The Thailand mobile payments market is experiencing robust growth, projected to reach a significant value by 2033. A compound annual growth rate (CAGR) of 14% from 2019 to 2024 indicates a strong upward trajectory, driven by increasing smartphone penetration, rising internet usage, and a growing preference for cashless transactions among Thai consumers. Key drivers include government initiatives promoting digital financial inclusion, the expansion of mobile network infrastructure, and the widespread adoption of mobile wallets by both businesses and individuals. The market is segmented by payment type, with proximity payments and remote payments representing distinct categories. Major players like Grab, True Money, and leading banks like KASIKORNBANK are actively competing in this dynamic landscape, fostering innovation and competition. The convenience and security offered by mobile payment systems are attracting a broad user base, pushing the market towards further expansion. While challenges such as cybersecurity concerns and digital literacy gaps exist, the overall growth trajectory remains positive, indicating substantial potential for investors and businesses operating within the Thai mobile payments sector. The continued growth of e-commerce in Thailand further accelerates the adoption of mobile payment solutions. Consumers are increasingly embracing the speed, convenience, and security of mobile transactions for both online and offline purchases. This trend is fueled by younger demographics who are digitally savvy and comfortable using mobile technology for financial transactions. Furthermore, the integration of mobile payments with various loyalty programs and rewards systems enhances user engagement and incentivizes continued usage. The government's focus on developing robust digital infrastructure and promoting financial inclusion plays a critical role in supporting this growth. While the competitive landscape is intense, the market offers opportunities for both established players and emerging fintech companies to innovate and capture market share. This includes the development of specialized solutions targeting specific consumer segments and the integration of mobile payments with other financial services. This report provides a detailed analysis of the dynamic Thailand mobile payments industry, covering the period from 2019 to 2033. It leverages a robust data set, encompassing historical data (2019-2024), the base year (2025), and a comprehensive forecast (2025-2033), to offer actionable insights into this rapidly evolving market. We analyze key players like LINE Corporation, Grab, True Money Co Ltd, and Prompt Pay Ltd, alongside emerging fintech disruptors, to provide a complete picture of the competitive landscape. The report uses high-search-volume keywords such as "Thailand mobile payments market size," "Thailand mobile wallet adoption," and "Thailand fintech trends" to ensure maximum visibility. Recent developments include: April 2023: Shopee has announced that ShopeePay is Available in Thailand as a payment method to buy Apple services, such as the app store, icloud storage, etc. Customers can use their Shopee Pay account to pay for things like Apple Music, Oscar TV apps, iTunes store purchases, etc. Adding a mobile wallet as an Apple ID payment method offers a new and more convenient way for payments with Apple services while allowing you to make one-tap purchases on products like iPhone, iPad, or Mac without using your credit card., November 2022: Google Pay and Wallet have launched in Thailand as the pandemic has filled a fear of cash across every customer, and while contactless payment options were widely available and used in other countries, Thailand can also have a boarding pass stored in Google Wallet so that you don't need to carry paper passes when flying, which makes it slightly more convenient.. Key drivers for this market are: Thailand's emerging e-commerce market is expected to grow by double digits., Internet Penetration Witnessing a Significant Growth in the Thailand Mobile Payments Market. Potential restraints include: Integration issues with traditional systems, Data quality and accuracy issues. Notable trends are: Proximity Payment to Witness the Growth.

Cryptocurrency payments are forecast to grow at a CAGR of nearly 17 percent between 2023 and 2030, although the market is relatively small. The forecast is according to a market estimate made in early 2023, based on various conditions and sources available at that time. It should be noted, however, that cryptocurrency used for payments is predicted to be a far smaller market than the predicted transaction value of CBDC, or the forecast market size of instant payments. Indeed, research from early 2023 across 40 countries suggested that the market share of cryptocurrency in e-commerce transaction was "less than one percent" in all survey countries, with predictions being this would not change in the future.

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

The smartphone penetration ranking is led by Canada with 97 percent, while the United Arab Emirates is following with 97 percent. In contrast, Mozambique is at the bottom of the ranking with 9.48 percent, showing a difference of 87.52 percentage points to Canada. The penetration rate refers to the share of the total population.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).

We conducted a cross-sectional study of the publicly available 2022 Open Payments data to characterize and quantify sponsored events (available for download at: https://www.cms.gov/priorities/key-initiatives/open-payments/data/dataset-downloads). Data sources We downloaded the 2022 dataset ZIP files from the Open Payments website on June 30th, 2023. We included all records for nurse practitioners, clinical nurse specialists, certified registered nurse anesthetists, and certified nurse-midwives (hereafter advanced practiced registered nurses (APRNs)); and allopathic and osteopathic physicians (hereafter, ‘physicians’). To ensure consistency in provider classification, we linked Payments data to the National Plan and Provider Enumeration System data (June 2023) by National Provider Identifier (NPI) and the National Uniform Claim Committee (NUCC) and excluded individuals with an ambiguous provider type. Event-centric analysis of Open Payments records: Creating an event typology We included only payments classified as “food and beverage” to reliably identify distinct sponsored events. We reasoned that food and beverage would be consumed on the same day in the same place, thus assumed that records for food and beverage associated with the same event would share the date of payment and location. We also assumed that the reported value of a food and beverage payment is the total cost of the hospitality divided by the number of attendees, thus grouped payment records with the same amount, rounded to the nearest dollar. Inferring which Open Payment records relate to the same sponsored event requires analytic decisions regarding the selection and representation of variables that define an event. To understand the impact of these choices, we undertook a sensitivity analysis to explore alternative ways to group Open Payments records for food and beverage, to determine how combination of variables, including date (specific date or within the same calendar week), amount (rounded to nearest dollar), and recipient’s state, affected the identification of sponsored events in the Open Payments data set. We chose to define a sponsored event as a cluster of three or more individual payment records for food and beverage (nature of payment) with the following matching Open Payments record variables: • Submitting applicable manufacturer (name) • Product category or therapeutic area • Name of drug or biological or device or medical supply • Recipient state • Total amount of payment (USD, rounded to nearest dollar) • Date of payment (exact) After examining the distribution of the data, we classified events in terms of size (≥20 attendees as “large” and 3-<20 as “small”) and amount per person. We categorized events <$10 as “coffee”, $10-<$30 as “lunch”, $30-<$150 as “dinner”, and ≥$150 as “banquet”.

