Market modeling estimates that BNPL transactions in the United Kingdom would be about ** percent higher in 2025 than in 2024. This is according to a market model released in the first quarter of 2025, which placed the UK among the highest user markets in Europe for buy now, pay later. Klarna launched in the UK in late 2018, with Australia's Afterpay joining in 2019 alongside domestic alternatives such as Laybuy. Klarna downloads in the United Kingdom were not as high as those of PayPal in 2022, but the difference between the two apps was getting smaller. Note that the source does not give further indications of what the figures provided here are based on. As buy now, pay later was unregulated in many countries across the world in early 2024, transaction figures on this payment method were scarce unless a domestic financial supervisor managed to request data from individual BNPL providers.

Australia's buy now, pay later (BNPL) user figure climbed by around ** percent year-on-year in the wake of the coronavirus pandemic in 2020. Now reaching almost *********** users, the country is regarded as one of the top countries in the world when it comes to the use of buy now, pay later or POS financing. This is largely due to local players AfterPay and and Zip, although PayPal did announce it would enter the world with its own Pay in * service in 2021.

The Buy Now Pay Later (BNPL) platform market has emerged as a transformative force in the retail and e-commerce sectors, providing consumers with flexible payment options that enhance their shopping experience. By allowing customers to make purchases immediately while deferring payment over time, BNPL solutions have

The Australian Buy Now Pay Later (BNPL) services market is experiencing robust growth, fueled by increasing consumer adoption and the expansion of digital commerce. With a Compound Annual Growth Rate (CAGR) exceeding 10% from 2019 to 2024, the market demonstrates significant potential. The market's expansion is driven by several factors, including the rising preference for flexible payment options among consumers, particularly younger demographics, the increasing penetration of e-commerce, and the aggressive marketing strategies employed by major BNPL providers. The market is segmented across various channels (online and Point of Sale), enterprise sizes (large and small/medium enterprises), and end-user types (consumer electronics, fashion and personal care, healthcare, leisure & entertainment, and retail). Key players such as Afterpay, Zippay, and Paypal actively compete, offering diverse services and features to attract and retain customers. Regulatory scrutiny and the management of associated risks, including consumer debt, pose potential restraints on market growth. However, the increasing integration of BNPL services into existing e-commerce platforms and the development of innovative payment solutions are expected to mitigate these challenges. The significant presence of established players and continuous innovation ensures the market's dynamism and growth in the coming years. The forecast period (2025-2033) anticipates continued expansion of the Australian BNPL market, driven by the ongoing digitalization of retail and the expanding acceptance of BNPL among merchants. Market segmentation continues to evolve, with a growing focus on personalized offerings tailored to specific customer needs and purchasing behaviors. The competitive landscape remains highly dynamic, with ongoing innovation in product offerings, partnership development, and the entrance of new players, albeit at a potentially slower pace as the market matures. The market will likely witness consolidation amongst existing players and a greater focus on profitability and risk management as regulatory scrutiny increases. Successful companies will be those adapting quickly to evolving consumer expectations and proactively addressing the potential risks associated with this rapidly growing sector. Maintaining customer trust and demonstrating responsible lending practices will be crucial for sustained success in this evolving landscape. This comprehensive report provides a detailed analysis of the burgeoning Australia Buy Now Pay Later (BNPL) services market, covering the period 2019-2033. Leveraging robust data and insightful analysis, this report is an indispensable resource for businesses, investors, and stakeholders seeking to understand this dynamic sector. With a focus on key market segments, competitive landscapes, and future growth trajectories, this report offers actionable intelligence to navigate the complexities of the Australian BNPL market. The base year for this report is 2025, with estimations for 2025 and a forecast period extending to 2033. The historical period analyzed is 2019-2024. Recent developments include: In March 2022, Australian buy now, pay later (BNPL) firm Zip has announced a definitive agreement to acquire rival US BNPL fintech Sezzle. The deal values Sezzle at approximately USD 360 million (AUD 491 million)., In October 2021, Visa announced that the firm had expanded its BNPL offering, Visa Installments, to Australia. As part of its launch in the Australian BNPL sector, Visa has entered into a strategic alliance with ANZ and Quest Payment Systems.. Notable trends are: Increase of Non-Cash Payments helps in Market growth.

