The credit card penetration in Singapore was forecast to continuously increase between 2024 and 2029 by in total 3.5 percentage points. According to this forecast, in 2029, the credit card penetration will have increased for the eighth consecutive year to 47.51 percent. The penetration rate refers to the share of the total population who use credit cards.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the credit card penetration in countries like Cambodia and Philippines.

According to a survey on digital payments usage in Singapore, 68 percent of the Gen Z respondents stated that they used PayNow for their payments. In comparison, 50 percent of the Baby Boomer respondents stated that they used PayNow.

The number of users of digital payments in digital commerce in Singapore was projected to increase by almost *********** in 2030. The number of users of mobile point of sales (POS) payments was projected to be slightly lower than those of digital commerce.The Statista Market Insights cover a broad range of additional markets.

Singapore - Volume of cashless payments, card and e-money payments with cards and e-money issued inside the country by e-money payments

In 2023, about 80 percent of adults used a mobile wallet in Singapore. A mobile wallet is a virtual wallet that stores payment card information on a mobile device, it is either a built-in feature or an app that can be installed onto smartphones.

According to a survey on consumer payment behavior conducted in 2023 in Singapore, approximately ** percent of respondents stated that they had used contactless card for online payments. Meanwhile, about ** percent of the surveyed consumers stated that they had used either credit or debit card for online payments.

The funding value for digital payment companies reached about ***** million U.S. dollars in 2024 in Singapore, across ** deals. This was a slight decrease compared to the previous year, when ** deals were completed, with a funding value of ****** million U.S. dollars. Digital payments In 2023, digital payments stood among the most funded fintech categories in Singapore, with cryptocurrencies, with *** million U.S. dollars raised. The development of digital payments is supported by a connected and innovative financial environment, driven by a national strategy from the Monetary Authority of Singapore (MAS). This is reflected in national programs such as PayNow, Fast And Secure Transfer (FAST), and the Singapore Quick Response Code (SGQR), which facilitate instant, cross-platform, and cross-border transfers. The transaction value of digital payments in Singapore in 2023 reached ***** billion U.S dollars. The sector is projected to grow and is estimated to reach a transaction value of **** billion U.S. dollars in 2028. Companies This innovation-friendly environment has allowed the industry to grow with the success of various companies. The fintech scene is growing rapidly, and the payments sector accounted for ** percent of fintech companies in 2023. Among them, Grab is one of the major players in the sector, with Grabpay, the market leader in mobile wallets, expected to reach *** million users by 2025. In addition, payments are also leading the startup scene, with two payments startups among Singapore's top fintech startups. Nium, the global payments platform, raised *** million U.S. dollars in funding, while M-Daq, the cross-border payments specialist, raised *** million U.S. dollars.

Electronic payments used to make payments (% age 15+) in Singapore was reported at 91.04 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Singapore - Electronic payments used to make payments (% age 15+) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Transactions from digital payments in Singapore was projected to increase ********* between 2024 and 2030. Mobile POS payments is estimated to make up a majority share of the digital payments transaction value.The Statista Market Insights cover a broad range of additional markets.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

Twenty-eight of 55 Planning Areas were excluded due to zero or small population size, accounting for less than 3 percent of the total population. In addition, individuals living in private condos or landed properties were excluded, representing approximately 20 percent of households in Singapore.

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Singapore is 1000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

The Singapore Fintech Market size was valued at USD 10.00 Billion in 2024 and is projected to reach USD 30.00 Billion by 2032, growing at a CAGR of 15.00% from 2026 to 2032.

Key Market Drivers:

Governmental Support and Regulatory Framework: The Singapore government's strong push for a cashless society, as well as financial sector innovation, contribute significantly to the fintech market's growth. The Monetary Authority of Singapore (MAS) established the Singapore FinTech Festival to bring together innovators and investors. According to the MAS Annual Report 2023, Singapore's digital payments sector is expected to exceed SGD 200 billion by 2025, boosted by government efforts such as the establishment of PayNow and other digital payment platforms. This demonstrates how government backing fosters rapid growth in fintech.

