Behavioral biometric based personal authentication in feature phones

<p><span>The usage of mobile phones has increased multifold in the recent decades mostly because of its utility in most of the aspects of daily life, such as communications, entertainment, and financial transactions. Feature phones are generally the keyboard based or lower version of touch based mobile phones, mostly targeted for efficient calling and messaging. In comparison to smart phones, feature phones have no provision of a biometrics system for the user access. The literature, have shown very less attempts in designing a biometrics system which could be most suitable to the low-cost feature phones. A biometric system utilizes the features and attributes based on the physiological or behavioral properties of the individual. In this research, we explore the usefulness of keystroke dynamics for feature phones which offers an efficient and versatile biometric framework. In our research, we have suggested an approach to incorporate the user’s typing patterns to enhance the security in the feature phone. We have applied k-nearest neighbors (k-NN) with fuzzy logic and achieved the equal error rate (EER) 1.88% to get the better accuracy. The experiments are performed with 25 users on Samsung On7 Pro C3590. On comparison, our proposed technique is competitive with almost all the other techniques available in the literature.</span></p>

Forensic Analysis of Security features in India Bank Cheque- Authentication & Recognition through Docucenter Nirvis

Cheque is one of the most important and commonly encountered financial documents by many individuals and banks for various financial transactions all over the world. Thus, the security and integrity of the cheque is the acute need. Different kind of security features are embedded in bank cheques in order to prevent fraud and counterfeiting of cheques and other bank security documents. Security features appended are in two different ways covert and overt features, some of which are watermarks, logo, serial number, A/c number etc. which can be viewed under different light sources and instruments for examination. In this study the embedded security features of Indian Bank Cheque are examined under instrument Docucenter Nirvis. After examination, deriving to the conclusion that the Indian bank note is appended with ample security features. Keywords: cheque, security features, embedded, examination, counterfeiting

PELATIHAN PENCATATAN KEUANGAN BAGI UMKM di SEKOLAH KEWIRAUSAHAAN BINA AMANAH CORDOVA, TANGGERANG

Abstract :The importance of education in improving the quality of life of a nation and state is well recognized by the Bina Amanah Cordova Entrepreneurship School as an Entrepreneurial Human Resource educational institution that provides free education to high school graduates who have economic constraints in continuing their education to a higher level of education and allow a definitive answers regarding the existing economic constraints. The School of Entrepreneurship Bina Amanah Cordova is in dire need of Lecture who can provide knowledge and skills in accounting through financial recording training for Micro, Small and Medium Enterprises (UMKM). Therefore, the Faculty of Economics and Business - Trisakti University in order to help meet the needs of these teaching staff, in collaboration with the Bina Amanah Cordova Entrepreneurship School carry out Community Service (PKM) by providing practical training on recording MSME financial transactions in accordance with SAK-ETAP Accounting standards. , begins with an explanation of the understanding and accounting process starting from recording financial transactions in journals to the posting process to the general ledger and financial statements, practicing accounting questions, and discussing financial recording problems that Cordova students still face. The results after the PKM implementation were in line with expectations, where participants were very enthusiastic about asking about SAK-ETAP accounting and answering the accounting exercises given.

Adjusting Financial Reporting in the Perspective of Transferring Financial Transactions to the Cryptocurrency Market

The regulation of the cryptocurrency market is becoming an increasingly debated topic in the context of the transition to the digital economy and the health security procedures adopted by the authorities in this period dominated by the pandemic and economic crisis. In this context, we propose a prospective analysis of the effects of legislative regulation and the shift to the cryptocurrency market as a unit supporting digitization. The methods and procedures used in the analysis aim to obtain the staging of the interaction between the accounting system and the cryptocurrency trading processes. Thus, we will address the issue of the digital economy and the effects produced by it, and the result of this approach will be to identify viable solutions that will prevent certain effects on financial reporting and limit tax evasion or other money laundering techniques as a result of the widespread transition to this trading system. The results obtained will be useful to economic decision-makers and tax authorities concerned with these aspects of economic development.

A Comparative Study of Users versus Non-Users’ Behavioral Intention towards M-Banking Apps’ Adoption