This dataset analyzes the direct effect of positive and negative word-of-mouth, peer influence, alternative attractiveness, and trust in the incumbent provider on users' intentions to switch. It also examines the mediating role of alternative attractiveness in the relationship between positive WOM, and switching intentions; the mediating role of trust in incumbent service in the relationship between negative WOM and switching intentions.

Payment cards in Ukraine; non-cash payments using payment terminals; payment infrastructure; transactions carried out using payment cards; distribution of non-cash transactions using payment cards; general data on the number of customers, electronic means of payment (including payment systems) and payment devices; the amount and number of transactions using electronic means of payment issued by Ukrainian banks; types of electronic means of payment issued by Ukrainian banks; data in terms of participants of payment systems on the number of payment cards and their service infrastructure; data in the regional context on the number of electronic means of payment and the infrastructure of their service.

Variability in mean payment per physician, number of physicians, and aggregated payments for transactions in the Open Payments database, 2014–2018, for each top-category specialty available for allopathic and osteopathic physicians.

The population share with mobile internet access in India was forecast to continuously increase between 2024 and 2029 by in total 25 percentage points. After the fifteenth consecutive increasing year, the mobile internet penetration is estimated to reach 73.62 percent and therefore a new peak in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.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 population share with mobile internet access in countries like Bangladesh and Sri Lanka.

For each type of payment category among general payments in the Open Payments database, 2013–2018, the mean payment per physician, the number of physicians receiving payments, and the total amount of money transacted.

Comprehensive dataset of 2 Payment terminals in Japan as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Global Online Payment Fraud Detection market size 2025 was XX Million. Online Payment Fraud Detection Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

Comprehensive dataset of 1 Payment terminals in New Mexico, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

In financial year 2024, almost *** billion digital payments were recorded across India. This was a significant increase compared to the previous three years.  Variety of digital payments  The total value of digital payments included large-scale interbank payments, such as Real Time Gross Settlement (RTGS) or National Electronic Funds Transfer (NEFT), as well as payments used by individuals, such as credit and debit cards. India’s mobile payment system, Unified Payments Interface (UPI), recorded strong gains, both in numbers and in value, since 2015. Thereby, it comes as no surprise that international key players, such as Google Pay or Amazon Pay, entered the market. Nevertheless, the most used app in 2021 was domestic app PhonePe.  COVID-19 effects  Since the beginning of the COVID-19 pandemic in India, the number of digital payment transactions continued to grow. This was also true for the various methods of credit and debit transfers, including mobile payments through UPI. Nevertheless, the value of card payments and of large value credit transfers, such as RTGS, decreased considerably in financial year 2021.

Envestnet®| Yodlee®'s Consumer Spending Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: Analytics B2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis.

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

The Measurable AI Amazon Consumer Transaction Dataset is a leading source of email receipts and consumer transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact michelle@measurable.ai for a data dictionary and to find out our volume in each country.

Introduction: Although hospitals are key health service providers, their financial ties to drug companies have been rarely scrutinised. In developing this body of work, we examine industry payments for non-research activities to National Health Service (NHS) trusts – hospital groupings providing publicly funded secondary and tertiary care in England. Methods: We extracted data from the industry-run Disclosure UK database, analysing payment distribution descriptively and identifying trends in medians with the Jonckheere-Terpstra test. The payment value and number per NHS trust were explained using random effects models. Results: Between 2015 and 2018, 116 companies reported paying £60,253,421.86 to 235 trusts. As a share of payments to all healthcare organisations the number of payments to trusts rose from 38.64% to 39.48%, but their value dropped from 33.01% to 23.61%. While the number of all payment types rose, fees for service and consultancy and contributions to costs of events increased by 61.55% and 29.43%, respectively. The median payment values decreased significantly for trusts overall, including those with lower autonomy from central government; providing acute services; and from four of the eight regions of England. The random effects model showed that trusts with all other service profiles received a significantly lower value of payments on average than acute trusts; and trusts from East England received significantly less than those from London. However, trusts enjoying greater autonomy from government did not receive significantly more payments than others. Trusts also received significantly lower (but not fewer) payments in 2018 than in 2015. Conclusion: NHS trusts were losing importance as funding targets relative to other healthcare organisations. Industry payment strategies shifted towards engaging with NHS trusts using events sponsorship, consultancies, and smaller payments. Industry prioritised payments to trusts with specific service and geographical profiles. More granular disclosure is necessary to understand the role of corporate funding across the health system.

Comprehensive dataset of 2 Payment terminals in Sweden as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Asian Journal of Economics, Finance and Management

The online banking penetration rate in India was forecast to continuously increase between 2024 and 2029 by in total 19.3 percentage points. After the fifteenth consecutive increasing year, the online banking penetration is estimated to reach 64.34 percent and therefore a new peak in 2029. Notably, the online banking penetration rate of was continuously increasing over the past years.Shown is the estimated percentage of the total population in a given region or country, which makes use of online banking.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 online banking penetration rate in countries like Pakistan and Bangladesh.