Payment System Data Research Articles

Analysis of the volatility of cash flows in the field of labor relations for monitoring the sustainability of the Russian economy

The aim of this research paper is to assess the applicability of the payment system data provided by the Bank of Russia for a prompt monitoring of the labor relations situation. Considering the growth of precarization, the change in the dynamics of labor relations is considered in the article as an operational indicator of the onset of potential economic crises. Taking the Bank of Russia payment system data as a basis, it is possible to use the volatility of cash flows information in the field of labor relations to determine the state of the economy. A methodological approach to determining critical values of this indicator by applying the three-sigma rule is also given in the article. In addition, the research paper investigates the relationship between the emergence of non-standard practices of interaction between economic actors in the labor market and values of the cash flow in the field of labor relations.

Real-Time Identification and High-Frequency Analysis of Deposits Outflows

AbstractWe propose a method based on control charts to identify in real-time sudden deposits outflows through the payment system. The performance of the methodology is assessed with both Monte Carlo simulations and real transaction-level TARGET2 data for a large sample of Italian banks. We identify a set of idiosyncratic bank stress episodes. Using high-frequency payment system data, we provide new evidences on the interaction between retail, wholesale, and central bank funding in the post global financial crisis period.

Mind the gap: an administrative data analysis of dental treatment outcomes and severe mental illness.

ObjectivesOral health of people with severe mental illness (SMI) remains an important public health issue with some evidence pointing suboptimal dental health outcomes. We test the hypotheses that individuals with SMI have lower contact with dental services and higher levels of fillings and extractions. We also examine effect modification by age. ApproachWe used linked administrative data from general practitioner (GP) records, hospital admissions data and dental payment system data to examine dental service use and treatments (extractions, fillings, crowns and x-rays) among the Northern Ireland hospital population between January 2015 and November 2019 (N=798,564). Logistic regression examined the association of SMI with dental service use and treatments, controlling for socio-demographic characteristics. In a further series of models, the interaction of SMI and age-group was examined. ResultsAn estimated 1.2% (9,656) recorded an SMI diagnosis from hospital records at some point during follow-up. Over two-thirds (67.2%) of the non-SMI hospital population received any dental treatment compared with 62.2% of persons SMI-diagnosed. After adjusting for available socio-demographic characteristics, analysis indicated lower levels of dental service use (OR=0.80, 95% CI=0.77, 0.84), including lower likelihood of fillings (OR=0.81, 0.77, 0.84) and x-rays (OR=0.77, 0.74, 0.81), but higher levels of extractions (OR=1.23, 1.18, 1.29) among patients with SMI. We also found effect modification by age-group: while older age was generally associated with higher likelihood of extractions and crowns, interaction analysis points to lower likelihood of all four dental treatments among older individuals with SMI. ConclusionsOur findings of lower engagement with dental services, lower levels of remedial treatments and higher levels of tooth loss raise concerns about availability, access, and promotion of routine dental care for people with SMI. There is a need for improved understanding of barriers to treatment and development of psychoeducational interventions.

The variations in individual consumption change and the substitution effect under the shock of COVID-19: Evidence from payment system data in China.

Over the last two decades, scholars have pointed to the significance of the impact of extreme events on consumption, a prominent part of national economies. How does the COVID‐19 epidemic influence consumption? Using high‐frequency payment system panel data, we explicitly consider the individual consumption changes and the substitution effect between online and offline markets of multiple categories by constructing autoregressive integrated moving average (ARIMA) models and conducting regression analyses. The p value and regression coefficients of the substitution elasticity are used to estimate the changes and the substitution effects from the offline to the online channels. The results show that consumption saw a remarkable decline after the surge of COVID‐19 in 2020 compared to 2019. Overall, online markets were more resilient than the offline markets and the substitution effects after the epidemic's outbreak between the online and offline markets were significant for one‐third of the consumption categories. However, the online market could not replace the offline market for some categories due to the product characteristics. The vulnerable industries in the face of the epidemic's intervention are determined as being traditional catering, transportation, tourism, and education, and the shortage of healthcare services in extreme events is also pointed out. The results provide suggestions for policies on targeted enterprises and public service.