The banking sector has been considered as one of the primary adopters of Information and Communications Technologies. Especially during the last years, they have invested a lot into the digital transformation of their business process. Concerning their retail customers, banks realized very early the great potential abilities to provide value added self-services functions via mobile devices, mainly smartphones to them; thus, they have invested a lot into m-banking apps’ functionality. Furthermore, the COVID-19 pandemic has brought out different ways for financial transactions and even more mobile users have taken advantage of m-banking app services. Thus, the purpose of this empirical paper is to investigate the determinants that impact individuals on adopting or not m-banking apps. Specifically, it examines two groups of individuals, users (adopters) and non-users (non-adopters) of m-banking apps, and aims to reveal if there are differences and similarities between the factors that impact them on adopting or not this type of m-banking services. To our knowledge, this is the second scientific attempt where these two groups of individuals have been compared on this topic. The paper proposes a comprehensive conceptual model by extending Venkatech’s et al. (2003) Unified Theory of Acceptance and Use of Technology (UTAUT) with ICT facilitators (i.e., reward and security) and ICT inhibitors (i.e., risk and anxiety), as well as the recommendation factor. However, this study intends to fill the research gap by investigating and proving for the first time the impact of social influence, reward and anxiety factors on behavioral intention, the relationship between risk and anxiety and the impact of behavioral intention on recommendation via the application of Confirmatory Factor Analysis and Structural Equation Modeling (SEM) statistical techniques. The results reveal a number of differences regarding the factors that impact or not these two groups towards m-banking app adoption; thus, it provides new insights regarding m-banking app adoption in a slightly examined scientific field. Thus, the study intends to assist the banking sector in better understanding their customers with the aim to formulate and apply customized m-business strategies and increase not only the adoption of m-banking apps but also the level of their further use.

Issues with mobile money in developing countries

A major criterion is the reduction of snags in the management of mobile money systems. The study was prompted by the ongoing problems with mobile money management. Previous research in underdeveloped nations concentrated mostly on technology algorithms for mobile money systems, with little attention paid to managerial issues. The research aims to reduce hiccups in the management of mobile money systems. A qualitative investigation was carried out, which was supported by activity theory and directed by an interpretative paradigm. The major data tools were semi-structured interviews and an internal document review. Expert purposive sampling was used, and data was thematically evaluated and themes were mapped onto activity theory nodes. The study's key findings included inadequate monitoring of mobile money agents, insufficient confidentiality and privacy in financial transactions, the use of general accounts for financial transactions, the use of generic guidelines and policies, third-party involvement in sensitive mobile money activities, and weak staff recruitment policies. The study's managerial implications include online customer registration, the implementation of online transaction monitoring, the online categorization of mobile money accounts, digital financial crime checks, digital validation of customer identities, and the continuous review and updating of mobile money guidelines.

Parallel analysis of Ethereum blockchain transaction data using cluster computing

Ability to perform fast analysis on massive public blockchain transaction data is needed in various applications such as tracing fraudulent financial transactions. The blockchain data is continuously growing and is organized as a sequence of blocks containing transactions. This organization, however, cannot be used for parallel graph algorithms which need efficient distributed graph data structures. Using message passing libraries (MPI), we develop a scalable cluster-based system that constructs a distributed transaction graph in parallel and implement various transaction analysis algorithms. We report performance results from our system operating on roughly 5 years of 10.2 million block Ethereum Mainnet blockchain data. We report timings obtained from tests involving distributed transaction graph construction, partitioning, page ranking of addresses, degree distribution, token transaction counting, connected components finding and our new parallel blacklisted address trace forest computation algorithm on a 16 node economical cluster set up on the Amazon cloud. Our system is able to construct a distributed graph of 766 million transactions in 218 s and compute the forest of blacklisted address traces in 32 s.

Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method

The application of industry 4.0 in banking presents many challenges, with several operational risks related to downtime and timeout services due to system failures. One of the operational risk management steps is to estimate the value of the maximum potential losses. The purpose of this study is to estimate the maximum potential losses for digital banking transaction risks. The method used for estimating risks is the EVaR method. There are several steps in this study. The first step is to resample the data using MEBoot. This process is a simulation of the operational risk loss data of digital banking. Next, the threshold value is determined to obtain the extreme data value. Then, a Kolmogorov–Smirnov test is conducted to fit the data with the GPD. Afterward, the GPD parameter is estimated. Then, EVaR is calculated using a portfolio approach to obtain a combination of risk values as maximum potential losses. The analysis results show that the maximum potential loss is IDR144,357,528,750.94. The research results imply that the banks need to pay attention to the maximum potential losses of digital financial transactions as a reference for risk management. Therefore, banks can anticipate the adequacy of reserve funds for these potential risks.

Policies and Innovations of Financial Technology Business Models in the Digital Economy Era on the E-Business Ecosystem in Indonesia

The rapid development of technology and information systems continues to give birth to various innovations, especially those related to financial technology to meet the various needs of the community, including access to financial services and processing of financial transactions. Financial technology (FinTech) is the implementation and utilization of technology to improve financial and banking services. The development of financial technology in Indonesia itself is growing rapidly, along with the development of existing technology. FinTech is developed by utilizing the latest software, internet, and computing technologies. Based on this, this study examines the development of innovation and policies for the fintech business model in the e-business ecosystem in Indonesia. This research is a qualitative research with data collection methods through focus group discussions, in-depth interviews, and literature studies. This chapter recommends that the government develop and make policies for fintech business model innovation in the e-business ecosystem in Indonesia.