried out by applying machine learning methods such as Logistic Regression, K-Nearest Neighbor, Decision Trees, Random Forest, Support Vector Machines, AdaBoost, Multi-Layer Sensors and Naive Bayes methods on the relevant datasets. It was seen that the most successful method in the customer loss analysis performed on both datasets was the Random Forest method.

In the finance industry, the stock price prediction is one of the greatest notable implementations. Price forecast is a demonstration of trying to ascertain the future estimation of stock. This paper clarifies the forecast of a stock closing price utilizing Machine Learning. The dedicated and analytic or the time arrangement inspection is utilized by most of the stockbrokers to creating the stock price forecasting. The programming language is utilized to foresee the stock price prediction utilizing machine learning is Python. Machine learning itself utilizes various techniques to make estimation simpler and genuine. In this report, we recommend a Machine Learning (ML) approach that is destined to be prepared from the accessible stock's information and gain insight and afterward utilizes this gained information for a precise forecast of the stock values. In this set, this investigation utilizes a ML procedure called LSTM to envisage stock values. The objective of the model is to forecast the stock prices and finding out the instabilities in the stock value based on the last sixty days closing prices of the stock.

The trendy discussion in India these days is the creation of a cashless economy. The effective execution of demonetization has driven the economy towards digital transactions. This innovation is absolutely embraced with quick adoption of systems like IMPS, RTGS, NEFT, E-wallets, Aadhar Pay, Debit Cards and UPI. We find ourselves in a virtual wonderland, wherein an ice cream vendor accepts wallet payment with delight, a person buys a book worth Rs. 100 using a credit card, a milkman gives his Paytm number without a fuss and the vegetable dealer makes use of QR code. The modern era of digitalization has given a kick to completely new dimensions. The Prime Minister, Mr Narender Modi gave, “Make in India” and “Digital India” schemes which are new buzzwords contributing towards economic development of India. Digitalization provides answers for short term and long-term business and technological requirements. In the current scenario, elements like improving customer satisfaction, best customer experiences, faster output, big data, operational efficiencies etc. are being sought through digital technology. To digitally empower India, “Digital India campaign” was launched to promote India towards self-reliance and sustainable development. Faceless, paperless, cashless are buzzwords in Indian economy. This research paper talks about evolution of financial services to the FinTech revolution in India to the emergence of FinTech companies like Paytm, BillDesk, PhonePe, PolicyBazaar, RazorPay as unicorns.

Blockchain is one of the most popular upcoming technologies which has the potential to grow and disrupt the ongoing business model. Its scope is not only limited to finance but has great potential in the field of tourism, health care, supply chain, real estate, etc. Existing studies have explored its potential in financial industry and cryptocurrency. There is a great potential to explore the application of blockchain in other areas. This paper attempts to explain the application of blockchain in marketing.

There is a trend in Open APIs and data sharing to provide better and innovative financial services. Data regulations aim to improve consumers’ privacy and control over their data. However, it still confronts the challenges of properly managing and protecting users’ privacy in such an open data sharing paradigm. This paper proposes an automated consent management model based on blockchain technology for various coming financial services. We employ the Proof of Authority consensus mechanism to balance the business flexibility and efficiency with client privacy control. Our model enables automated, transparent, and accountable management for consumer consents in financial services. We implement a proof of concept prototype to demonstrate the feasibility and applicability of our model. Performance testing is also conducted to demonstrate the feasibility and efficiency from the performance viewpoint.

With the continuous development of information technology and the coming of big data era, it is a key measure to promote the steady development of the financial industry to build a big data information system through cloud computing technology and further promote the financial credit risk supervision mechanism. Facing the problems of credit management in the traditional financial market and the credit risk problems caused by the virtual and diversified operation characteristics of Internet finance, it is of great significance to make full use of big data information technology to explore the financial credit risk supervision paradigm. Internet finance makes use of search engines, social networks, big data and cloud computing technology to achieve a breakthrough in the current financial operation and management system, improve market efficiency and optimize the allocation of resources, but the risks it brings cannot be ignored. This paper discusses various situations existing in Internet financial supervision, and puts forward effective solutions based on cloud computing technology, hoping to minimize the risks of Internet finance.

