Financial Transaction Networks Research Articles

Competitor identification with memory in a dynamic financial transaction network

Competitor identification with memory in a dynamic financial transaction network

Visual Exploration of Financial Data with Incremental Domain Knowledge.

Modelling the dynamics of a growing financial environment is a complex task that requires domain knowledge, expertise and access to heterogeneous information types. Such information can stem from several sources at different scales, complicating the task of forming a holistic impression of the financial landscape, especially in terms of the economical relationships between firms. Bringing this scattered information into a common context is, therefore, an essential step in the process of obtaining meaningful insights about the state of an economy. In this paper, we present Sabrina2.0, a Visual Analytics (VA) approach for exploring financial data across different scales, from individual firms up to nation-wide aggregate data. Our solution is coupled with a pipeline for the generation of firm-to-firm financial transaction networks, fusing information about individual firms with sector-to-sector transaction data and domain knowledge on macroscopic aspects of the economy. Each network can be created to have multiple instances to compare different scenarios. We collaborated with experts from finance and economy during the development of our VA solution, and evaluated our approach with seven domain experts across industry and academia through a qualitative insight-based evaluation. The analysis shows how Sabrina2.0 enables the generation of insights, and how the incorporation of transaction models assists users in their exploration of a national economy.

A Comprehensive Study on Namecoin

Abstract: Financial transaction networks are among the world's biggest networks. The digital (crypto) currency network, such as Bitcoin, is a relatively new sort of financial network. Namecoin is a cryptocurrency that is based on Bitcoin and includes functionalities such as DNS. The Namecoin network has nearly 17 million edges and over 2 million nodes. The analysis of such a crypto currency network can aid in the modelling or prediction of future transaction network growth. We evaluated the Namecoin blockchain data in 7 six-month periods in order to analyse the transaction network graph over time. In comparison to Bitcoin, our data imply that user behaviour and development patterns are different [1]. "An experimental open-source technology that improves the decentralisation, security, censorship resistance, privacy, and speed of certain components of the Internet infrastructure, such as DNS and identities," according to Namecoin's definition.

Structural importance and evolution: An application to financial transaction networks

A fundamental problem in the study of networks is the identification of important nodes. This is typically achieved using centrality metrics, which rank nodes in terms of their position in the network. This approach works well for static networks, that do not change over time, but does not consider the dynamics of the network. Here we propose instead to measure the importance of a node based on how much a change to its strength will impact the global structure of the network, which we measure in terms of the spectrum of its adjacency matrix. We apply our method to the identification of important nodes in equity transaction networks and show that, while it can still be computed from a static network, our measure is a good predictor of nodes subsequently transacting. This implies that static representations of temporal networks can contain information about their dynamics.

Penegakan Hukum Kejahatan Carding dengan Modus Wiretapping di Polda Jawa Timur

Modernization in the field of financial transactions is the modernization of new criminal modes, this can occur because the electronic financial transaction network system creates a blemish that can be exploited by every cyber crime actor. In this case, it is the carding perpetrator, the carding crime has also undergone a revolution in accordance with the development of the financial transaction system, of course in this era of electronic banking, the crime of carding also uses the internet network system. Wiretapping is done by tapping credit card transactions through a communication network. there are problems faced in law enforcement of Carding wiretapping crime. In this case, the author is interested in exploring more clearly related to how the cyber crime unit of the East Java Police in carrying out law enforcement for carding crimes. This study uses a normative juridical approach, in order to examine legal protection for victims of carding crime, so that later it produces a descriptive conclusion. The implementation of law enforcement is carried out by means of members of the Cyber Team of the Ditreskrimsus Polda Jatim conducting investigations in the form of cyber patrol activities against perpetrators who previously communicated on social media such as Facebook groups by using wiretapping tools that can find perpetrators quickly through the Cyber system. The efforts that have been made by the Cybercrime Unit of the East Java Regional Police are to create a report hotline number addressed to the public to make it easier to submit complaints. And we also make socialization through social media that never give personal data to others.

A Novel Method for Identifying Competitors Using a Financial Transaction Network

Identifying competitors is an essential first step in creating a competitive advantage to generate superior business performance. Diverse methods for competitor identification have been introduced in the literature, but financial transactions are overlooked in these models even though such transactions among firms, as the basic economic unit of interfirm business activities, contain critical information. This article proposes a novel method for the identification of firm-level competitors using a network constructed from financial transactions among firms. To identify competitors by the intensity of interfirm competition, the proposed method is based on two dimensions of pairs of firms, resource similarity and market commonality, which are represented in a data-driven approach using a transaction network. We propose network-theoretic similarity measures to quantify the resource similarity and market commonality between firms by considering both direct and indirect transactions in a transaction network. Then, based on the two dimensions, three types of competitors are identified with respect to a focal firm: direct, potential, and indirect competitors. The proposed method is applied to toy network data and a real-world case that includes the financial transactions of firms in Korea in 2014.

Structural Importance and Evolution: An Application to Financial Transaction Networks

Structural Importance and Evolution: An Application to Financial Transaction Networks

Throughput Optimal Routing in Blockchain-Based Payment Systems

Cryptocurrency networks, such as Bitcoin, have emerged as a distributed alternative to traditional centralized financial transaction networks. However, there are major challenges in scaling up the throughput of such networks. Lightning network and Spider network are alternates that build bidirectional payment channels on top of cryptocurrency networks using smart contracts, to enable fast transactions that bypass the Blockchain. In this article, we study the problem of routing transactions in such a payment processing network. We first propose a Stochastic model to study such a system, as opposed to a fluid model that is studied in the literature. Each link in such a model is a two-sided queue, and unlike classical queues, such queues are not stable unless there is an external control. We propose a notion of stability for the payment processing network consisting of such two-sided queues using the notion of on-chain rebalancing. We then characterize the capacity region and propose a throughput optimal algorithm that stabilizes the system under any load within the capacity region. The stochastic model enables us to study closed-loop policies, which typically have better queuing/delay performance than the open-loop policies (or static split rules) studied in the literature. We investigate this through simulations.

Improving Bitcoin Ownership Identification Using Transaction Patterns Analysis

Bitcoin is a cryptocurrency and a financial transaction network implemented using blockchain technology. Users in the Bitcoin network use pseudonymous Bitcoin addresses and conduct transactions with others without revealing their real identities. In order to further enhance their privacy and convenience, users often use a large number of different addresses. In this paper, we analyze different patterns of transactions occurring in the Bitcoin network in order to cluster addresses that share the same ownership. In order to evaluate the proposed clustering approach, Bitcoin addresses belonging to known entities are tagged and these are used in conjunction with the Gini impurity index to test the accuracy of the recovered identity-based clusters. The results show that our heuristic was able to detect relationships between Bitcoin addresses that were missed by the existing heuristics.

Network and eigenvalue analysis of financial transaction networks

We study a dataset containing all financial transactions between the accounts of practically all major financial players within Austria over one year. We empirically analyze transaction networks of money (in and out) flows and report the characteristic network parameters. We observe a significant dependence of network topology on the time scales of observation, and remarkably low correlation between node degrees and transaction volume. We further use transaction timeseries of the financial agents to compute covariance matrices and their eigenvalue spectra. Eigenvectors corresponding to eigenvalues deviating from the Marcenko-Pastur law are analyzed in detail. The potential for practical use as an automated detection mechanism for abnormal behavior of financial players is discussed. The opinion expressed in this paper is that of the authors and does not necessarily reflect the opinion of the OeNB or the ESCB.