Artificial intelligence-based analysis of payment system data can predict one-year mortality of hospitalized patients with heart failure

Abstract Background Individual risk stratification is fundamental in the care of heart failure (HF) patients. However, the prediction performance of risk scores, such as MAGGIC and SHFM, is not adequate and, more importantly, they need additional predictors including various biomarkers, imaging data, and environmental factors. Data from a case-mix payment system including diagnosis and procedures with outcomes can be used to develop the risk prediction models, allowing the use of big data for a more accurate prediction of mortality. Purpose This study aimed to develop artificial intelligence (AI) models for predicting 1-year mortality in patients hospitalized due to HF. Methods We analyzed the data from 10175 patients enrolled in the Japanese Registry Of Acute Decompensated Heart Failure (JROADHF). Candidate variables included the data obtained from a payment system introduced by the Japanese government, the Diagnosis Procedure Combination (DPC), which included each patient profile (age, sex, height, weight), principal diagnosis for hospitalization, comorbidities, procedures, length of hospital stay, and discharge status. They did not include clinical data available from patients such as vital status, laboratory data including bio-makers, electrocardiographic and echocardiographic data. The collected data were divided into the training set and the validation set (80%: 20%). With the training set, 5 AI models (logistic regression, random forest, support vector machine, neural network, and ensemble classifier) learned the one-year mortality results. AI models were evaluated by using the validation set with ROC analysis. The training and validation steps were repeated 10 times with different seed values to calculate the C-statistic of each model. We also identified the predictors for one-year prognosis acquired from the AI models. Results At 1-year of follow-up, a total of 1727 patients had died (17%). Among the machine learning models, the ensemble classifier showed the highest C-statistic of 0.76 (95% confidence interval: 0.75 to 0.77) for predicting mortality. Top predictors acquired from the random forest classifier was ADL (Barthel Index) at discharge, age, body mass index, and length of hospital stay. Conclusion By using AI-based analysis of a national case-mix payment system data, the present risk stratification model could predict the one-year mortality of hospitalized HF patients without any quantitative laboratory and physiological data. Furthermore, the present results could emphasize the advantage of this approach using the claim-based data that are routinely collected in a usual daily practice with no need to collect any additional information. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Japan Agency for Medical Research and Development

Development of the predicted and standardized carbapenem usage metric: Analysis of the Japanese Diagnosis Procedure Combination payment system data

This study aimed to develop a metric for standardized and predicted carbapenem consumption using the Diagnosis Procedure Combination payment system database and patients’ characteristics. Based on Diagnosis Procedure Combination data analysis, the developed metric will provide useful benchmarks that stewardship programs can use to help drive improvements.

Can India get to 25 billion retail digital transactions in 2017–18?

The government of India has set a target of 25 billion retail digital transactions for the year 2017–18 and is pushing all agencies to work towards this goal. This is an ambitious goal when one considers that India had only 9.6 billion retail digital transactions in the year 2016–17. This paper examines the last 10 years of payment system data to establish trends and identify when India is likely to reach this target of 25 billion retail digital transactions. It also examines where India stands on the Rogers diffusion innovation curve with regard to the adoption of digital payments. Drawing on statistics from the Committee on Payments and Market Infrastructures, the paper discusses aspects that support the adoption of digital payments. It then proposes strategies for how India could meet the government’s goal more quickly, including pricing decisions, reducing taxation, widening access and tapping specific market segments. While this paper focuses on India, the strategies are applicable to any developing country interested in strengthening digital payments.