In 2013, Internet money fund products led by Yu'e Bao were born. They are dependent on the Internet and attract a large number of customers with their unique marketing platform, convenient trading methods and high liquidity. At present, Alipay, NetEase, SUNING, Tencent, Baidu, JD.com and other companies have joined the ranks. Many Internet money funds are springing up in the market like bamboo shoots after a spring rain. However, against the backdrop of a marked decline in the global economy affected by the COVID-19 epidemic, the reference rate of return of various Internet money funds has plummeted, with Tianhong Yu'ebao ‘s 7-day annualized yield falling below 2% to 1.855% for the first time since its establishment. In this paper, 20 sample funds are tested by VaR model, and the corresponding evaluation is given. The analysis results show that the overall value at risk of Internet money funds is not high, so it is suitable to avoid risk, but the rate of return and VaR do not fully reflect the market law of “high risk, high return; low risk, low return”, indicating that there is still a lot of room for improvement in risk control of Internet money funds in China. The development of Internet money fund has a profound impact on the traditional fund industry and even the whole financial industry. based on this, it is particularly important to analyze the development status, operation mechanism and risk of Internet money fund. Empirical analysis through data statistical test and ARCH effect test can analyze the risk more effectively and help investors understand Internet money fund products so that they can invest better. First of all, compared with the Internet money fund, the risk of bank wealth management products is relatively higher. Second, the risk spillover of Internet money funds and bank wealth management products is asymmetric, and the risk spillover of Internet money funds to bank wealth management products is stronger. Third, Internet money funds of banking and fund departments have positive risk spillover effects on bank wealth management products, which means that Internet money funds of banking and fund departments can aggravate the risk impact on China's commercial banks.

Financial technology is the technical power of financial innovation. All financial institutions should constantly develop the innovation ability and practicability of financial technology, and strive to achieve the goal of deep integration of finance and technology, which subverts the traditional business model of commercial banks to a great extent. Commercial banks are the premise and foundation for maintaining the stable operation of the financial industry. With the increasingly complex situation and changes in the external environment, the rise of financial technology gradually promotes the strategic transformation of commercial banks. Commercial banks at home and abroad have continuously increased investment in financial technology. Based on matrix algorithm analysis, this paper analyzes the influence of financial technology on banking business development and the existing financial technology layout of commercial banks, and explores the future development direction of financial technology of commercial banks.

Introduction of the Exchange Traded Funds (ETFs) in 1989 in the USA added another category to the bunch of asset classes that continues to gain increasing popularity not only with the Asset Management Companies but also the potential investors who had been looking eagerly for innovative ways of diversifying their investment portfolio. Among a host of theme-based ETFs which offer varied investment strategies and returns, Bitcoin ETFs are of relatively recent origin and have caught investors’ attention as a substitute for investment in cryptocurrency directly or through derivatives. Bitcoin ETFs have appeared on the horizon designing products backed by Bitcoin and Bitcoin backed structured products like Bitcoin futures etc. At the same time, there is a growing class of unregulated Bitcoin ETF, Trusts and other financial products that track the value of Bitcoin and trade on traditional market exchanges rather than cryptocurrency exchanges. Also, there is growing another asset class of Blockchain ETFs, operating many a time as a proxy for Bitcoin/Bitcoin ETFs that invest in companies having direct or indirect exposure to Blockchain. The instant paper attempts to examine the fundamentals of Bitcoin and Blockchain ETFs, Blockchain technology, the factors throwing up Bitcoin ETFs in to prominence and the issues relating to their legal recognition by the regulators. It also attempts to provide a governance model for effective regulation of the two without mutual overlapping either in investors’ comprehension or in practice.

