Mobile payments apps are used by more than two billion people globally, with millions more coming online each year. In India, South-east Asia and South America, the younger generation skipped the...

Digital wallets are expected to process nearly 78 billion U.S. dollars of online shppping transactions in Italy by 2028. This is according to hybrid research released in 2024, which - depending on the country - either used database modeling or data acquired via a consumer survey. Wallets ranked relatively high among Italy's most-used payment methods when shopping online. Indeed, more than 50 percent of Gen Z in Italy preferred mobile payments to other payment methods when ordering online.

This dataset provides granular, transaction-level data on digital wallet usage, including top-ups, peer-to-peer transfers, and merchant payments. It features rich contextual information such as user, wallet, merchant, device, and location details, making it ideal for payment analytics, fraud detection, and fintech product development.

Card-linked wallets are forecast to be the most used type of wallet in Canada in 2028, but their growth is not as fast as non-card-linked wallets. This is according to hybrid research released in 2024, which - depending on the country - either used database modelling or data acquired via a consumer survey. The market share of wallets in Canada was estimated to increase in the country during online shopping, while credit card may potentially experience some lost. Conversely, adoption of wallets in Canada was lower, though, when compared to other countries worldwide.

This dataset provides granular transaction-level insights into retail payment method adoption, capturing details such as payment type, provider, transaction value, location, and status. It enables comprehensive analysis of trends in cash, card, mobile, and crypto usage across stores and customer segments, supporting retail analytics, fraud detection, and strategic decision-making.

Card-linked wallets are forecast to be the main type of wallet in the U.S. in 2028, with a transaction value much higher than of non-card-linked wallets. This is according to hybrid research released in 2024, which - depending on the country - either used database modeling or data acquired via a consumer survey. The adoption of wallets in the United States ranks lower than of other countries in the world, especially compared to countries from Asia-Pacific.

This dataset brings together real-world payments data from Pay.UK and UK Finance, covering the period August 2024 to July 2025.

It captures how money moves through bank rails (e.g. Bacs, Faster Payments, Cheques) and card rails (debit and credit cards) across the UK - allowing you to analyse trends in transaction volumes, values, adoption, and costs.

The dataset was cleaned, normalised, and structured for analytical use, supporting research into: Market share between bank and card rails Adoption of Faster Payments vs Bacs Debit vs credit card behaviour Illustrative cost modelling across payment systems

Card-linked wallets are predicted to take up the vast majority of e-commerce spending conducted with digital wallets in the UK by 2028. This is according to hybrid research released in 2024, which - depending on the country - either used database modeling or data acquired via a consumer survey. Wallets ranked relatively high among the UK's most-used online payment methods. In contrast, the adoption of wallets in the UK was relatively lower, when compared to other countries worldwide.

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 provides detailed, simulated records of international payment transactions, including sender and recipient information, transaction amounts, currencies, risk scores, and AML/fraud flagging. It is ideal for developing, testing, and benchmarking anti-money laundering, fraud detection, and risk assessment models in cross-border financial environments.