Demand for interest-free BNPL options for holiday shopping is higher than that of credit cards for 16 out of 17 surveyed countries. This according to a 2022 survey submitted by Klarna - although it states the consumers asked included both users and non-users of this particular buy now, pay later app. On average, ** percent of global consumers preferred BNPL in comparison to a quarter of consumers who said they would opt for credit cards. Several things should be noted, however. First, the question specifically asked for "interest-free" BNPL. Second, Klarna is especially used in Europe as opposed to other parts of the world, whilst the use of credit cards is relatively uncommon in Europe.

In a survey conducted in 2021 about credit card usage in Australia, ** percent of respondents who were part of Generation Z said they had used buy now pay later services in the six months. Younger Australians were more likely to use BNPL as a method of payment when compared to older generations.

The French payment industry, valued at €209.78 million in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 11.98% from 2025 to 2033. This expansion is driven by several key factors. The increasing adoption of digital wallets and mobile payment solutions like Apple Pay, Google Pay, and Samsung Pay is significantly contributing to this growth. Furthermore, the rise of e-commerce and the increasing preference for contactless payments in the retail, entertainment, and hospitality sectors are fueling market expansion. Government initiatives promoting digital financial inclusion also play a crucial role. While the prevalence of cash transactions continues to be a significant factor, the steady shift toward digital payments is undeniable. Competition among payment providers like Mastercard and Visa, along with innovative entrants such as Lydia and Lyf Pay, is fostering innovation and enhancing consumer choice. The industry segments by payment mode (Point of Sale vs. Online) and end-user industry (Retail, Entertainment, Healthcare, Hospitality) reveal diverse growth trajectories, with online sales and digital wallets expected to show particularly strong growth in the forecast period.
Growth is also being influenced by several trends, including the increasing integration of payment solutions with other financial services, the development of advanced security features to combat fraud, and the expansion of open banking initiatives. However, challenges remain. Maintaining consumer trust in digital security and addressing concerns regarding data privacy are essential for sustained growth. Furthermore, the need for robust infrastructure to support the growing volume of digital transactions will require significant investment. The continued evolution of regulatory frameworks will also influence the industry's trajectory. This dynamic market landscape presents both opportunities and challenges for established players and new entrants alike. The French payment industry is poised for continued expansion, driven by technological advancements and changing consumer preferences. Recent developments include: November 2023: Apple, a United States-based technology firm, announced that businesses in France can accept contactless and in-person payments using iPhone Tap to Pay. With the help of this new feature, millions of retailers and small businesses can easily and securely accept payments from digital wallets such as Apple Pay, contactless bank cards, and others. A user's iPhone and a partner's iOS app are only required without any additional hardware or payment terminal., January 2023: Ingenico and Binance launched an integrated crypto payments tool to facilitate cryptocurrency payments in French stores. Initially, the merchants can accept crypto payments at two outlets in France and slowly expand to others., December 2022: Atlantic Money, the international money transfer provider, started its services in France, Italy, and Spain. The company is offering a flat fee of USD 3 transaction fee to beat its rivals and has also revised its sending limit., November 2022: Viva Wallet partnered with Klarna, the Swedish BNPL(Buy Now Pay Later) platform, to provide European merchants with new payment solutions. The payment gateway has been designed to promote e-shopping. The POS app can turn an Android smartphone into a card terminal., November 2022: Worldline and BR-DGE partnered to offer merchants various payment options via a single integration point. To help merchants optimize their payment stack and gain from payment orchestration, the collaboration brings together more than 300 payment providers and technological solutions., September 2022: Thunes collaborated with Alipay+ to accept payments from European mobile users through the wallets of Asian companies, creating a global shopping experience for buyers. The partnership will help network merchants working with Thunes to cater to online consumers based in Asia.. Key drivers for this market are: High Proliferation of E-commerce, including the rise of m-commerce and cross-border e-commerce supported by the increase in purchasing power, Bank Transfers is a Popular Payment Method for High Ticket Items; SMBs are Using Different Payment Methods to Stabilize Sales. Potential restraints include: High Proliferation of E-commerce, including the rise of m-commerce and cross-border e-commerce supported by the increase in purchasing power, Bank Transfers is a Popular Payment Method for High Ticket Items; SMBs are Using Different Payment Methods to Stabilize Sales. Notable trends are: E-Commerce is Observing Significant Growth.

