Payment Flows Research Articles

Shallow or deep? Training an autoencoder to detect anomalous flows in a retail payment system

Our paper applies a deep neural network autoencoder (AE) to detect anomalous payment flows in Canada's retail batch clearing payments system, the Automated Clearing Settlement System (ACSS). We aim to investigate an AE's potential for detecting complex changes in the liquidity outflows between participants, which could provide an early warning indication for exceptionally large outflows for a participant. As the Canadian financial system has neither faced bank runs nor severe liquidity shocks in recent history, we trained our models on “normal” data and evaluated them out-of-sample using test data drawn from two constructed scenarios: a sample derived from the largest 1% of observed historical multilateral net outflows and a sample drawn from a simulated bank run. In both cases, the trained AE performed well by producing larger than usual reconstruction errors. Our approach highlights the efficacy of a class of unsupervised machine learning methods as a useful component of a system operator's risk management toolkit.

Pengaruh Capital Adequacy Rasio, Dana Pihak Ketiga, dan Non Performing Loan, terhadap Profitabilitas dengan LDR sebagai Variabel Intervening pada Bank Umum Konvensional di Indonesia

The Bank is an institution that has a role as a financial intermediary between parties who need funds and can facilitate the payment flows. Additionally, The Bank has a function as industries that rely on public trust, so the health level of the Bank needs to be maintained. Profitability is essential for a bank because it measures a company's effectiveness in generating profits by maximizing its assets. This research aim is to investigate the effect of Capital Adequacy Ratio (CAR), Third Parties Funds (TPF), dan Non-Performing Loan (NPL), on Profitability with LDR as Intervening Variables. This research population is conventional commercial banks in Indonesia between 2012 to 2016. This research type is causality research using quantitative data—the sampling technique using a purposive sampling total of 150 samples. The statistical analysis tool in this research is lane analysis with software versions of AMOS 22 and IBM SPSS. The conclusion from this research results that CAR, TAPI F, and NPL variables affect LDR. The CAR, TPF, and LDR variables have not affected profitability, while the NPL variable affects profitability. Furthermore, CAR, TPF, and NPL cannot mediated by LDR.

The Role of Offset in the Collection of Federal Taxes

The legal principle of offset has played a key role in debt collection by private parties for centuries. In 2021, offset plays an equally essential role in the United States government’s collection of debts owed to it, accounting for billions of dollars in funds taken from outgoing payments. The right of offset arises when two parties owe each other debts. The party asserting offset can subtract what is owed to them from what they owe, allowing the parties to avoid an unnecessary transaction. Offset thus makes intuitive sense, simplifying two payment flows into one. But offset becomes far more complex when one of the parties is the federal government, which is unlike a traditional private creditor in important ways.Offset has perhaps its largest impact in the tax system, where Congress has legislated that the Internal Revenue Service (the “Service”) has the authority (and sometimes, the mandate) to offset tax refunds. Refunds are commonly offset when a taxpayer owes prior year tax liabilities, other agency debts (e.g., student loans), state taxes, or past due child support. Despite its frequent use by the Service, offset is subject to minimal procedural protections, likely due to its origin in longstanding common law doctrine. Unlike other forms of tax collection, offset does not carry a right to prepayment judicial review in Tax Court. Nor does offset require the Service to issue a notice to the taxpayer prior to taking collection action. Courts also treat offset inconsistently when the applicable taxpayer/debtor is protected by a collection stay under Title 26 or Title 11, allowing offset in some scenarios and denying it in others.Finally, Congress and the Service have often failed to use their authority to make offset more equitable, particularly as applied to low-income taxpayers. The Service has a limited administrative remedy available for taxpayers to affirmatively request bypass from the offset of their refund to a tax debt. But the remedy is little-publicized, little-used, and difficult to administer. During the COVID-19 pandemic and recession, Congress legislatively protected advance stimulus payments from some forms of offset. But Congress failed to make that protection expansive or to extend it to conventional tax refunds, both of which would have put needed funds in the hands of millions of taxpayers during an economic crisis. Similarly, the Service declined to exercise its statutory discretion to systemically suspend offset of conventional tax refunds to past tax liabilities. These issues extend to payments of the Earned Income Tax Credit (EITC), which are subject to offset. Both Congress and the Service have failed to acknowledge the EITC’s unique nature as a type of public benefit, treating it instead as a conventional tax refund subject to offset. This disproportionately hurts the low-income taxpayers, and their children, that the EITC was enacted to benefit. We argue that policymakers should pay closer attention to offset and make the necessary changes to apply it in a more equitable and logical manner.