Artificial intelligence (AI) has recently become an integral part of the financial industry. Wide opportunities for AI are seen in tasks associated with credit scoring. However, the expected potential and benefits of AI-based credit scoring and the overall path to digitalised lending could be challenged by the fact that a relatively large number of SMEs in Europe are credit invisible due to their financing patterns, that limit the availability of credit history and other creditworthiness sensitive data needed to build and train AI models. The research aims to estimate the nature and availability of credit scoring relevant SMEs data in different European countries. Research methods respective to the above-stated aim are the following: scientific literature review, descriptive, and country to country comparative analysis of the data from the Survey on the Access to Finance of Enterprises (SAFE). The survey data indicate that the share of credit invisible SMEs changes in the range of 1030%, but there is no direct relationship with the characteristics of the country. The initiatives to score the creditworthiness of SMEs should consider the share of SMEs that do not use services of the traditional financing sector, which means that data about its creditworthiness are limited. The whole population of SMEs could be covered by AI models incorporating both conventional financial data from credit visible SMEs processed by machine learning as well as unstructured non-financial data (describing credit invisible SMEs) processed by deep learning techniques and methods. Thus, we claim that despite the huge potential of AI incorporation into creditworthiness evaluation, the use of AI is not a magic pill, enhancing easier SME’s access to finance, unless its use is aligned to the available data. Accordingly to the accessible data, different AI tools and techniques should be applied in credit scoring applications.

Paper focuses on the patterns of disruptive and sustaining innovations in the context of challenges posed to the incumbent firms by Fintech startups in the financial service sector and retail banking services in particular. It reveals the key trends of Fintech-induced disruption in the banking sector, the main catalysts behind the Fintech disruption and the areas of the retail banking sector that are more susceptible to disruptive vs. sustaining innovations. Paper also presents a discussion regarding the impact of Fintech on three core businesses of the incumbent banks – infrastructure business, customer relationship business and product innovation business. The Fintech-related disruptive innovations have greatest potential in the areas, which are outside the core competencies of banks and are difficult to assimilate into the existing architecture of the banking business. The repertoire of incumbent bank strategies for addressing the challenges of Fintech innovation is given special consideration.

Financial Exclusion has resulted in 2.5 billion of the world’s working adults excluded from formal financial services. Financial exclusion is a common phenomenon among emerging and developing countries and accounts for more than 60% of global poverty. The absence of documented income proof and lack of physical access to financial institutions are considered factors that fuel financial exclusion worldwide. The ability to understand and predict financial discipline among those that are financially excluded is of interest to financial institutions, and individuals themselves and is considered a vital part in enabling Financial Inclusion. This research proposes a methodology to address the issue of inaccessibility of financial services among individuals that do not possess the means to prove individual creditworthiness to financial service providers. The research project aims to leverage on the existing technology and communications infrastructure that has transformed how individuals use mobile phones and digital technology to fulfill daily needs. The project focuses on analyzing mobile phone interactions to derive a credit score that depicts an individual’s financial discipline (creditworthiness) that would enable access to previously denied financial services. The solution leverages on mobile interactions where an individual’s digital footprint is subject to analysis.

The recent developments of information technology have led to more automation process that enforce a fundamental reorganization of the business models of financial services value chain and highly recognized with the term ‘Financial Technology" or "FinTech" in short. The research analyzed the important factors that affect the usage behavior as well as the effectiveness towards usage behavior of FinTech 3.0 adoptions in one of the well-known private banks in Indonesia. The findings of the test showed that four out of five variables have significant value towards the hypothesis of this research. This means the likelihood of people use FinTech is mostly determined by the perceived of usefulness, ease, and availability of required resources. This information is significant to the banks to innovate their services and products that will be in accordance with customers’ preference as well in competing with other non-financial industry. With the dynamic changes and competitive advantages offered by various parties, this research extends the existing literatures by providing real insights from the users of FinTech in one of the prominent BUKU 4 (Commercial Bank Based on Business Activities) with a core capital of more than IDR 30 trillion. In Indonesia’s banking industry, the reputation of this institution in this research is more than enough to make people choose their services and gain trust to use the technology. However, this might not be the case with other banks, considering the market competitiveness between financial and non-financial institutions.