The "Brazilian Payment Methods" dataset provides comprehensive monthly statistics on payment transactions in Brazil in its various forms, sourced from the Banco Central do Brasil (Banco Central do Brasil). This dataset is invaluable for researchers, analysts and policymakers interested in understanding the dynamics of payment methods in Brazil's financial ecosystem.

It is possible to follow the evolution of different payment methods over time, such as the rise of PIX in contrast to the decline of methods such as DOC. This allows for a detailed analysis of how the adoption and use of different payment instruments has changed over the years.

Main features:

Period: The dataset covers monthly data starting from January 2016. Data sources: All data comes directly from the Central Bank of Brazil, ensuring high accuracy and reliability. Payment methods included: PIX: Instant payments. TED (Electronic Transfer Available): High value transfers. TEC (Electronic Credit Transfer): Commonly used to pay salaries DOC (Documentary Credit Order): Interbank transfers. Check: Paper payment method. Boleto: Boletos issued by the bank. Metrics: The dataset includes the quantity and total value of transactions for each payment method. Columns:

YearMonth: The reference month in YYYYMM format. quantityPix: Number of PIX transactions. valuePix: Total value of PIX transactions. quantityTED: The number of TED transactions. valueTED: The total value of TED transactions. quantityTEC: The number of TEC transactions. valueTEC: The total value of TEC transactions. quantityBankCheck: The number of check transactions. valueBankCheck: The total value of check transactions. quantityBrazilianBoletoPayment: Number of boleto transactions. valueBrazilianBoletoPayment: The total value of the boleto transactions. quantityDOC: The number of DOC transactions. valueDOC: The total value of DOC transactions.

This dataset can be used for a variety of analyses, including but not limited to:

Trend analysis: Track the growth or decline in the use of different payment methods over time. Economic Research: Study the impact of economic events on payment behavior. Financial Planning: Assistance in decision-making for financial institutions and companies. Policy making: Inform policy decisions regarding the regulation and promotion of payment methods. Data collection:

Data is collected and updated monthly, ensuring that users have access to the most current information. The script used to collect and update data was designed to be executed automatically, fetching the most recent data from the Central Bank of Brazil API.

Column Names Translation:

The original column names from the Central Bank of Brazil's API have been translated into English where possible. For instance:

    AnoMes has been translated to YearMonth
    quantidadePix has been translated to quantityPix
    valorPix has been translated to valuePix
    quantidadeTED has been translated to quantityTED
    valorTED has been translated to valueTED
    quantidadeTEC has been translated to quantityTEC
    valorTEC has been translated to valueTEC
    quantidadeCheque has been translated to quantityBankCheck
    valorCheque has been translated to valueBankCheck
    quantidadeBoleto has been translated to quantityBrazilianBoletoPayment
    valorBoleto has been translated to valueBrazilianBoletoPayment
    quantidadeDOC has been translated to quantityDOC
    valorDOC has been translated to valueDOC

Thanks:

We thank the Central Bank of Brazil for providing open access to this valuable data. For more details, visit Central Bank of Brazil – Open Data.

License:

This dataset is licensed under the Open Data Commons Open Database License (ODbL). You are free to share, modify and use the data, as long as you attribute the source.

