The statistic shows the amount of money people spent on their last pair of running shoes according to a survey carried out in late 2017. A quarter of the survey respondents said that they spent between 101 and 120 U.S. dollars on their last pair of running shoes.

Money Supply M2 in the United States increased to 21942 USD Billion in May from 21862.40 USD Billion in April of 2025. This dataset provides - United States Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

In 2024, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years, and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges, where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the Financial Crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

View data of the frequency at which one unit of currency purchases domestically produced goods and services within a given time period.

Context

There is a lack of public available datasets on financial services and specially in the emerging mobile money transactions domain. Financial datasets are important to many researchers and in particular to us performing research in the domain of fraud detection. Part of the problem is the intrinsically private nature of financial transactions, that leads to no publicly available datasets.

We present a synthetic dataset generated using the simulator called PaySim as an approach to such a problem. PaySim uses aggregated data from the private dataset to generate a synthetic dataset that resembles the normal operation of transactions and injects malicious behaviour to later evaluate the performance of fraud detection methods.

Content

PaySim simulates mobile money transactions based on a sample of real transactions extracted from one month of financial logs from a mobile money service implemented in an African country. The original logs were provided by a multinational company, who is the provider of the mobile financial service which is currently running in more than 14 countries all around the world.

This synthetic dataset is scaled down 1/4 of the original dataset and it is created just for Kaggle.

Headers

This is a sample of 1 row with headers explanation:

1,PAYMENT,1060.31,C429214117,1089.0,28.69,M1591654462,0.0,0.0,0,0

step - maps a unit of time in the real world. In this case 1 step is 1 hour of time. Total steps 744 (30 days simulation).

type - CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.

amount - amount of the transaction in local currency.

nameOrig - customer who started the transaction

oldbalanceOrg - initial balance before the transaction

newbalanceOrig - new balance after the transaction

nameDest - customer who is the recipient of the transaction

oldbalanceDest - initial balance recipient before the transaction. Note that there is not information for customers that start with M (Merchants).

newbalanceDest - new balance recipient after the transaction. Note that there is not information for customers that start with M (Merchants).

isFraud - This is the transactions made by the fraudulent agents inside the simulation. In this specific dataset the fraudulent behavior of the agents aims to profit by taking control or customers accounts and try to empty the funds by transferring to another account and then cashing out of the system.

isFlaggedFraud - The business model aims to control massive transfers from one account to another and flags illegal attempts. An illegal attempt in this dataset is an attempt to transfer more than 200.000 in a single transaction.

Past Research

There are 5 similar files that contain the run of 5 different scenarios. These files are better explained at my PhD thesis chapter 7 (PhD Thesis Available here http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12932).

We ran PaySim several times using random seeds for 744 steps, representing each hour of one month of real time, which matches the original logs. Each run took around 45 minutes on an i7 intel processor with 16GB of RAM. The final result of a run contains approximately 24 million of financial records divided into the 5 types of categories: CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.

Acknowledgements

This work is part of the research project ”Scalable resource-efficient systems for big data analytics” funded by the Knowledge Foundation (grant: 20140032) in Sweden.

Please refer to this dataset using the following citations:

PaySim first paper of the simulator:

E. A. Lopez-Rojas , A. Elmir, and S. Axelsson. "PaySim: A financial mobile money simulator for fraud detection". In: The 28th European Modeling and Simulation Symposium-EMSS, Larnaca, Cyprus. 2016

Household Saving Rate in the United States increased to 4.90 percent in April from 4.30 percent in March of 2025. This dataset provides - United States Personal Savings Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to Apr 2025 about savings, personal, rate, and USA.

Graph and download economic data for Deposits, All Commercial Banks (DPSACBW027SBOG) from 1973-01-03 to 2025-06-11 about deposits, banks, depository institutions, and USA.