Financial technology-based institutions have grown tremendously in the last five years. Due to its strong growth, Indonesian Financial Service Authority (FSA) such as OJK, has many difficulties to supervise their operations. Studies show many foreign financial technology-based institutions operate in Indonesia and earn many benefits from Indonesian customers. They may conduct illegal operation and many of them operate beyond the knowledge of OJK as a supervision body. For this reason, the article examines the importance of developing data management service that enables to equip FSA to easily supervise all financial institutions. so that all activities in the financial services sector are where Organized regularly, fairly, transparently, and accountably, Able to realize a financial system that grows in a sustainable and stable manner and Able to protect the interests of consumers and society.

Messaging is essential when organizations dealing with financial customers need to share information. Technological innovations, such as machine learning (ML), have provided financial service institutions (FSIs) with the ability to reach out to consumers in more intelligent ways. Multi-Channel Messaging System (MCM), in use by FSI's, enables the seamless integration of disparate channels of communication within a single system. This problem has driven us to develop a machine learning-enabled channel assignment algorithm in the Multi-Channel Messaging System. The decision-making module would incorporate heterogeneous channels using an Enterprise Service Bus (ESB) layer, and use machine learning algorithms to assess channel capacity, dynamic assignment, and customer trends. Our framework would extend and maximize this approach, to achieve a fast balance between the exploration-exploitation dilemma, for channel selection. This research explores the problems and challenges a multi-channel messaging system currently used by financial services organizations, while proposing a multichannel framework which incorporates a machine learning channel selection and learning module. The analysis concentrates on the complex approach to channel selection and integration methods that can make the system effective and usable. The lessons learned from the design would be further refined to inspire future work in this field.

Cross-domain service, which integrates the services from various types of businesses in multiple administrative domains, has been proposed and instantiated along with the development of modern service industry. By converging the various services from different administrative domains, the efficiency and quality of services are promoted. As the important element of services, the user’s credit information is vital to evaluate the user’s credibility in the fields of e-commerce, financial services, etc. However, due to the heterogeneity and complexity of the cross-domain service, the issue of credit management is more complicated compared with the traditional service. This paper firstly explores the definition and representative features of credit management in the cross-domain service. Furthermore, we propose a blockchain-based credit management model with a hierarchical management committee structure, and the complete workflow including credit collection, credit sharing and credit convergence will also be presented. Finally, future challenges in credit management of the cross-domain service are illustrated and discussed.

Recently AI technologies, especially Deep Neural Network (DNN), have been widely used in the financial industry, such as stock price and movement prediction. In order to develop an AI-based solution for the global financial market, daily-based DNN model training for each stock is required to need collaboration among a large scale of entities. However, it is usually challenging due to data privacy, the cost of AI computing, and the lack of motivation to share information. This research proposes a novel Blockchain based platform, which utilizes the decentralized network, federated learning, and master-node to tackle these issues. The decentralized computing framework of federated learning, along with transfer learning, is applied to meet the data privacy requirements. Furthermore, the proposed federated learning platform with collaborative training is built on a decentralized AI computing cloud, which is highly affordable compared to centralized AI clouds. The master-node of Blockchain technology is further employed to enable the scalable global financial service, and effective rewards are applied to incentivize information sharing as well. We have applied the proposed Blockchain based platform to the stock prediction global service, which demonstrates the platform is practical and useful.