Discover the booming Vietnam payments industry! This report analyzes market size, CAGR (10.58%), key players (MoMo, ZaloPay, VNPAY), and future trends driving growth in digital wallets, online payments, and POS systems. Explore investment opportunities and market segmentation data for 2019-2033. Recent developments include: June 2022 - Vietnam Posts and Telecommunications Group (VNPT) and the Joint Stock Commercial Bank for Foreign Trade of Vietnam (Vietcombank) have inked a collaboration agreement (VNPT). The development of digital payment services and platforms is covered under this bilateral cooperation agreement., November 2021 - Le Minh Khai, the Deputy Prime Minister, has signed a resolution authorizing a scheme for the growth of cashless payment in Vietnam from 2021 to 2025. The project's goals are to promote positive changes in cashless payment in a high-growth economy, make the practice a habit for inhabitants in metropolitan regions, progressively expand to rural, remote, and mountainous locations, as well as eliminate cash-related expenditures.. Key drivers for this market are: High Proliferation of E-commerce, including the rise of m-commerce and cross-border e-commerce supported by the increase in purchasing power, Enablement Programs by Key Retailers and Government encouraging digitization of the market; Growth of Real-time Payments, especially Buy Now Pay Later in the country. Potential restraints include: High Installation Costs Coupled with Maintenance Costs. Notable trends are: Digital Wallets to Drive the Payment Market.

National pension insurance, basic guaranteed old-age pension and monthly statistics data on cases and amounts of indigenous people's benefits

HTTP method: GET

https://api.hkma.gov.hk/public/market-data-and-statistics/monthly-statistical-bulletin/banking/ch-statistics-turnover-fps-hkd-payment-vol?<parameters...>

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The United States had the highest number of merchants who offered Afterpay until 2025, but the market share in Australia and New Zealand was far higher. Estimates based on website tracking and the technologies used within them reveal that merchants from Oceania - Afterpay, not to be confused with AfterPay (Riverty since 2023) from the Netherlands, originates from Australia - offer this payment method relatively more often than anywhere else in the world. For example, estimates are that nearly ***** New Zealand merchants up to February 2024 had Afterpay on their website - a number that equaled over ** percent of all domains in the country with a buy now, pay later (BNPL) option.