The Point of Sale Terminal market is booming very fast, due to high usage of digital payment, contactless transactions, and cloud-based point of sale. The growth of the Point of Sale terminal market is in linear motion because demand for the sector has been rapidly growing in retail, hospitality, healthcare, and e-commerce sectors. The key innovations include analytics, powered by artificial intelligence, biometric authentication, and mobile point-of-sale systems, which provide both enhanced customer experience and greater operational efficiency. The transition toward cashless economies and the encouragement of digital transactions through regulations increase market adoption further. Security is paramount in the EMV, end-to-end encryption, and tokenization protecting data from users.However, these options come with negatives such as initial higher costs, complexities in integration with the systems, and other security-related challenges.Key competitors include Square, Ingenico, Verifone, and NCR Corporation which emphasize on the cloud-based as well as AI-driven POS options to lead market. IoT penetration, 5G, as well as the blockchain is all expected to revolutionize further steps within the business as well. Recent developments include: June 2023: A new point-of-sale (POS) system from Payabl., a reputable FinTech company known for the payment solutions, has been presented with a focus on supporting the expansion of European retailers. The POS system was created as a reaction to the expanding omnichannel shopping trend. The recently released payabl. POS terminals in fact are designed to take advantage of this potential by making it simpler for merchants to accept payments across all channels, allowing them to work with just one supplier for both online and in-person transactions. The recently released payabl. POS terminals guarantee to give a seamless omnichannel platform, combining transaction processing for cards that are present as well as cards that are not. These terminals will support Visa, Mastercard, along with payments made using Google & Apple Pay, giving businesses a streamlined payment option., May 2023: Axis Bank, one of the biggest private sector banks in India, has launched "Sarathi," the first digital onboarding system which enables retailers to employ point of sale (POS) or electronic data capture (EDC). The technology expedites the application process and offers paperless onboarding for businesses to enable quick POS installation. This is done through real-time database checks & live video verification. The solution provides quick installations within about 45 minutes of the processing of the application. The solution enables merchants to complete the onboarding process in just four easy steps, including real-time database checks for quicker application processing, the live video verification for authenticating merchant information at the merchant's convenience, elimination of the field verification process to help in immediate decision-making, and instant POS installation., May 2023: In collaboration with ICICI Bank, merchant commerce platform Pine Labs announced the adoption of digital Rupee on its point-of-sale terminals. Due to the technical integration between two parties, major retail locations in Mumbai and Bengaluru will now be able to take Digital Rupee at Pine Labs point-of-sale terminals. The process of Digital Rupee payment is carried out entirely digitally by Pine Labs using dynamic QR (quick response) embedded into their intelligent Android PoS terminals., January 2022: The introduction of mobile Android point-of-sale (POS) terminals in the EU, UK, and US was announced by Adyen.. Key drivers for this market are: The increasing adoption of mobile payments The growth of e-commerce The increasing popularity of cloud-based POS systems The growing number of regulations around the world. Potential restraints include: The high cost of POS terminals The complexity of POS systems The lack of interoperability between POS systems. Notable trends are: The increasing adoption of mobile payments is driving demand for new and innovative POS terminals. The growth of e-commerce is also driving demand for POS terminals, as businesses need to be able to accept payments online and in-store. The increasing popularity of cloud-based POS systems is making it easier for businesses to deploy and manage their POS terminals..

Non-card-linked wallets are forecast to be the most used type of wallet in Spain in 2028, but their growth is not as fast as card-linked wallets. This is according to hybrid research released in 2024, which - depending on the country - either used database modeling or data acquired via a consumer survey. Digital wallets ranked first among Spain's most-used online payment methods. This is relatively surprising as the adoption of wallets in Spain was relatively low when compared to other countries worldwide.

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.

Other-Current-Liabilities Time Series for Global Payments Inc. Global Payments Inc. provides payment technology and software solutions for card, check, and digital-based payments in the Americas, Europe, and the Asia-Pacific. It operates through two segments, Merchant Solutions and Issuer Solutions. The Merchant Solutions segment offers authorization, settlement and funding, customer support, chargeback resolution, reconciliation and dispute management, terminal rental, sales and deployment, payment security, and consolidated billing and reporting services. This segment also provides an array of enterprise software solutions that streamline business operations of its customers in various vertical markets; and value-added solutions and services, such as point-of-sale software, analytics and customer engagement, payroll and reporting, and human capital management. The Issuer Solutions segment offers solutions that enable financial institutions and retailers to manage their card portfolios through a platform; and commercial payments, accounts payables, and electronic payment alternatives solutions for businesses and governments. It markets its products and services through direct sales force, trade associations, agent and enterprise software providers, referral arrangements with value-added resellers, and independent sales organizations. The company was founded in 1967 and is headquartered in Atlanta, Georgia.