In 2023, the United States spent around 916.02 billion U.S. dollars on its military. U.S. military spending has been increasing in current dollar terms since 2016. Spending increased dramatically in 2022 after the Russian invasion of Ukraine. After the first year of the war, the U.S. had contributed more than 40 billion euros worth of military aid to Ukraine. What military spending entails Military spending in the United States is the part of the national outlays of the Department of Defense. While the department has over two trillion dollars in budgetary resources, its outlays - money actually paid out - are significantly lower. This budget is designated for the four branches of the United States military, and is used for everything from salaries, trainings, development of new military technologies, and new aircraft and weaponry. The high cost of U.S. spending The United States is well known for spending more on its military than any other country. In 2023, it was estimated that per capita defense spending amounted to 2,220 U.S. dollars. While this figure is extremely high, many Americans may find it worthwhile, as a majority believe the United States to be the number one military power in the world.

United States US: Money Market Rate data was reported at 0.800 % pa in 2017. This records an increase from the previous number of 0.395 % pa for 2016. United States US: Money Market Rate data is updated yearly, averaging 4.546 % pa from Dec 1954 (Median) to 2017, with 64 observations. The data reached an all-time high of 16.378 % pa in 1981 and a record low of 0.089 % pa in 2014. United States US: Money Market Rate data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s United States – Table US.IMF.IFS: Money Market and Policy Rates: Annual.

In this paper, an empirically stable money demand model for M3 in the euro area is constructed. Starting with a multivariate system, three cointegrating relationships with economic content are found: (i) the spread between the long-term and the short-term nominal interest rates, (ii) the long-term real interest rate, and (iii) a long-run demand for broad money M3. There is evidence that the determinants of M3 money demand are weakly exogenous with respect to the long-run parameters. Hence, following a general-to-specific modelling approach, a parsimonious conditional error-correction model for M3 money demand is derived which can be interpreted economically. For the conditional model, long-run and short-run parameter stability is extensively tested and not rejected.

U.S. military spending/defense budget for 2022 was 876.94 billion US dollars, a 8.77% increase from 2021.

<ul style='margin-top:20px;'>

<li>U.S. military spending/defense budget for 2021 was <strong>806.23 billion US dollars</strong>, a <strong>3.58% increase</strong> from 2020.</li>
<li>U.S. military spending/defense budget for 2020 was <strong>778.40 billion US dollars</strong>, a <strong>6% increase</strong> from 2019.</li>
<li>U.S. military spending/defense budget for 2019 was <strong>734.34 billion US dollars</strong>, a <strong>7.6% increase</strong> from 2018.</li>
</ul>Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country).

View the total value of the assets of all Federal Reserve Banks as reported in the weekly balance sheet.

This table represents the amount Treasury has in short-term cash investments. Deposits and withdrawals of short-term cash investments are also represented in the Deposits and Withdrawals of Operating Cash table. This program was suspended indefinitely in 2008. All figures are rounded to the nearest million. As of February 14, 2023, Table V Short Term Cash Investments will no longer be updated and removed from the published report. The historical data will remain available.

Key information about Sri Lanka Foreign Exchange Reserves

• Sri Lanka Foreign Exchange Reserves was measured at 6.0 USD bn in Feb 2025, compared with 6.0 USD bn in the previous month
• Sri Lanka Foreign Exchange Reserves: USD mn data is updated monthly, available from Dec 1956 to Feb 2025
• The data reached an all-time high of 9.0 USD bn in Apr 2018 and a record low of 38.0 USD mn in Apr 1970

CEIC extends history for monthly Foreign Exchange Reserves. The Central Bank of Sri Lanka provides Foreign Exchange Reserves in USD. Foreign Exchange Reserves prior to November 2013 is sourced from the International Monetary Fund.


Further information about Sri Lanka Foreign Exchange Reserves

• In the latest reports, Sri Lanka Foreign Exchange Reserves equaled 3.4 Months of Import in Jan 2025.
• Its Money Supply M2 increased 42.7 USD bn YoY in Jan 2025.
• Sri Lanka Domestic Credit reached 58.3 USD bn in Dec 2024, representing an increased of 4.0 % YoY.
• The country's Non Performing Loans Ratio stood at 4.5 % in Dec 2021, compared with the ratio of 4.9 % in the previous year.
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Graph and download economic data for Chinese Yuan Renminbi to U.S. Dollar Spot Exchange Rate (EXCHUS) from Jan 1981 to May 2025 about China, exchange rate, currency, rate, and USA.

This table represents the issues and redemption of marketable and nonmarketable securities. All figures are rounded to the nearest million.