This data set is a subset of the "Records of foreign capital" (Registros de capitais estrangeiros", RCE) published by the Central Bank of Brazil (CBB) on their website.The data set consists of three data files and three corresponding metadata files. All files are in openly accessible .csv or .txt formats. See detailed outline below for data contained in each. Data files contain transaction-specific data such as unique identifier, currency, cancelled status and amount. Metadata files outline variables in the corresponding data file.RCE_Unclean_full_dataset.csv - all transactions published to the Central Bank website from the four main categories outlined belowMetadata_Unclean_full_dataset.csvRCE_Unclean_cancelled_dataset.csv - data extracted from the RCE_Unclean_full_dataset.csv where transactions were registered then cancelledMetadata_Unclean_cancelled_dataset.csvRCE_Clean_selection_dataset.csv - transaction data extracted from RCE_Unclean_full_dataset.csv and RCE_Unclean_cancelled_dataset.csv for the nine companies and criteria identified belowMetadata_Clean_selection_dataset.csvThe data include the period between October 2000 and July 2011. This is the only time span for the data provided by the Central Bank of Brazil at this stage. The records were published monthly by the Central Bank of Brazil as required by Art. 66 in Decree nº 55.762 of 17 February 1965, modified by Decree nº 4.842 of 17 September 2003. The records were published on the bank’s website starting October 2000, as per communique nº 011489 of 7 October 2003. This remained the case until August 2011, after which the amount of each transaction was no longer disclosed (and publication of these stopped altogether after October 2011). The disclosure of the records was suspended in order to review their legal and technical aspects, and ensure their suitability to the requirements of the rules governing the confidentiality of the information (Law nº 12.527 of 18 November 2011 and Decree nº 7724 of May 2012) (pers. comm. Central Bank of Brazil, 2016. Name of contact available upon request to Authors).The records track transfers of foreign capital made from abroad to companies domiciled in Brazil, with information on the foreign company (name and country) transferring the money, and on the company receiving the capital (name and federative unit). For the purpose of this study, we consider the four categories of foreign capital transactions which are published with their amount and currency in the Central Bank’s data, and which are all part of the “Register of financial transactions” (abbreviated RDE-ROF): loans, leasing, financed import and cash in advance (see below for a detailed description). Additional categories exist, such as foreign direct investment (RDE-IED) and External Investment in Portfolio (RDE-Portfólio), for which no amount is published and which are therefore not included.We used the data posted online as PDFs on the bank’s website, and created a script to extract the data automatically from these four categories into the RCE_Unclean_full_dataset.csv file. This data set has not been double-checked manually and may contain errors. We used a similar script to extract rows from the "cancelled transactions" sections of the PDFs into the RCE_Unclean_cancelled_dataset.csv file. This is useful to identify transactions that have been registered to the Central Bank but later cancelled. This data set has not been double-checked manually and may contain errors.From these raw data sets, we conducted the following selections and calculations in order to create the RCE_Clean_selection_dataset.csv file. This data set has been double-checked manually to secure that no errors have been made in the extraction process.We selected all transactions whose recipient company name corresponds to one of these nine companies, or to one of their known subsidiaries in Brazil, according to the list of subsidiaries recorded in the Orbis database, maintained by Bureau Van Dijk. Transactions are included if the recipient company name matches one of the following:- the current or former name of one of the nine companies in our sample (former names are identified using Orbis, Bloomberg’s company profiles or the company website);- the name of a known subsidiary of one of the nine companies, if and only if we find evidence (in Orbis, Bloomberg’s company profiles or on the company website) that this subsidiary was owned at some point during the period 2000-2011, and that it operated in a sector related to the soy or beef industry (including fertilizers and trading activities).For each transaction, we extracted the name of the company sending capital and when possible, attributed the transaction to the known ultimate owner.The name of the countries of origin sometimes comes with typos or different denominations: we harmonized them.A manual check of all the selected data unveiled that a few transactions (n=14), appear twice in the database while bearing the same unique identification number. According to the Central Bank of Brazil (pers. comm., November 2016), this is due to errors in their routine of data extraction. We therefore deleted duplicates in our database, keeping only the latest occurrence of each unique transaction. Six (6) transactions recorded with an amount of zero were also deleted. Two (2) transactions registered in August 2003 with incoherent currencies (Deutsche Mark and Dutch guilder, which were demonetised in early 2002) were also deleted.To secure that the import of data from PDF to the database did not contain any systematic errors, for instance due to mistakes in coding, data were checked in two ways. First, because the script identifies the end of the row in the PDF using the amount of the transaction, which can sometimes fail if the amount is not entered correctly, we went through the extracted raw data (2798 rows) and cleaned all rows whose end had not been correctly identified by the script. Next, we manually double-checked the 486 largest transactions representing 90% of the total amount of capital inflows, as well as 140 randomly selected additional rows representing 5% of the total rows, compared the extracted data to the original PDFs, and found no mistakes.Transfers recorded in the database have been made in different currencies, including US dollars, Euros, Japanese Yens, Brazilian Reais, and more. The conversion to US dollars of all amounts denominated in other currencies was done using the average monthly exchange rate as published by the International Monetary Fund (International Financial Statistics: Exchange rates, national currency per US dollar, period average). Due to the limited time period, we have not corrected for inflation but aggregated nominal amounts in USD over the period 2000-2011.The categories loans, cash in advance (anticipated payment for exports), financed import, and leasing/rental, are those used by the Central Bank of Brazil in their published data. They are denominated respectively: “Loans” (“emprestimos” in original source) - : includes all loans, either contracted directly with creditors or indirectly through the issuance of securities, brokered by foreign agents. “Anticipated payment for exports” (“pagamento/renovacao pagamento antecipado de exportacao” in original source): defined as a type of loan (used in trade finance)“Financed import” (“importacao financiada” in original source): comprises all import financing transactions either direct (contracted by the importer with a foreign bank or with a foreign supplier), or indirect (contracted by Brazilian banks with foreign banks on behalf of Brazilian importers). They must be declared to the Central Bank if their term of payment is superior to 360 days.“Leasing/rental” (“arrendamento mercantil, leasing e aluguel” in original source) : concerns all types of external leasing operations consented by a Brazilian entity to a foreign one. They must be declared if the term of payment is superior to 360 days.More information about the different categories can be found through the Central Bank online.(Research Data Support provided by Springer Nature)