ANALISIS SISTEM INFORMASI AKUNTANSI ALUR PEMBAYARAN BIAYA KULIAH DI MASA PANDEMI COVID-19

This study aims to identify and analyze accounting information systemsFlow of Tuition Fees during the Covid-19 Pandemic at the University of TechnologySumbawa. The method used in this research is descriptive methodqualitative. The sources for this research included the Vice Chancellor for 2 FieldsFinance, to be precise, the Directorate of Financial Administration, University of Technology, Sumbawa.Employees who become informants in this study are Finance Director, TreasurerUniversity, and Sub Directorate of Financial Administration. The data source of this researchare primary and secondary, with data collection methods using techniquesinterviews, documentation and observation. The results of this study indicate thatAccounting Information System Flow of Payment of Tuition Fees during the Covid-19 PandemicThe Sumbawa University of Technology has been carried out effectively and efficiently. That mattercan be seen based on existing data, procedures, infrastructure and software as wellresponsible and competent human resources. With the Grooveonline payment of tuition fees during the Covid-19 pandemichelp Sub. The Directorate of Financial Administration works while still doingsocial distancing so that the payment flow can be applied to date.

Payment Risk and Bank Lending

Deposits finance bank lending and serve as means of payment for bank customers. Under uncertain payment flows, deposits are debts with random maturities. Payment outflows drain reserves, and the risk is most prominent when funding markets are under stress and banks are unable to smooth out payment shocks. We provide the first evidence on the negative impact of payment risk on bank lending, bridging the literatures on payment systems and credit supply. An interquartile increase in payment risk is associated with a decline in loan growth rate that is 10% of standard deviation. Our findings are stronger in times of funding stress and robust across banks of different sizes and loans of long and short maturities. Banks with higher payment risk raise deposit rates to expand customer base and internalize payment flows. Finally, we show that payment risk dampens the bank lending channel of monetary policy transmission.

Cashless Payment and Financial Inclusion

This paper evaluates the impact of mobile cashless payment on credit provision to the underprivileged. Using a representative sample of Alipay users that contained detailed information about their activities in consumption, credit, investment, and digital footprints, I exploit a natural experiment to identify the real effects of cashless payment adoption. In this natural experiment, the staggered placement of Alipay-bundled shared bikes across different Chinese cities brings exogenous variations to the payment flow, allowing me to address the endogeneity issues and establish a causal relationship. I find that the use of in-person payment in a month increases the likelihood of getting access to credit in the same month by 56.3%. Conditional on having credit access, a 1% increase in the in-person payment flow leads to a 0.41% increase in the credit line. Those having higher in-person payment flow also use their credit lines more. Importantly, the positive effect of in-person payment flow on credit provision mainly exists for the less educated and the older, suggesting that cashless payment particularly benefits those who are traditionally underserved.