Given the actual volume and speed of financial transactions, financial fraud detection systems are constantly evolving based on new computational intelligence algorithms. Therefore, transaction monitoring and analysis prevent monetary losses caused by fraudsters. Since the fraud detection process is a labor-intensive task for human auditors given the huge amount of daily transactions processed by financial services information systems. Credit card is the financial product most explored in the financial fraud detection literature, while mobile money service is becoming a popular option for payments, fraud detection for such financial product has not yet been deeply explored. Therefore, it is interesting to optimize the auditing process and test new quantitative techniques, such as deep learning, to support human auditors before double-checking a suspicious transaction. Thus, we propose an integration of adversarial autoencoders and machine learning methods to perform an objective classification among three transaction types: regular, local, and global anomaly. The integration consists of using the autoencoder's generated latent vectors as features for the supervised learning algorithms. The experiments considered different latent vector space forms concerning their dimensionality and the clusters generated by a prior Gaussian mixture. The results show that some classifiers may accept latent characteristics well, getting better or similar performance when using all the original characteristics.

Supply chain finance can reduce the financing cost of enterprises, which is of great significance for promoting inclusive finance. However, there are many problems in the current supply chain finance, e.g., information asymmetry, inefficient financing, and high performance risk. In this paper, we propose a novel supply chain financial service platform based on blockchain technology. We first analyze the limitations of mainstream supply chain finance mode. Then, we introduce the proposed financial service platform in detail, including its components, operation process, and advantages. Toward the end, we discussed the future development trends of the platform. This paper is aimed at providing helpful guidance and reference for future research efforts.

E-AgriFinance mobile application is a digital platform that integrates activities related to farming which includes supply of input, production, marketing, distribution, and financial services. In this study, researchers focused on determining the relationship between system quality attributes and intention to use the e-AgriFinance mobile application from the perspective of suppliers. This study also examines the moderating effect of information and communication technology (ICT) readiness on the relationship between the system quality attributes and the intention of use of the mobile application by the suppliers of the agriculture sector. This study uses the TAM and ISO 25010 model, while the variable used are Perceived Usefulness, Perceived Ease of Use, Perceived Security, and Perceived Maintainability. A total of 276 questionnaires were collected in this study, and they were analysed using partial least squares structural equation modeling (PLS-SEM). Data were collected from the suppliers of the agriculture sector in Sarawak. The results of this study indicate that six of the eight research hypotheses are supported. This research contributes to the body of literature by providing empirical understanding and insights on system quality attributes of mobile application so that the agriculture sector suppliers will maximise the usage of the mobile application.

Ever since their introduction in 1989 in the USA, Exchange Traded Funds (ETF's) have gained wide popularity and Asset Management Companies (AMCs) have been using innovative nomenclature for them to entice potential investors. Among a host of theme-based ETFs which offer varied investment strategies and returns, Blockchain ETF's are of relatively recent origin and have caught investors' attention as a substitute for investment in blockchain technology directly or through listed companies. Since 2018, blockchain ETFs have appeared on the horizon designing products backed by companies investing in or developing blockchain based solutions. Parallel to this, there is a growing class of unregulated Bitcoin ETF, Trusts and other financial products that track the value of Bitcoin and trade on traditional market exchanges rather than cryptocurrency exchanges. The instant paper attempts to examine the fundamentals of blockchain technology, the factors throwing up block chain and Bitcoin ETFs in to prominence and their legal recognition by the regulators. It also attempts to study whether the blockchain ETF is just a proxy for Bitcoin/Bitcoin ETFs or a radically different product requiring fresh regulatory oversight.11The contents of this research are personal views of the authors.

Financial services are used everywhere and function with high complexity. With the increase in online transacting, frauds too are increasing alarmingly. An automated Fraud Detection System is thus required. With millions of transactions taking place, it is practically impossible to detect frauds manually with good speed and accuracy. We propose a system that provides a robust, cost effective, efficient yet accurate solution to detect frauds in both online payment transactions and credit card payments. The proposed solution is a Machine Learning model that will serve the purpose of detecting “fraudulent” and “genuine” transactions in real time. This is beneficial for all sectors that are even mildly aligned to finance. The solution will help them analyse based on various factors if the ongoing transaction can be harmful and will prevent many unfortunate incidents.

