Biblio

With the advent of ease of access to the internet and an increase in digital literacy among citizens, digitization of the banking sector has throttled. Countries are now aiming for a cashless society. The introduction of a Unified Payment Interface (UPI) by the National Payments Corporation of India (NPCI) in April 2016 is a game-changer for cashless models. UPI payment model is currently considered the world’s most advanced payment system, and we see many countries adopting this cashless payment mode. With the increase in its popularity, there arises the increased need to strengthen the security posture of the payment solution. In this work, we explore the privacy challenges in the existing data flow of UPI models and propose approaches to preserve the privacy of customers using the Unified Payments Interface.

Frauds in supply chain finance not only result in substantial loss for financial institutions (e.g., banks, trust company, private funds), but also are detrimental to the reputation of the ecosystem. However, such frauds are hard to detect due to the complexity of the operating environment in supply chain finance such as involvement of multiple parties under different agreements. Traditional instruments of financial institutions are time-consuming yet insufficient in countering fraudulent supply chain financing. In this study, we propose a novel blockchain-driven framework for deterring fraud in supply chain finance. Specifically, we use inventory financing in jewelry supply chain as an illustrative scenario. The blockchain technology enables secure and trusted data sharing among multiple parties due to its characteristics of immutability and traceability. Consequently, information on manufacturing, brand license, and warehouse status are available to financial institutions in real time. Moreover, we develop a novel rule-based fraud check module to automatically detect suspicious fraud cases by auditing documents shared by multiple parties through a blockchain network. To validate the effectiveness of the proposed framework, we employ agent-based modeling and simulation. Experimental results show that our proposed framework can effectively deter fraudulent supply chain financing as well as improve operational efficiency.

Nowadays, data is essential in several fields, such as science, finance, medicine, and transportation, which means its value continues to rise. Relational databases are vulnerable to copyright threats when transmitted and shared as a carrier of data. The watermarking technique is seen as a partial solution to the problem of securing copyright ownership. However, most of them are currently restricted to numerical attributes in relational databases, limiting their versatility. Furthermore, they modify the source data to a large extent, failing to keep the characteristics of the original database, and they are susceptible to solid malicious attacks. This paper proposes a new robust reversible watermarking technique, Fields Based Inserting Position Tuples algorithm (FBIPT), for relational databases. FBIPT does not modify the original database directly; instead, it inserts some position tuples based on three Fields―Group Field, Feature Field, and Control Field. Field information can be calculated by numeric attributes and any attribute that can be transformed into binary bits. FBIPT technique retains all the characteristics of the source database, and experimental results prove the effectiveness of FBIPT and show its highly robust performance compared to state-of-the-art watermarking schemes.

This article analyzes the basic concepts of supply chain finance, participating institutions, business methods, and exposure to risks. The author combined the basic content of the Internet of Things and block chain technology to carry out research. This paper studies the specific applications of the Internet of Things and block chain technology in supply chain financial risk identification, supply chain financial risk assessment, full-process logistics supervision, smart contract transaction management, corporate financial statement sorting, and risk prevention measures. The author's purpose is to improve the financial risk management level of the enterprise supply chain and promote the stable development of the enterprise economy.

Under the background of "Internet +", in order to solve the problem of deeply mining credit risk behind online supply chain financial big data, this paper proposes an online supply chain financial credit risk assessment method based on deep belief network (DBN). First, a deep belief network evaluation model composed of Restricted Boltzmann Machine (RBM) and classifier SOFTMAX is established, and the performance evaluation test of three kinds of data sets is carried out by using this model. Using factor analysis to select 8 indicators from 21 indicators, and then input them into RBM for conversion to form a more scientific evaluation index, and finally input them into SOFTMAX for evaluation. This method of online supply chain financial credit risk assessment based on DBN is applied to an example for verification. The results show that the evaluation accuracy of this method is 96.04%, which has higher evaluation accuracy and better rationality compared with SVM method and Logistic method.

The rise of supply chain finance has provided effective assistance to SMEs with financing difficulties. This study mainly explores the financial risk evaluation of supply chain under the leadership of 3PL. According to the risk identification, 27 comprehensive rating indicators were established, and then the model under the BP neural network was constructed through empirical data. The actual verification results show that the model performs very well in risk assessment which helps 3PL companies to better evaluate the business risks of supply chain finance, so as to take more effective risk management measures.

FinBots are chatbots built on automated decision technology, aimed to facilitate accessible banking and to support customers in making financial decisions. Chatbots are increasing in prevalence, sometimes even equipped to mimic human social rules, expectations and norms, decreasing the necessity for human-to-human interaction. As banks and financial advisory platforms move towards creating bots that enhance the current state of consumer trust and adoption rates, we investigated the effects of chatbot vignettes with and without socio-emotional features on intention to use the chatbot for financial support purposes. We conducted a between-subject online experiment with N = 410 participants. Participants in the control group were provided with a vignette describing a secure and reliable chatbot called XRO23, whereas participants in the experimental group were presented with a vignette describing a secure and reliable chatbot that is more human-like and named Emma. We found that Vignette Emma did not increase participants' trust levels nor lowered their privacy concerns even though it increased perception of social presence. However, we found that intention to use the presented chatbot for financial support was positively influenced by perceived humanness and trust in the bot. Participants were also more willing to share financially-sensitive information such as account number, sort code and payments information to XRO23 compared to Emma - revealing a preference for a technical and mechanical FinBot in information sharing. Overall, this research contributes to our understanding of the intention to use chatbots with different features as financial technology, in particular that socio-emotional support may not be favoured when designed independently of financial function.