ПЛАНИРОВАНИЕ И ОЦЕНКА НАЛОГОВОЙ НАГРУЗКИ СЕЛЬСКОХОЗЯЙСТВЕННЫХ ТОВАРОПРОИЗВОДИТЕЛЕЙ ПО «ПРИБЫЛЬНЫМ» НАЛОГАМ И СБОРАМ

The agrarian sector of the Russian economy is systematically exposed to state intervention in terms of establishing a special tax regime and providing benefits for certain types of taxes. Along with this, the current tax mechanisms that stimulate investment, innovation and entrepreneurial activities of economic entities of the agro-industrial complex have not been sufficiently worked out, which periodically entails the emergence of a number of problems in the conduct of agribusiness. The article deals with the problematic issues of assessing and planning the tax burden of agricultural producers on the «profitable» taxes at the regional level, the revenues of which largely depends on the filling of budgets of all levels. In this regard, for the executive and legislative authorities of subsidized regions, the issues of objectivity in assessing the tax burden on «profitable» taxes and the possibility of increasing tax revenues from them to the budget are of particular interest. The aim of the study is to analyze the tax burden of agricultural enterprises in terms of «profitable» taxes and to develop proposals for improving the methodology for its assessment, taking into account the close relationship between production efficiency and tax policy. The authors analyzed the flows of tax payments, as well as the list of obligations of agricultural producers of the Saratov region to pay «profitable» taxes, depending on the selected tax regime. The problematic issues of the mechanism of formation of the taxable base and the possibility of eliminating the impact of the human factor on the reduction of the taxable base for «profit- able» taxes are considered. The author’s method of determining the normative taxable base of agricultural organizations for «profitable» taxes, taking into account the profitability of production and differentiation by natural and economic microzones of the Saratov region, is proposed. Using the available information base, the calculation of the regulatory taxable base of agricultural enterprises for profit tax and unified agricultural tax in the context of microzones of the Saratov region was carried out.

Rent-based income redistribution in developed market economies

Public-sector workers in many countries earn more, on average, than their private-sector peers with similar characteristics. In terms of economic theory, these rewards represent economic rents most of which paid by a nation’s taxpayers. In contrast to economic rents accruing to recipients at the top of income distribution, most of these payments flow from one group of workers to another. For this reason, we call these payments “horizontal” economic rents. The level of horizontal rents is analyzed in this paper for 28 OECD countries, mostly representing Europe, based on public-private sector pay gap data from a number of studies. We found that measured as a ratio of public-sector overpayments to GDP, the highest horizontal rents are paid to government workers in Mediterranean EU countries. These rents are relatively low in larger EU countries, such as Germany and the United Kingdom and negative in Scandinavian countries, possibly reflecting the recognition of the non-monetary benefits of public employment, such as job security. Analyzing the determinants of horizontal rents, we found that their levels are lower in countries with stronger trade unions, as measured by trade-unions density and higher in countries with larger foreign-born populations. Macroeconomic variables, including GDP per capita, trade openness, labor force participation and government indebtedness were found to not measurably influence the level of horizontal rents. Further research is seen to be connected with a wider range of the countries under analysis, including the developing countries, and the other groups of employees with the horizontal economic rent, as well as the possible ways to decrease or to invalidate it as regards the practices analysis of the countries with the negligible or negative rent such as Denmark, Norway, Sweden, Portugal, and Iceland.

ОЦЕНКА НАЛОГОВОГО ПОТЕНЦИАЛА АГРАРНОГО СЕКТОРА ЭКОНОМИКИ САРАТОВСКОЙ ОБЛАСТИ

The authors considered the problematic issues of assessment and the directions of improvement of the tax potential application efficiency at the regional and municipal levels as the fill rate of their budgets mainly depends on tax revenues. In this regard, the issues of objectivity of the assessment of tax potential and the possibility of its increase are of great interest for municipal administrations and bodies of the legislative power of beneficiary regions. The authors evaluated the aggregate taxable entities at macro-and micro-levels. The study identified the possibility of using tax potential when assessing the level of impact of taxation on the formation of the conditions for enterprise growth. The authors discovered the interrelation between tax potential at the macro-level and the priority directions choice for the region’s development, analyzed the structure of taxpayers, and determined their role in the formation of the Saratov Region revenue. The paper substantiates the significance of each type of tax, levies, and charge in the formation of the consolidated budget of the region. The authors focus on the types of taxation regimes of agricultural organizations, the size of their tax burden. The paper includes the analysis of tax payment flows, as well as presents the list of obligations of agricultural producers to pay taxes depending on the selected taxation regime. The authors analyzed the influence of tax returns from agroindustrial complex activity on the formation of the Saratov Region revenue, evaluated the taxation of agriculture by the types of taxes, and calculated the tax potential of agricultural producers. The study identified the problems and specified several meaningful measures, which allow identifying the reserves aimed at increasing the level of tax potential of the region and municipal entities.