A good business environment is a basic element to attract business gathering, release investment potential, improve the credibility and influence of the entire country, and promote economic development. The business environment assessment aimed at sorting out the status quo of the business environment and discovering the weaknesses of the business environment is the prerequisite and basis for optimizing the business environment. This article takes China's 34 provincial capitals, municipalities directly under the Central Government, and single-planned cities as the research objects, sorts out the current business environment indicator system and various research conditions on the system, and builds on this basis to cover the market environment and government environment The evaluation index system of 34 important cities' business environment including eight first-level indicators including legal and policy environment, humanistic environment, human resources, financial services, public services, and innovation environment, and the use of entropy-Topsis model to analyse the data. The research results indicate that in 2017, among the 34 major cities in China, Shanghai, Beijing, and Shenzhen have the best overall business environment in the three first-tier cities, and each city has its own unique advantages.

Every day, web credibility is becoming an increasingly important. It affects how we interact with information on the internet as well as the quality of those interactions. Web credibility also spills into the real world, as misinformation from the internet can have very real consequences and catastrophic losses. It also sets an aggressive challenge of choice among different web services in the internet user community. In this paper, we investigate the existing practice and research work on evaluating web service credibility. We classify the available techniques and discuss their capabilities and impacts on user/web interactions. This work will help discover the venues for credibility measures and highlight the effective techniques in different web service domains. It will also help build more rigorous techniques to disseminate credibility measures and support internet user choices.

With the rapid development of Internet technologies such as social networking, big data, cloud computing, and search engines, with the support of Internet research, innovation and development of new technologies, the development speed of Internet finance has also reached its peak. Various institutions, including traditional financial institutions and Internet companies, have entered the Internet financial industry one after another, through the development of new businesses to achieve financial communication, payment services and information intermediary. The purpose of this article is to study the application of big data information processing technology for the Internet financial system. This article starts with the importance of the development of Internet finance and the risks it faces, and proposes the establishment of a new and complete credit investigation system for Internet finance. This article uses cloud computing and BP neural network and other technologies and algorithms to try to establish a more complete credit evaluation model. Based on various basic information of enterprises and individuals, combined with enterprise service operations and personal online behaviors, quantitative analysis and evaluation are carried out. This article puts forward countermeasures and suggestions such as establishing a protection mechanism in line with big data credit investigation, constructing a diversified basic database, improving society's awareness of big data credit investigation, promoting the open sharing of government and social information resources, and attaching importance to the use of big data supervision. Experimental research shows that it takes only about 26 seconds to calculate a sample using this model, and the efficiency is several orders of magnitude higher than the efficiency of manual review

The mobile industry is one of the rapidly growing markets in the world. This has created a unique platform for delivering mobile network-based social and financial services. Mobile banking is the best and last banking platform that provides value-added services anytime and anywhere with a reduction in effort, time, and money. However, there is a decrease in the adoption rate of these services in Arabian countries, including Saudi Arabia. Although there have been several studies to determine factors influencing the adoption of these services worldwide, few studies have been done in this part of the world. This paper considers the current literature on mobile banking to investigate the factors that increase mobile banking adoption in Saudi Arabia. We propose a new integrative model for mobile banking adoption (IMMBA) that combines and expands the IS Success and UTAUT2 models to include factors that account for Perceived Trust, Perceived Privacy, and Perceived Compatibility. IMMBA provides a comprehensive analysis of the current challenges of mobile banking adoption in Saudi Arabia. Identifying the factors that affect mobile banking adoption and understanding the relationships between them will help banks, particularly in Saudi Arabia, to lay a roadmap for the proper implementation of it following the perceptions of their customers.

