Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract In the current environment, where the Covid-19 pandemic has exposed the vulnerabilities of the incumbent paper-based trade and supply chain finance systems, digital transformation pledges to alleviate the friction on international trade. Here, we provide a timely review of state-of-the-art industry applications and theoretical perspectives on the use of blockchain as the medium toward digitalisation for supply chain finance systems. We argue that blockchain technology has an innovation promoting role in supply chain finance solutions through reducing inefficiencies and increasing visibility between different parties, which have hitherto constituted the main challenges in this sphere. Based on a review of the academic literature as well as an analysis of the industrial solutions that have emerged, we identify and discuss the financial, operational and legal challenges encountered in supply chain financing and the promise of blockchain to address these limitations. We discuss the bottlenecks as well as the benefits of blockchain and identify some necessary conditions required for the emergence of blockchain-enabled trade and supply chain financing, such as the establishment of co-opetition among supply chain actors, integration with IoT systems for data quality, and reform of regulatory and legal frameworks. We conclude by identifying promising research directions about the implementation process, inviting further research into the transformation of business models toward a more collaborative nature. PubDate: 2022-05-09
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Decentralized Finance is an alternative form of finance that has no central authority to control or monitor transactions. Decentralized finance can potentially replace traditional elements of a centralized financial system with smart contracts, a set of self-executing code, enabled through a blockchain. The motive behind such a decentralized mechanism is that it creates a trust-free, transparent, and open-source ecosystem where the transactions can be carried out, eliminating intermediaries. Decentralized Finance has gained increased awareness in late 2020, led by platforms such as Uniswap, Maker DAO, Aave, and Curve. The aggregated market capitalization of Decentralized Finance has crossed USD $87B of total value locked as of May 9, 2021. From stablecoins to tokenized Bitcoin, from money lending to borrowing platforms, decentralized finance has grown exponentially. Decentralized Finance has many advantages over the centralized financial system, but it also suffers from many issues such as slow transactions, and unpredictable, sometimes high transaction costs. This has led to the introduction of Hybrid Finance, which is operated by verified entities over a public blockchain system that is proposed to have high transaction speeds, low transaction costs, and Know Your Customer compliance. This case study outlines the details of the collaboration between Acala, a decentralized finance platform built on the Polkadot blockchain, and Current, a New York-based challenger bank that focuses on mobile payments, online banking, and financial services, as they established a new category in the financial technology sector with Hybrid Finance. The major objective of this study is to understand and appreciate the relevance of blockchain technology for enabling a hybrid finance ecosystem that is trust-free and completely decentralized with no intermediaries. PubDate: 2022-04-28
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The expansion of information technology in everyday reality through the spread of social networks and mobile devices, emerging technologies -such as cloud services and the internet of things, has led to increased vulnerability for individuals and businesses. Individuals can suffer fraud, identity theft, embarrassment and distress when critical information (such as financial or sensitive personal data) is compromised or even publicly revealed, as a result of a cybercrime. The increase of cyber-risks impacts both individuals and entities, such as businesses and governments and renders cyber-insurance -on top of other cyber security means- more necessary with the passage of time. In this paper, it is assumed that multiple cyber-incidents are possible during the (digital) life of an individual. They resemble to illness that can affect the physical life of an individual. Illness as opposed to fatality—which has been used in research so far- can occur several times within the lifetime of an individual and the same can happen during his or her digital life, as he or she may suffer several cyber-attacks (digital illnesses) and yet digitally survive. This study mimics physical illness insurance-based actuarial pricing techniques to evaluate the cost for offering financial protection against multiple cyber-attacks, in a way similar to that of pricing health insurance. Consequently, this approach further advances the research on cyber insurance valuation and development. It can be a valuable pricing tool for the interested parties and targeted audience, as is helps estimate the residual risk left after technological cyber-security safety-nets have been used. PubDate: 2022-04-27