This statistical release makes available the most recent Mental Health and Learning Disabilities Dataset (MHLDDS) final monthly data (December 2014). This publication presents a wide range of information about care delivered to users of NHS funded secondary mental health and learning disability services in England. The scope of the Mental Health Minimum Dataset (MHMDS) was extended to cover Learning Disability services from September 2014. Many people who have a learning disability use mental health services and people in learning disability services may have a mental health problem. This means that activity included in the new MHLDDS dataset cannot be distinctly divided into mental health or learning disability spells of care - a single spell of care may include inputs from either of both types of service. We will be working with stakeholders to define specific information and reporting requirements relating to specific services or groups of patients. Four new measures have been added to this release to help with interpretation of the data. At local level these contextual figures will provide an indication of the increased caseload that could be attributed to the extension of the dataset to cover LD services. Information on these measures can found in the Announcement of Change paper which accompanies this release. The Currencies and Payment file that forms part of this release is specifically limited to services in scope for currencies and payment in mental health services and remains unchanged. This information will be of particular interest to organisations involved in delivering secondary mental health and learning disability care to adults and older people, as it presents timely information to support discussions between providers and commissioners of services. The MHLDS Monthly Report also includes reporting by local authority for the first time. For patients, researchers, agencies, and the wider public it aims to provide up to date information about the numbers of people using services, spending time in hospital and subject to the Mental Health Act (MHA). Some of these measures are currently experimental analysis. The Currency and Payment (CaP) measures can be found in a separate machine-readable data file and may also be accessed via an on-line interactive visualisation tool that supports benchmarking. This can be accessed through the related links at the bottom of the page.

The Monthly Banking Statistics publication provides selected information on the banking business of individual banks within the domestic market.

It contains high-level breakdowns of the domestic assets and liabilities of each bank as well as more detail on loans & advances to and deposits by different sectors of the economy. Information on securitisation activity is also included. Both Australian-dollar denominated transactions and the Australian-dollar equivalent of foreign-currency denominated transactions are included.

This dataset contains the monthly average on time performance (OTP) percentage by route and service day type (weekday, Saturday, and Sunday/Holiday service). A bus is considered on time if it is no more than one minute early or five minutes late to a timepoint.

Port Authority has an OTP goal of 73% for bus and 80% for rail service.

Starting in October 2018, Port Authority moved to a different OTP recording system called Clever. OTP data from the Clever system is more accurate because it uses more timepoints; the previous system excluded a large portion of data from OTP processing due to minor technical issues with rider counts on certain trips.

The Mon Incline is not included in this dataset because it does not have a schedule. Service runs every 15 minutes.

OTP only goes back as far as November 2018 for the "T" light rail line because the railcars did not have Automated Vehicle Locators installed until then.

U.S. Economic and Financial Dataset

Dataset Description

This dataset combines historical U.S. economic and financial indicators, spanning the last 50 years, to facilitate time series analysis and uncover patterns in macroeconomic trends. It is designed for exploring relationships between interest rates, inflation, economic growth, stock market performance, and industrial production.

Key Features

• Frequency: Monthly
• Time Period: Last 50 years from Nov-24
• Sources:
  • Federal Reserve Economic Data (FRED)
  • Yahoo Finance

Dataset Feature Description

1. Interest Rate (Interest_Rate):

   • The effective federal funds rate, representing the interest rate at which depository institutions trade federal funds overnight.

2. Inflation (Inflation):

   • The Consumer Price Index for All Urban Consumers, an indicator of inflation trends.

3. GDP (GDP):

   • Real GDP measures the inflation-adjusted value of goods and services produced in the U.S.

4. Unemployment Rate (Unemployment):

   • The percentage of the labor force that is unemployed and actively seeking work.

5. Stock Market Performance (S&P500):

   • Monthly average of the adjusted close price, representing stock market trends.

6. Industrial Production (Ind_Prod):

   • A measure of real output in the industrial sector, including manufacturing, mining, and utilities.