Credit card fraud is an ever-growing threat to the financial industry. High dependence on internet technology leads to an increase in fraudulent transactions. Nowadays, banking sector is the most vulnerable for attacks as most of the transactions are done through credit cards and net banking. Many techniques have been proposed to tackle this problem. This paper mainly focus on recognizing fraudulent transactions by analyzing the previous set of transaction records. This research work attempts to integrate big data analytics along with machine algorithms for fast detection of large real-time data. This paper gives a brief comparison of some of the machine learning techniques using Big data.

Each bank has different clients and each client may have transactions with multiple banks. Hence, clients’ data in a single bank may be partial and incomplete. If the data can be combined, each bank obtains comprehensive information, so as to better carry out business and enhance the quality of service, such as recommending financial products and inquiring about personal credit records. However, after the promulgation of GDPR by European Union in 2018, it is illegal to directly consolidate data crossing enterprises due to privacy and security concerns, especially for privacy-sensitive industries. Emerging federated learning(FL) is very suitable for secure data sharing for distributed banks in privacy. To prevent from connection of clients’ data and the certain bank, we adopt anonymity mechanism to hide the real identity of banks. In this paper, we first propose blockchain-empowered secure federated learning for distributed banks based on multi-party computation(MPC) with multi-key fully-homomorphic encryption(FHE) scheme. Then, we give detailed description of multi-key FHE based MPC protocol, anonymity mechanism and permissioned blockchain consensus protocol. Finally, we analyze the security and compare our scheme with several existed schemes. Numerical results show that the proposed data sharing scheme has good performance in terms of computational overhead and model accuracy.

The construction of the financial module is very important in the intelligent management of the company. The collection, transmission, and processing of relevant item information in the financial module are of great significance to the construction of the intelligent financial module. The intelligent financial processing module can improve financial office efficiency and increase the security of the financial processing process. Section 404 of the Sarbanes-Oxley Act (SOX 404) has always played a decisive role in the quality and transparency of the financial modules of the Enterprise Resource Planning (ERP) of multinational corporations, due to the mandatory nature of the Securities and Exchange Commission (SEC). The purpose of this research is to design a set of intelligent ERP financial modules for multinational corporations that could comply with SOX 404, strengthen internal controls, and improve the security and convenience of the financial module. Thereby achieving intelligent management of financial modules and intelligent prevention of risks. Our solution could fortify the preventive controls over inappropriate payments to products with quality issues, reduce manual efforts for credit memo issuance and receipt. It could also fortify the preventive controls over financial risks potentially threatened by a sudden contingent liability concerning E&O issues. It could prevent potential and unexpected frauds arisen by a malicious user to meet the requirements of SOX over preventive and detective controls over the financial system. It could also ensure the consistency of journals between the preparer’s entry and review’s approval as well as improve the completeness and timeliness of financial statement assertions.

In the mobile era, more and more financial institutions are beginning to cooperate with financial technology companies to enhance services and attract customers. The emergence of 5G technology has provided new choices for financial institutions, and the impact of the 5G ecosystem on financial companies and mobile banking is self-evident. With its strong security and speed, 5G technology will greatly shorten the trading cycle and transaction delay, and the capital market will also usher in a revolution. 5G technology will bring more customers and market share to financial institutions and financial technology. In terms of financial technology innovation and application services, 5G will increase productivity and customer satisfaction. The financial services industry hopes that 5G will improve real-time mobile trading and high-frequency trading. Safety is the most critical of the financial sector, 84 percent of financial services executives pay more attention to the potential of 5G provides a more secure transactions. This paper studies financial technology to promote business innovation, reduce risks in the financial sector, and promote high-quality economic development. Relying on high-speed, large-bandwidth, low-latency mobile 5G networks, it is carried out in the backbone network technology evolution, computer room drone inspection, and cash box transportation path monitoring.