Star architecture projects. The assessment of spatial economic effects by means of a spatial incidence analysis

Following the inauguration of the Guggenheim Museum Bilbao, designed by the star architect Frank Gehry, numerous cities have developed star architecture projects that try to emulate the ‘Bilbao Effect’ in the hope of triggering positive impacts for tourism and socio-economic development. However, as many projects do not achieve the set goals, their role as catalysts for urban development is debatable. We argue that the temporal and spatial economic dimension of the effects of star architecture projects – which are hardly discussed in existing literature – is crucial to assess the contribution of such projects for urban regeneration. The Kunsthaus Graz in Austria serves as a case study where we apply and modify the approach of the ‘spatial incidence analysis’ to assess the socioeconomic and intertemporal effects of a publicly funded, star architecture project. The spatial incidence analysis is closely related to the cost-benefit analysis, but takes into consideration wider aspects of the spatial distribution and intertemporal development of three dimensions of effects: payment flows, goods and services, and utility. We identify the following key findings by applying a combination of quantitative and qualitative methods: the operation of the Kunsthaus Graz provides an inflow of purchasing power for the municipality of Graz; the Kunsthaus Graz has positive, long-term effects in the fields of tourism marketing, civic pride and identification, creative industries and urban regeneration; the effects of the Kunsthaus Graz are spatially concentrated in the municipality of Graz.

GraphSIF: analyzing flow of payments in a Business-to-Business network to detect supplier impersonation

Supplier Impersonation Fraud (SIF) is a rising issue for Business-to-Business companies. The use of remote and quick digital transactions has made the task of identifying fraudsters more difficult. In this paper, we propose a data-driven fraud detection system whose goal is to provide an accurate estimation of financial transaction legitimacy by using the knowledge contained in the network of transactions created by the interaction of a company with its suppliers. We consider the real dataset collected by SIS-ID for this work.We propose to use a graph-based approach to design an Anomaly Detection System (ADS) based on a Self-Organizing Map (SOM) allowing us to label a suspicious transaction as either legitimate or fraudulent based on its similarity with frequently occurring transactions for a given company. Experiments demonstrate that our approach shows high consistency with expert knowledge on a real-life dataset, while performing faster than the expert system.

Say No to Price Discrimination: Decentralized and Automated Incentives for Price Auditing in Ride-Hailing Services

As the most successful application of the sharing economy, ride-hailing service is popular worldwide and serves millions of users per day worldwide. Ride-hailing service providers (SPs) usually collect users’ personal data to improve their services via big data technologies. However, SPs may also use the collected user data to apply personalized prices to different users, which raises price fairness concerns. In this paper, we propose a smart price auditing system named Spas. Spas allows a user to purchase <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fair Price Insurance</i> in the form of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Price Auditing Contract</i> , then the price of ride-hailing service (RHS) order will be audited automatically once completed. According to the auditing result, the contract punishes misbehaving SPs and also compensates affected users automatically. By replacing an untrustworthy centralized auditor with carefully designed smart contracts, we construct a decentralized price auditing system which is trustworthy and transparent. We demonstrate a theoretical model for practical payment flows based on real RHS user data and we implement Spas in Hyperledger Fabric to show that decentralizing and automating price auditing for RHS with financial incentives is technically feasible.

Measuring the Impact of a Failing Participant in Payment Systems

Banks and financial market infrastructures (FMIs) that are not able to fulfill their payment obligations can be a source of financial instability. This paper develops a composite risk indicator to evaluate the criticality of participants in a large value payment system network, combining liquidity risk and interconnections in one approach, and applying this to the TARGET2 payment system. Findings suggest that the most critical participants in TARGET2 are other payment systems, because of the size of underlying payment flows. Some banks may be critical, but this is mainly due to their interconnectedness with other TARGET2 participants. Central counterparties and central securities depositories are less critical. These findings can be used in financial stability analysis, and feed into central bank policies on payment system access, oversight, and crisis management.