Interest-Income Time Series for Cantaloupe Inc. Cantaloupe, Inc., a digital payments and software services company, provides technology solutions for the self-service commerce market. The company offers a suite of solutions, including micro-payment processing, self-checkout kiosks, mobile ordering, connected point-of-sale (POS) systems, and enterprise cloud software. It also provides G11 cashless and pulse kits that are 4G LTE digital payment devices for payment and consumer engagement applications; G11 chip kit, a digital reader that accepts contact EMV and contactless EMV payment methods; Engage series comprising Engage, Engage Combo, and Engage Plus, which are digital touchscreen devices that offer networking, security, and interactivity payment methods; and P series, which are card touchscreen card readers that include P66, P100, P100Pro, and P30. In addition, the company offers self-checkout kiosks, smart store concepts, and the Cantaloupe Go management platform comprising Go Mini, Go MiniX, Go Plus100, Go Plus200, Go Plus300, Go Max, Cooler Cafe, Smart Store Go Micro kiosk, and Cantaloupe Smart Aisle. Further, it provides the Seed platform, a cloud-based asset management and optimization solution; remote price change, an add-on software service within the Seed platform; Cantaloupe Go portal, a robust cloud-based platform; Seed API, an API web service; and additional services, such as Cantaloupe Go consumer mobile app loyalty programs, campus card integrations, digital ad-management, and data warehouse services. Additionally, the company offers professional, network infrastructure, card processing, and customer/consumer services. Cantaloupe, Inc. was formerly known as USA Technologies, Inc. The company was incorporated in 1992 and is headquartered in Malvern, Pennsylvania.

Cross-Border Payment Data Enrichment Market Outlook

According to our latest research, the global Cross-Border Payment Data Enrichment market size reached USD 2.14 billion in 2024, supported by a robust demand for advanced payment data solutions across industries. The market is projected to grow at a CAGR of 15.6% from 2025 to 2033, reaching a forecasted value of USD 7.44 billion by 2033. This rapid expansion is being driven by the increasing complexity and volume of international transactions, regulatory requirements for transparency, and the growing need for enhanced data analytics to combat financial crime and optimize payment processes.

The primary growth driver for the Cross-Border Payment Data Enrichment market is the surge in global trade and digital commerce, which has significantly increased the volume and complexity of cross-border payments. Businesses are increasingly seeking solutions that can provide real-time, enriched payment data to streamline reconciliation, reduce errors, and ensure compliance with global regulatory standards such as SWIFT, PSD2, and FATF guidelines. Additionally, as multinational corporations expand their operations into new markets, the demand for seamless and transparent payment processes is fueling the adoption of data enrichment platforms that can handle multi-currency, multi-jurisdictional requirements with ease.

Another significant factor propelling market growth is the rapid advancement in payment technologies and the integration of artificial intelligence and machine learning into payment data enrichment solutions. These technologies enable organizations to extract actionable insights from vast payment datasets, automate anomaly detection, and enhance fraud prevention mechanisms. The shift towards open banking and the proliferation of APIs are further enabling seamless data sharing and enrichment across financial ecosystems, providing businesses with a holistic view of their cross-border transactions and helping them optimize liquidity, reduce operational costs, and improve customer experiences.

The regulatory landscape is also playing a pivotal role in shaping the Cross-Border Payment Data Enrichment market. Governments and regulatory bodies worldwide are imposing stricter requirements on anti-money laundering (AML), know-your-customer (KYC), and transaction monitoring for cross-border payments. As a result, financial institutions and payment service providers are under pressure to implement robust data enrichment tools that can ensure compliance, minimize risk, and provide transparent audit trails. The increasing adoption of real-time payment systems further necessitates the need for accurate and enriched payment data to facilitate instant settlements and reduce friction in international transactions.

From a regional perspective, North America currently dominates the market, accounting for the largest share due to its advanced financial infrastructure, high adoption of digital payment technologies, and stringent regulatory environment. However, the Asia Pacific region is expected to witness the fastest growth over the forecast period, driven by the rapid expansion of e-commerce, increasing cross-border trade, and the proliferation of fintech startups. Europe also remains a significant market, bolstered by regulatory harmonization efforts such as the Single Euro Payments Area (SEPA) and the growing demand for payment transparency among businesses and consumers alike.