Dataset Statistics

1. Total Entries: 599
2. Columns: 6
3. Memory usage: 37.54 kB
4. Data types: float64

Feature Overview

• Columns:
  • Interest_Rate: Monthly Federal Funds Rate (%)
  • Inflation: CPI (All Urban Consumers, Index)
  • GDP: Real GDP (Billions of Chained 2012 Dollars)
  • Unemployment: Unemployment Rate (%)
  • Ind_Prod: Industrial Production Index (2017=100)
  • S&P500: Monthly Average of S&P 500 Adjusted Close Prices

Executive Summary

This project explores the interconnected dynamics of key macroeconomic indicators and financial market trends over the past 50 years, leveraging data from the Federal Reserve Economic Data (FRED) and Yahoo Finance. The dataset integrates critical variables such as the Federal Funds Rate, Inflation (CPI), Real GDP, Unemployment Rate, Industrial Production, and the S&P 500 Index, providing a holistic view of the U.S. economy and financial markets.

The analysis focuses on uncovering relationships between these variables through time-series visualization, correlation analysis, and trend decomposition. Key findings are included in the Insights section. This project serves as a robust resource for understanding long-term economic trends, policy impacts, and market behavior. It is particularly valuable for students, researchers, policymakers, and financial analysts seeking to connect macroeconomic theory with real-world data.

Potential Use Cases

• Economic Analysis: Examine relationships between interest rates, inflation, GDP, and unemployment.
• Stock Market Prediction: Study how macroeconomic indicators influence stock market trends.
• Time Series Modeling: Perform ARIMA, VAR, or other models to forecast economic trends.
• Cyclic Pattern Analysis: Identify how economic shocks and recoveries impact key indicators.

Snap of Power Analysis

https://github.com/user-attachments/assets/1b40e0ca-7d2e-4fbc-8cfd-df3f09e4fdb8">

To ensure sufficient power, the dataset covers last 50 years of monthly data i.e., around 600 entries.

Key Insights derived through EDA, time-series visualization, correlation analysis, and trend decomposition

• Interest Rate and Inflation Dynamics: The interest Rate and inflation exhibit an inverse relationship, especially during periods of aggressive monetary tightening by the Federal Reserve.
• Economic Growth and Market Performance: GDP growth and the S&P 500 Index show a positive correlation, reflecting how market performance often aligns with overall economic health.
• Labor Market and Industrial Output: Unemployment and industrial production demonstrate a strong inverse relationship. Higher industrial output is typically associated with lower unemployment
• Market Behavior During Economic Shocks: The S&P 500 experienced sharp declines during significant crises, such as the 2008 financial crash and the COVID-19 pandemic in 2020. These events also triggered increased unemployment and contractions in GDP, highlighting the interplay between markets and the broader economy.
• Correlation Highlights: S&P 500 and GDP have a strong positive correlation. Interest rates negatively correlate with GDP and inflation, reflecting monetary policy impacts. Unemployment is negatively correlated with industrial production but positively correlated with interest rates.

Link to GitHub Repo

https:/...

Monthly data showing the Direct Debit failure rate and average Direct Debit transaction amount in the UK. These are official statistics in development. Source: Pay.UK and Vocalink.

The Balance of Payments is the statistical statement that systematically summarises transactions between residents and non-residents. It consists of the goods and services account, the primary income account, the secondary income account, the capital account and the financial account (BPM6 – 2.12) The financial account shows net acquisition and disposal of financial assets and liabilities The financial account indicates the functional categories, sectors, instruments, and maturities used for net international financing transactions. (BPM6 – 8.1). Five functional categories of investment are distinguished in the international accounts: a) direct investment, b) portfolio investment, c) financial derivatives and employee stock options, d) other investment and e) reserve assets. (BPM6 – 6.1). Source of euro area data: European Central Bank (ECB).

In a survey conducted during the second quarter of 2023, Buy Now, Pay Later (BNPL) solutions appeared to be particularly popular among global shoppers. Just over ** percent of respondents in Portugal said they'd prefer using BNPL services to pay for a big-ticket purchase in interest-free installments rather than using their credit card. In contrast, that figure stood at ** percent in the United Kingdom.