Singapore SG: Tax Payments data was reported at 5.000 Number in 2017. This stayed constant from the previous number of 5.000 Number for 2016. Singapore SG: Tax Payments data is updated yearly, averaging 6.000 Number from Dec 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 6.000 Number in 2015 and a record low of 5.000 Number in 2017. Singapore SG: Tax Payments data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Singapore – Table SG.World Bank.WDI: Company Statistics. Tax payments by businesses are the total number of taxes paid by businesses, including electronic filing. The tax is counted as paid once a year even if payments are more frequent.; ; World Bank, Doing Business project (http://www.doingbusiness.org/).; Unweighted average; Data are presented for the survey year instead of publication year.

According to a 2021 survey on usage of digital payments among senior citizens, ** percent of the respondents indicated that they use contactless card payments. On the other hand, ** percent of respondents indicated that they use QR code payment methods for digital payments.

Abstract

The Global Findex 2025 reveals how mobile technology is equipping more adults around the world to own and use financial accounts to save formally, access credit, make and receive digital payments, and pursue opportunities. Including the inaugural Global Findex Digital Connectivity Tracker, this fifth edition of Global Findex presents new insights on the interactions among mobile phone ownership, internet use, and financial inclusion.

The Global Findex is the world’s most comprehensive database on digital and financial inclusion. It is also the only global source of comparable demand-side data, allowing cross-country analysis of how adults access and use mobile phones, the internet, and financial accounts to reach digital information and resources, save, borrow, make payments, and manage their financial health. Data for the Global Findex 2025 were collected from nationally representative surveys of about 145,000 adults in 141 economies. The latest edition follows the 2011, 2014, 2017, and 2021 editions and includes new series measuring mobile phone ownership and internet use, digital safety, and frequency of transactions using financial services.

The Global Findex 2025 is an indispensable resource for policy makers in the fields of digital connectivity and financial inclusion, as well as for practitioners, researchers, and development professionals.

Geographic coverage

National Coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most low- and middle-income economies, Global Findex data were collected through face-to-face interviews. In these economies, an area frame design was used for interviewing. In most high-income economies, telephone surveys were used. In 2024, face-to-face interviews were again conducted in 22 economies after phone-based surveys had been employed in 2021 as a result of mobility restrictions related to COVID-19. In addition, an abridged form of the questionnaire was administered by phone to survey participants in Algeria, China, the Islamic Republic of Iran, Libya, Mauritius, and Ukraine because of economy-specific restrictions. In just one economy, Singapore, did the interviewing mode change from face to face in 2021 to phone based in 2024.

In economies in which face-to-face surveys were conducted, the first stage of sampling was the identification of primary sampling units. These units were then stratified by population size, geography, or both and clustered through one or more stages of sampling. Where population information was available, sample selection was based on probabilities proportional to population size; otherwise, simple random sampling was used. Random route procedures were used to select sampled households. Unless an outright refusal occurred, interviewers made up to three attempts to survey each sampled household. To increase the probability of contact and completion, attempts were made at different times of the day and, where possible, on different days. If an interview could not be completed at a household that was initially part of the sample, a simple substitution method was used to select a replacement household for inclusion.

Respondents were randomly selected within sampled households. Each eligible household member (that is, all those ages 15 or older) was listed, and a handheld survey device randomly selected the household member to be interviewed. For paper surveys, the Kish grid method was used to select the respondent. In economies in which cultural restrictions dictated gender matching, respondents were randomly selected from among all eligible adults of the interviewer’s gender.

In economies in which Global Findex surveys have traditionally been phone based, respondent selection followed the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies in which mobile phone and landline penetration is high, a dual sampling frame was used.

The same procedure for respondent selection was applied to economies in which phone-based interviews were being conducted for the first time. Dual-frame (landline and mobile phone) random digit dialing was used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digit dialing was used in economies with limited or no landline presence (less than 20 percent). For landline respondents in economies in which mobile phone or landline penetration is 80 percent or higher, respondents were selected randomly by using either the next-birthday method or the household enumeration method, which involves listing all eligible household members and randomly selecting one to participate. For mobile phone respondents in these economies or in economies in which mobile phone or landline penetration is less than 80 percent, no further selection was performed. At least three attempts were made to reach the randomly selected person in each household, spread over different days and times of day.