2017 Merit-based Incentive Payment System Data Validation and Auditing.

2017 Merit-based Incentive Payment System Data Validation and Auditing.

Using the Payment System Data to Forecast the Italian GDP

Payment systems track economic transactions and therefore could be considered important indicators of economic activity. This paper describes the available monthly data on the retail settlement system for Italy and selects some of them for short-term forecasting. Using a mixed frequency factor model to predict Italian GDP, we find that payment system flows stand out when compared to other standard business cycle indicators.

Identifying Interbank Loans, Rates, and Claims Networks from Transactional Data

Our objective is to identify interbank (i.e., non-collateralized) loans between financial institutions from Colombian large-value payment system data by implementing Furfine’s method. After identifying interbank loans from transactional data, we obtain the interbank rates and claims without relying on financial institutions’ reported data. Contrasting identified loans with those consolidated from financial institutions’ reported data suggests the algorithm performs well, and it is robust to changes in its setup. The weighted average rate implicit in transactional data matches local interbank rate benchmarks strictly. From identified loans, we also build the interbank claims network. The three main outputs (i.e., the interbank loans, the rates, and the claims networks) are valuable for examining and monitoring the money market, for contrasting data reported by financial institutions, and as inputs in models of financial contagion and systemic risk.

The use of payment systems data as early indicators of economic activity

Payment system data are available with virtually no time lag and could therefore help in tracking the evolution of GDP in real time. Using data from the Spanish national system for retail payments, we compare the performance of GDP nowcasts against that of alternative indicators. The main result is that payment system data can make a significant contribution to nowcasting GDP and they can also improve the performance of simple models that are currently based only on purchasing managers indices (PMIs).

Public Funding of HIV/AIDS Prevention, Treatment, and Support in California

To determine the amount of public financing for HIV/AIDS in California and its distribution among treatment, prevention, and support services. To determine the geographical distribution of public financing for HIV/AIDS within California. Data on HIV/AIDS expenditures were compiled across federal and state agencies supporting HIV/AIDS in fiscal year 2008. Federal and state data on programs that finance HIV/AIDS treatment, prevention, and support services, including the Ryan White Program, the Centers for Disease Control and Prevention, and the California General Fund, were compiled. California-specific expenditures for Medicare and Medicaid were calculated from claims data. Other entitlement program spending was estimated from national HIV/AIDS data. Data on AIDS cases by county were obtained from the California State Office of AIDS. Mapping to California counties was accomplished with Arc-GIS software. Public funders accounted for approximately $1.92 billion in HIV/AIDS services in California in fiscal year 2008. Most (90.4%) supported treatment; prevention accounted for 6.4% and support services for 2.6%. The majority of treatment financing came from 2 Federal health entitlement programs, Medicare (36%), and Medicaid (28%). Counties with the highest case loads had lower expenditures per case, suggesting economies of scale. Treatment expenditures overshadow prevention spending. The dominance of entitlement programs in funding for HIV/AIDS treatment challenges policy makers to monitor the extent and quality of HIV/AIDS care in California. A unified health information system for HIV/AIDS that bridged the fragmented health payment system's data silos would benefit policy makers' efforts to monitor the delivery of HIV/AIDS services.

Combining Liquidity Usage and Interest Rates on Overnight Loans: An Oversight Indicator

This study utilizes payment system data to analyze market participants’ liquidity usage and to trace interest rates paid on overnight loans. Our aim is to examine how liquidity usage has changed during the years 2006–2/2011 and to combine this information with data on overnight lending rates between market participants. It turns out that the Furfine algorithm used in the analysis produces overnight interest rates that correlate very closely with the EONIA curve. Based on Finnish payment system data, we identify four separate time periods: normal, start of turmoil, acute crisis and stabilizing period. The results show that, during the acute crisis period, TARGET2 participants holding an account with the Bank of Finland paid, on average, lower overnight interest rates than other banks in the euro area. However, the results reveal there has been some lack of confidence between Finnish participants since the onset of the financial crisis. A new indicator – the Grid – which we present here shows this very clearly. We suggest that this new indicator could be a highly useful tool for overseers in supporting financial stability analysis.