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract A spaceship steers by changing its existing orbit or trajectory. The current regulatory framework for financial institutions, albeit essential, is inefficient, thus steering the regulatory spaceship out of orbit. Regtech is an ever-evolving innovation which may have the potential to become a catalyst in financial services industry regulation. It provides automated solutions for monitoring, reporting and analysing regulatory requirements, thereby significantly reducing costs and improving productivity. This paper, however, argues that Regtech currently lacks the envisaged effective and widespread adoption because of risks, ambiguities, costs and difficulties associated with its application. The future of Regtech, therefore, remains uncertain. Yet through careful modification and implementation of recommendation initiatives, Regtech will be able to transform the way in which regulators and financial institutions observe and implement the current regulatory landscape, steering the regulatory spaceship in the right direction. PubDate: 2022-02-25 DOI: 10.1007/s42786-022-00038-9
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The purpose of this paper is to identify research that has been carried out about cryptocurrency regulation contributions and the current challenges that need to be addressed in future studies. The methodology used to conduct this research and report the findings was systematic mapping. We use this methodology to search, identify, and select all relevant primary studies on cryptocurrency regulation. The findings reveal that the key cryptocurrency regulation research topics are distributed governance, central bank digital currency, monetary policy, cryptocurrency adoption, security, regulation, cryptocurrency market, cybercrime economy and money laundering. The research proposals for cryptocurrency regulation comprise tools, protocols, methods, models, frameworks and, knowledge. The cryptocurrency regulatory challenges are cryptocurrency adoption, central bank digital currency regulation, accounting for cryptocurrencies and risk for cryptocurrencies. This systematic mapping provides an overview of the solutions proposed to regulate cryptocurrency as well as the current research challenges. PubDate: 2022-01-11 DOI: 10.1007/s42786-021-00037-2
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Customer credential verification is an ongoing activity at financial institutions. Know Your Customer is one such periodic verification activity. Often, organizations store the collected customer credentials on centralized storage platforms (e.g., cloud storage and central servers) which could result in major privacy breaches. In addition, when a customer has accounts at multiple institutions, this process is repeated at each of the institutions, resulting in wasted resources and inconvenience to the customer. In this paper, we describe Casper, a blockchain and self-sovereign identity-based digital identity platform, to address these issues. Unlike traditional identity systems, here the actual identity credentials of customers are stored on their own mobile wallet applications. The system only stores the proofs of the credentials on its blockchain-based decentralized storage system. Casper employs Zero-Knowledge Proof mechanisms to verify the identity information from the credential proofs. As a proof of concept, we have employed Casper in a banking environment. Preliminary evaluation studies show the system to be scalable and being capable of yielding high transaction throughput. PubDate: 2021-12-01 DOI: 10.1007/s42786-021-00036-3
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Finance is the backbone of any Organization. Government is no different. The financial health of the Government is critically dependent on the efficiency with which its banking needs are managed. The Central Bank is generally the banker to the Governments. In India, Reserve Bank of India and Government have been in the forefront of implementing various reforms to strengthen and streamline the processes involved in tax collections and transfer payments. Despite these reforms, some issues still persist and are having detrimental effect on overall efficiency of Government banking. One important factor for existence of these issues is the fact that every government transaction has to pass through a long chain of stakeholders before it is accounted with finality. Blockchain technology is an emerging technology that promises to facilitate speedy and efficient collaboration among all the stakeholders and also facilitates disintermediation to the extent possible. Further, during the crisis like situations (floods, earthquakes or COVID 19 like pandemics), immediate relief is of utmost importance and government aid should reach the vulnerable/affected citizens at a very high speed. However, it is during these stress times that physical access to banking channel gets restricted. Blockchain can facilitate decentralization of last mile delivery channel, by enabling peer-to-peer and cashless transactions even among the unbanked population. This paper identifies the current issues in Government banking ecosystem, analyses the root causes behind such issues, introduces Blockchain based distributed ledger technology, examines the extent to which this emerging technology can address such issues and describes the way forward. PubDate: 2021-09-03 DOI: 10.1007/s42786-021-00035-4