Research instrument

The English version of the questionnaire is provided for download.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in: Klapper, Leora, Dorothe Singer, Laura Starita, and Alexandra Norris. 2025. The Global Findex Database 2025: Connectivity and Financial Inclusion in the Digital Economy. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-2204-9.

Singapore E-Commerce Transactions: Value data was reported at 84,366.935 USD in 10 May 2025. This records a decrease from the previous number of 124,510.418 USD for 09 May 2025. Singapore E-Commerce Transactions: Value data is updated daily, averaging 293,592.360 USD from Dec 2018 (Median) to 10 May 2025, with 2324 observations. The data reached an all-time high of 4,031,980.227 USD in 09 Dec 2021 and a record low of 39,488.686 USD in 12 Apr 2025. Singapore E-Commerce Transactions: Value data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Singapore – Table SG.GI.EC: E-Commerce Transactions: by Device.

The Singapore Fintech Market Report is Segmented by Service Proposition (Digital Payments, Digital Lending & Financing, Digital Investments, Insurtech, Neobanking), End-User (Retail, Businesses), User Interface (Mobile Applications, Web/Browser, POS/IoT Devices), and Geography (Central Region, East Region, North Region, North-East Region, West Region). The Market Forecasts are Provided in Terms of Value (USD).

The most used digital wallets in Singapore typically originate from outside the country, with one app reaching a MAU of more than *******. This is according to a ranking of the biggest apps that can function as a digital wallet for payments, based on a minimum of ****** monthly active users (MAU). Singapore offers some domestic digital wallets like DBS digibank or YouTrip, but they are not the most used wallets in the country. That said, digital wallets are being used significantly more in Singapore POS than other payment types.

Singapore E-Commerce Transactions: Value: Science & Education: Others data was reported at 40.495 USD in 11 Nov 2023. This records a decrease from the previous number of 66.057 USD for 09 Nov 2023. Singapore E-Commerce Transactions: Value: Science & Education: Others data is updated daily, averaging 39.143 USD from Jan 2019 (Median) to 11 Nov 2023, with 298 observations. The data reached an all-time high of 1,393.624 USD in 20 Nov 2019 and a record low of 9.621 USD in 10 Feb 2020. Singapore E-Commerce Transactions: Value: Science & Education: Others data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Singapore – Table SG.GI.EC: E-Commerce Transactions: by Category.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 640 companies listed on the Singapore Exchange* (XSES) in Singapore. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Singapore:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Singapore:

Straits Times Index (STI): The main index that tracks the performance of companies listed on the Singapore Exchange (SGX). The STI provides a comprehensive view of the overall market performance in Singapore.

Company A: A prominent Singaporean company in sectors such as finance, real estate, or technology. This company's stock is among the most actively traded on the SGX and influences market trends.

Company B: A major Singaporean bank or financial institution offering a wide range of financial services. This company's stock is significant in the financial sector and reflects the economic landscape of Singapore.

Singapore Telecommunications Limited (Singtel): A leading telecommunications company in Singapore with international operations. The stock of Singtel is actively traded and has an impact on the communication sector.

Keppel Corporation Limited: A conglomerate in Singapore with interests in various sectors such as offshore and marine, real estate, and infrastructure. The stock of Keppel Corporation is a key player in the Singaporean market.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Singapore, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Singapore ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Singapore?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Singapore exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wir...

Singapore E-Commerce Transactions: Value: Tablet data was reported at 1,287.793 USD in 10 May 2025. This records an increase from the previous number of 1,187.732 USD for 09 May 2025. Singapore E-Commerce Transactions: Value: Tablet data is updated daily, averaging 6,983.921 USD from Dec 2018 (Median) to 10 May 2025, with 2324 observations. The data reached an all-time high of 152,334.943 USD in 11 Apr 2022 and a record low of 249.561 USD in 14 Sep 2024. Singapore E-Commerce Transactions: Value: Tablet data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Singapore – Table SG.GI.EC: E-Commerce Transactions: by Device.