Shallow or Deep? Detecting Anomalous Flows in the Canadian Automated Clearing and Settlement System using an Autoencoder

Financial market infrastructures and their participants play a crucial role in the economy. Financial or operational challenges faced by one participant can have contagion effects and pose risks to the broader financial system. Our paper applies (deep) neural networks (autoencoder) to detect anomalous flows from payments data in the Canadian Automated Clearing and Settlement System (ACSS) similar to Triepels et al. (2018). We evaluate several neural network architecture setups based on the size and number of hidden layers, as well as differing activation functions dependent on how the input data was normalized. As the Canadian financial system has not faced bank runs in recent memory, we train the models on “normal” data and evaluate out-of-sample using test data based on historical anomalies as well as simulated bank runs. Our out-of-sample simulations demonstrate the autoencoder’s performance in different scenarios, and results suggest that the autoencoder detects anomalous payment flows reasonably well. Our work highlights the challenges and trade-offs in employing a workhorse deep-learning model in an operational context and raises policy questions around how such outlier signals can be used by the system operator in complying with the prominent payment systems guidelines and by financial stability experts in assessing the impact on the financial system of a financial institution that shows extreme behaviour.

Systemic risk in the Angolan interbank payment system – a network approach

ABSTRACT In this work, we analyse the topology of the network of interbank payment flows settled via the real-time gross settlement system (RTGS) of the Angolan payment system (APS) during the fourth quarter of 2016, with the aim of discussing the APS resilience to systemic risk, focusing on its vulnerability in case of failures in the settlement of any bank payments. We conclude that (i) the Angolan RTGS payment network is sparse, characterized by low connectivity, (ii) it is a scale-free network with five banks with high connectivity, representing the main origin and destination of the settled transactions and concentrating about 47% of the total volume and amount of payments settled, which adds to contagion risk. However (iii) the systemic risk arising from the removal of a single participant from the network is low, since the largest bank in the system, with the greatest transacted volume and amount, accounts only for about 11% of the total transacted amounts. In addition, (iv) the adequate risk-mitigating operational processes of each of the RTGS subsystems safeguard the APS from systemic risk.

Digital Medicine – The New Frontier for AI in Healthcare

Digital Medicine – The New Frontier for AI in Healthcare

When FinTech Competes for Payment Flows

We study the impact of FinTech competition in payment services when a bank uses payment data to learn about consumers' credit quality. Competition from FinTech payment providers disrupts this information spillover. The bank’s price for payment services and its loan offers are affected. FinTech competition promotes financial inclusion, may hurt consumers with a strong bank preference, and has an ambiguous effect on the loan market. Both FinTech data sales and consumer data portability increase bank lending, but the effects on consumer welfare are ambiguous. Under mild conditions, consumer welfare is higher under data sales than with data portability.

Safeguarding the evidential value of forensic cryptocurrency investigations

Analyzing cryptocurrency payment flows has become a key forensic method in law enforcement and is nowadays used to investigate a wide spectrum of criminal activities. However, despite its widespread adoption, the evidential value of obtained findings in court is still largely unclear. In this paper, we focus on the key ingredients of modern cryptocurrency analytics techniques, which are clustering heuristics and attribution tags. We identify internationally accepted standards and rules for substantiating suspicions and providing evidence in court and project them onto current cryptocurrency forensics practices. By providing an empirical analysis of CoinJoin transactions, we illustrate possible sources of misinterpretation in algorithmic clustering heuristics. Eventually, we derive a set of legal key requirements and translate them into a technical data sharing framework that fosters compliance with existing legal and technical standards in the realm of cryptocurrency forensics. Integrating the proposed framework in modern cryptocurrency analytics tools could allow more efficient and effective investigations, while safeguarding the evidential value of the analysis and the fundamental rights of affected persons.

Behind closed doors: What happens when pharmacies close?

Behind closed doors: What happens when pharmacies close?

Owing Everyone: Debt Advice in the UK’s Time of Austerity

ABSTRACT Under UK austerity, people are obliged to pay up: either to the market (those with debt to commercial creditors) or the state (for those receiving welfare benefits – a person may owe the local council or tax office because she is in arrears or was ‘overpaid’). Seeking clarification or counsel from advisers means entering a world where payments often seem to be automated and where the state is ‘giving with one hand and taking away with the other’. Yet payments have a human, even moral/ethical aspect and must be negotiated. The adviser helps the debtor to close the flood gates through which these payments flow, or temporarily to reduce the cascade to a trickle. Under austerity, with legal aid and other funding withdrawn, the advice sector is performing the government’s work of care. Agencies must identify new wellsprings of care and concern, or intensify the demands placed upon existing ones.