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Notwithstanding the increasing importance of technology investment for the banking sector, evidence regarding its impact has been limited, especially from the standpoint of an emerging economy. To address this shortcoming, using an extended dataset on technology spending for India, we analyze its impact on bank performance and stability for the period 1992–2018. The findings indicate that technology exerts a negative impact on profitability and relatedly, it lowers stability as well. These findings resonate primarily for state-owned banks, although for other bank groups, the overall impact is generally observed to be positive. We also examine the impact of key phases in the technological journey of banks and the channels through which it affects bank behavior. The evidence supports the fact that demonetization exerted a significant adverse impact on state-owned banks. As well, weaknesses in asset quality and cost efficiencies are two key drivers that affect bank behavior. PubDate: 2021-08-31 DOI: 10.1007/s42786-021-00034-5
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Visual representation of a many-objective Pareto-optimal front in a high-dimensional (four or more) objective space requires a large number of data points. Choosing a single point from a large number of data points even with preference information is problematic, as it causes a large cognitive burden on the part of the decision-makers. Therefore, many-objective optimization and analytics practitioners have been interested in practical visualization methods that enable them to filter down a large set of data points to a few critical points for further analysis. Most existing visualization methods are borrowed from other data analytics domain and they are too generic to be effective for many-criteria decision making. In this paper, we propose a visualization method, following an earlier concept, using star-coordinate plots for effectively visualizing many-objective trade-off solutions (data points). We demonstrate the use of the proposed method to a couple of high-dimensional test problems and a 4-objective portfolio optimization problem. We also show a case of interactive exploratory data analytics where we use the ‘Pareto Race’ technique from the multi-criteria decision analysis (MCDA) literature to demonstrate the ease and advantage of the proposed visualization method. PubDate: 2021-07-26 DOI: 10.1007/s42786-021-00031-8
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Unpredictable nature of financial derivatives market has always been a challenge for investors to profit from these markets. It is known that computing volatility of underlying assets is a source of this challenge. In this work, we present an application of our data-driven neuro arch (DDNA) volatility model that is driven by the market data of the stock prices to price options. That is, we use the volatility forecast from our DDNA model along with the Monte Carlo (MC) simulations to compute option prices. Since the MC method requires a large number of simulations for better precision, we implement the proposed model on two cloud resources (Amazon’s EMR and Google’s Cloud DataProc) using the Hadoop MapReduce paradigm. Also, since the MC strategy is prone to errors due to uncertainties and random numbers, we propose to generate a fuzzified range of option prices instead of a single crisp option value to minimize these errors. Our experimental configuration consists of c3.xlarge instances and n1-standard-4 instances, both having 4 vCores, for EMR and GDP respectively. For a largest number of 10 million simulations on 40 VMs, the option pricing results (computed in 39 and 33 s respectively on EMR and GDR) are close to the last traded option value found on option chain tables with an error of 0.00101 (0.1%). The proposed DDNA model for forecasting volatility together with MC option pricing model implemented on MapReduce outperforms the existing option pricing models in terms of efficiency and accuracy. This proposed strategy could be used by investors for computing option prices precisely with relative ease, allowing them to value the numerous available option contracts for their investment decisions. PubDate: 2021-07-19 DOI: 10.1007/s42786-021-00032-7
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: A correction to this paper has been published: https://doi.org/10.1007/s42786-021-00029-2 PubDate: 2021-06-01 DOI: 10.1007/s42786-021-00029-2