When a robot breaks a person's trust by making a mistake or failing, continued interaction will depend heavily on how the robot repairs the trust that was broken. Prior work in psychology has demonstrated that both the trust violation framing and the trust repair strategy influence how effectively trust can be restored. We investigate trust repair between a human and a robot in the context of a competitive game, where a robot tries to restore a human's trust after a broken promise, using either a competence or integrity trust violation framing and either an apology or denial trust repair strategy. Results from a 2×2 between-subjects study ( n=82) show that participants interacting with a robot employing the integrity trust violation framing and the denial trust repair strategy are significantly more likely to exhibit behavioral retaliation toward the robot. In the Dyadic Trust Scale survey, an interaction between trust violation framing and trust repair strategy was observed. Our results demonstrate the importance of considering both trust violation framing and trust repair strategy choice when designing robots to repair trust. We also discuss the influence of human-to-robot promises and ethical considerations when framing and repairing trust between a human and robot.

In order to standardize and describe vulnerability information in detail as far as possible and realize knowledge sharing, reuse and extension at the semantic level, a vulnerability ontology is constructed based on the information security public databases such as CVE, CWE and CAPEC and industry public standards like CVSS. By analyzing the relationship between vulnerability class and weakness class, inference rules are defined to realize knowledge inference from vulnerability instance to its consequence and from one vulnerability instance to another vulnerability instance. The experimental results show that this model can analyze the causal and congeneric relationships between vulnerability instances, which is helpful to repair vulnerabilities and predict attacks.

"Good Governance" - may it be corporate or governmental, is a badly needed focus area in the world today where the companies and governments are struggling to survive the political and economical turmoil around the globe. All governments around the world have a tendency of expanding the size of their government, but eventually they would be forced to think reducing the size by incorporating information technology as a way to provide services to the citizens effectively and efficiently. Hence our attempt is to offer a complete solution from birth of a citizen till death encompassing all the necessary services related to the well being of a person living in a society. Our research and analysis would explore the pros and cons of using IT as a solution to our problems and ways to implement them for a best outcome in e-Governance occasionally comparing with the present scenario when relevant.

Nowadays the massive amount of data is produced from different sources and lots of applications are processing these data to discover insights. Sometimes we may get unexpected results from these applications and it is not feasible to trace back to the data origin manually to find the source of errors. To avoid this problem, data must be accompanied by the context of how they are processed and analyzed. Especially, data-intensive applications like e-Science always require transparency and therefore, we need to understand how data has been processed and transformed. In this paper, we propose mathematical formulation and implementation of query inversion techniques to trace the provenance of data in a relational database management system (RDBMS). We build mathematical formulations of inverse queries for most of the relational algebra operations and show the formula for join operations in this paper. We, then, implement these formulas of inversion techniques and the experiment shows that our proposed inverse queries can successfully trace back to original data i.e. finding data provenance.

We develop a contingency planning methodology for how a firm would build a global supply chain network with reserve manufacturing capacity which can be strategically deployed by the firm in the event actual demand exceeds forecast. The contingency planning approach is comprised of: (1) a strategic network design model for finding the profit maximizing plant locations, manufacturing capacity and inventory investments, and production level and product distribution; and (2) a scenario planning and risk assessment scheme to analyze the costs and benefits of alternative levels of manufacturing capacity and inventory investments. We develop an efficient heuristic procedure to solve the model. We show numerically how a firm would use our approach to explore and weigh the potential upside benefits and downside risks of alternative strategies.

Digital money drives modern economies, and the global adoption of mobile phones has enabled a wide range of digital financial services in the developing world. Where there is money, there must be security, yet prior work on mobile money has identified discouraging vulnerabilities in the current ecosystem. We begin by arguing that the situation is not as dire as it may seem–-many reported issues can be resolved by security best practices and updated mobile software. To support this argument, we diagnose the problems from two directions: (1) a large-scale analysis of existing financial service products and (2) a series of interviews with 7 developers and designers in Africa and South America. We frame this assessment within a novel, systematic threat model. In our large-scale analysis, we evaluate 197 Android apps and take a deeper look at 71 products to assess specific organizational practices. We conclude that although attack vectors are present in many apps, service providers are generally making intentional, security-conscious decisions. The developer interviews support these findings, as most participants demonstrated technical competency and experience, and all worked within established organizations with regimented code review processes and dedicated security teams.