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Robotic process automation (RPA) is a software robot technology designed to execute rules-based business processes by mimicking human interactions across multiple applications. As a virtual workforce, this software application has proven valuable to organizations looking to automate repetitive, low-added-value work. The combination of RPA and Artificial Intelligence (AI) is called CRPA (Cognitive Robotic Process Automation) or IPA (Intelligent Process Automation) and has led to the next generation of RPA bots. It has been transforming the banking industry by making the core financial operations exponentially more efficient and allowing banks to tailor services to customers while at the same time improving safety and security. Although intelligent automation is enabling banks to redefine how they work, it has also raised challenges regarding protection of both consumer interests and the stability of the financial system. This article presents a case study on Deutsche Bank’s successful implementation of intelligent automation and also discusses the ethical responsibilities and challenges related to automation and employment. We demonstrate how Deutsche Bank successfully automated Adverse Media Screening (AMS), accelerating compliance, increasing adverse media search coverage and drastically reducing false positives. This research contributes to the academic literature on the topic of banking intelligent automation and provides insight into implementation and development. PubDate: 2021-06-01 DOI: 10.1007/s42786-021-00030-9
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract In this study, we present a metric of consensus for Likert-type scales. The statistic provides the level of agreement for any given number of response options as the percentage of consensus among respondents. With this aim, we use a geometric framework that allows us to analytically derive a positional indicator. The statistic is obtained as the relative weight of the distance from the point containing the proportions of observations that fall in each category to the centre of a regular polygon with as many vertices as categories, which corresponds to the point of maximum dissent. The polygon can be regarded as the area that encompasses all possible answering combinations. In order to assess the performance of the proposed metric of consensus, we conduct an iterated forecasting experiment to test whether the inclusion of the degree of agreement in households’ expectations improves out-of-sample forecast accuracy of the unemployment rate in seven European countries and the Euro Area. We find evidence that the level of consensus among households contains useful information to predict unemployment rates in all cases. This result shows the potential of agreement metrics to track the evolution of economic variables. Finally, we design a simulation experiment in which we compare the sampling distribution of the proposed metric for three- and five-response alternatives, finding that the distribution of the former shows a higher level of granularity and dispersion. PubDate: 2021-06-01 DOI: 10.1007/s42786-021-00026-5
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract In early 2018 Bitcoin prices peaked at US$ 20,000 and, almost two years later, we still continue debating if cryptocurrencies can actually become a currency for the everyday life or not. From the economic point of view, and playing in the field of behavioral finance, this paper analyses the relation between Bitcoin price and the search interest on Bitcoin since 2014. We questioned the forecasting ability of Google Bitcoin Trends for the behavior of Bitcoin price by performing linear and nonlinear dependency tests, and exploring performance of ARIMA and Neural Network models enhanced with this social sentiment indicator. Our analyses and models are founded upon a set of statistical properties common to financial returns that we establish for Bitcoin, Ethereum, Ripple and Litecoin. PubDate: 2021-06-01 DOI: 10.1007/s42786-021-00027-4
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Propelled by recent policy initiatives and technological developments, India’s digital payment system is a promising success story in the making. At the same time, the data also points towards an increasing usage of cash. While aggregate country-level data can indicate overall preferences of citizens, we use a novel online survey-based dataset to understand how factors such as ‘perception’ and ‘trust’ in digital payments, and experience with online frauds, affect the payment behaviour of consumers. While demographic factors like age, gender and income are relevant factors which determine this choice, we find compelling evidence that a person’s usage of digital payment methods is influenced by her perception of these instruments, as well as her trust in the overall payments framework and banking system in general. We find that the degree to which past-experience with online fraud deters usage of digital payments varies with the purpose of the transaction. PubDate: 2021-06-01 DOI: 10.1007/s42786-020-00024-z
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The project constructs a stock selection model by machine learning methods to enhance the performance of the benchmark index for individual investors. Stock returns prediction is a highly researched topic. However, it is a difficult problem because the stock prices are complex, non-linear, and chaotic. Moreover, overfitting is always an important issue in machine learning field. In this article, it shows that how to solve these problems by dealing with time series data, feature engineering, and model construction. We apply the stock selection model on S&P 500 index and FTSE 100 index. The result shows that the portfolios with stock selection model outperform the benchmarks, and 2% of the number of constitution stocks is the best choice for the stock selection model. Besides, feature importance analysis shows that the stock selection model can measure import features appropriately, which means it has the ability to adapt to different economic environments. In addition, the portfolios with fewer stocks usually outperform the portfolios with more stocks shows the good prediction of the stock selection model. The results imply that machine learning techniques have a good application in stock markets. PubDate: 2021-06-01 DOI: 10.1007/s42786-021-00025-6
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Despite the limitations peculiar to Nigerian micro-finance banks in deposit-mobilization initiatives, some banks have managed to deploy technologies to mitigate these constraints. This paper identifies the capabilities responsible for enabling the acquisition and deployment of technologies for deposit mobilization activities with a view to inform policy and management practice. Using data collected from questionnaire administration on the managers of 100 selected micro-finance banks, we identify the technologies deployed; measure investment, operational, marketing and linkage capabilities in the sector using a five-item scale (1 = none and 5 = very high) and examine the technological capabilities that influence the deployment of the technologies. We found out that 28.2% of the banks used Point-of-sale (POS) machines, 10.3% deployed mobile banking technologies, while 33.3% and 20.5% used mobile money and card system technologies respectively for deposit mobilisation activities. The study revealed that technological capabilities in the sector ranged from moderate to high. Our regression model showed that capabilities to penetrate markets, procure and install equipment, negotiate and recruit skilled personnel were 21, 15.24, 5.75 and 0.03 times respectively more likely to influence the deployment of the POS. In addition, ability to link external agents to troubleshoot, test and train staff was 0.08 times more likely to influence the deployment of the POS machine. Capability to assess, procure and install technologies was 0.13 times more likely to influence the deployment of mobile money technologies. The study recommended training through professional bodies and regulatory and knowledge institutions to upgrade technological capabilities in the sector PubDate: 2021-06-01 DOI: 10.1007/s42786-021-00028-3
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The FinTech or ‘financial technology’ revolution has been gaining increasing interest as technologies are fundamentally changing the business of financial services. Consequently, financial technology is playing an increasingly important role in providing relative performance growth to firms. It is also well known that such relative performance can be observed through pairs trading investment. Therefore pairs trading have implications for understanding financial technology performance, yet the relationships between relative firm value and financial technology are not well understood. In this paper we investigate the impact of financial technology upon relative firm value in the banking sector. Firstly, using pairs trade data we show that financial technologies reveal differences in relative operational performance of firms, providing insight on the value of financial technologies. Secondly, we find that contribution of relative firm value growth from financial technologies is dependent on the specific business characteristics of the technology, such as the business application and activity type. Finally, we show that financial technologies impact the operational risk of firms and so firms need to take into account both the value and risk benefits in implementing new technological innovations. This paper will be of interest to academics and industry professionals. PubDate: 2020-10-01 DOI: 10.1007/s42786-020-00023-0
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract This paper describes an expert RFID biometric enabled dual system architecture which acts as a smart and digitized banking vault door locking system. The proposed system is novel, multipurpose and provides three levels of security. It is based on locker automation using RFID technology and software Android App technology that allows access only to the authorized owner of the bank vault to open it. Despite the legacy key system, the overall control of electronic smart door lock system utilizes the RFID tags via NFC, biometric traits via web portal and mobiles via Bluetooth as the procedure for authentication. Our proposed RFID technology-based access control authentication mechanism utilizes low cost irrevertible functions to control the vulnerable security attacks. Furthermore, the proposed lock system has a tag’s identification search complexity of only \({\mathcal {O}}(1)\) . PubDate: 2020-10-01 DOI: 10.1007/s42786-020-00022-1
Hybrid journal (It can contain Open Access articles)
[2469 journals]