Component Analysis

The Cross-Border Payment Data Enrichment market is segmented by component into Solutions and Services, each playing a critical role in meeting the evolving demands of global payment ecosystems. Solutions comprise the core software platforms and tools that automate the enrichment of payment data, ensuring accuracy, compliance, and actionable insights. These solutions are designed to integrate seamlessly with existing payment infrastructures, enabling organizations to capture, process, and analyze vast volumes of cross-border payment data in real time. The increasing complexity of international transactions and the need for comprehensive data management capabilities are driving the adoption of sophisticated solutions that can support multi-currency, multi-jurisdictional operations.

On the other hand, services encompass a broad range of offerings, including consulting, implementation, integration, sup

Context:

This dataset provides detailed insights into consumer behaviour and shopping patterns across various demographics, locations, and product categories. It contains 3,900 customer records with 18 attributes that describe purchase details, shopping habits, and preferences.

The dataset includes information such as:

• Customer demographics (age, gender, location)
• Product details (item purchased, category, size, color, season)
• Purchase information (amount spent in USD, payment method, shipping type)
• Shopping behaviour (frequency of purchases, previous purchases, subscription status, discount usage, promo codes)
• Customer feedback (review ratings)

This dataset can be used to explore consumer decision-making and market trends, including:

• How age, gender, or location influence shopping preferences.
• The relationship between discounts, promo codes, and purchase amounts.
• Which product categories and colors are most popular in different seasons.
• Patterns in payment method usage (e.g., PayPal vs. Credit Card).
• How subscription and loyalty behaviours affect shopping frequency.

Researchers, data analysts, and students can use this dataset to practice customer segmentation, predictive modelling, recommendation systems, and market basket analysis. It also serves as a valuable resource for learning techniques in exploratory data analysis (EDA), machine learning, and business analytics.

Dataset Glossory(Column wise)

Customer ID: A unique identifier assigned to each customer. It helps distinguish one shopper’s data from another without revealing their personal identity.

Age: The age of the customer in years, which can provide insights into generational shopping habits and how preferences differ across age groups.

Gender: Indicates whether the customer is male or female, allowing analysis of gender-based buying trends and preferences in product categories.

Item Purchased: The specific product that the customer bought, giving a direct view of consumer demand and popular items in the dataset.

Category: The broader classification of the purchased item, such as clothing or footwear, which helps in grouping products and understanding category-level trends.

Purchase Amount (USD): The total money spent on the purchase in U.S. dollars, which reflects customer spending power and the value of each transaction.

Location: The state or region where the customer resides, useful for identifying geographical shopping patterns and regional differences in consumer behaviour.

Size: The size of the purchased item (e.g., S, M, L), which helps reveal customer preferences in apparel and how sizing impacts sales.

Color: The chosen color of the purchased item, offering insights into which colors are more appealing to consumers during different seasons or product categories.

Season: The season (Winter, Spring, etc.) in which the purchase was made, showing how customer demand changes across seasonal trends.

Review Rating: A numerical score reflecting the customer’s satisfaction with the product, valuable for measuring quality perception and post-purchase behaviour.

Subscription Status: Indicates whether the customer has an active subscription with the store, which may influence loyalty, discounts, and purchase frequency.

Shipping Type: The delivery option chosen by the customer, such as free shipping or express, which highlights convenience preferences and urgency of purchase.

Discount Applied: Shows whether a discount was used during the purchase, allowing analysis of how discounts affect buying decisions and sales growth.

Promo Code Used: Specifies if the customer used a promotional code, useful for understanding the impact of marketing strategies on purchase behaviour.

Previous Purchases: The number of items the customer has bought before, reflecting their shopping history and overall loyalty to the store.

Payment Method: The mode of payment used (Credit Card, PayPal, etc.), which sheds light on financial behaviour and preferred transaction methods.

Frequency of Purchases: Indicates how often the customer engages in purchasing activities, a critical metric for assessing customer loyalty and lifetime value.

Acknowledgment

Special thanks to Sir Sourav Banerjee Associate Data Scientist at CogniTensor

Kolkata, West Bengal, India

This dataset provides detailed, event-level records of digital wallet user activities, including monetary transactions and non-monetary actions, with rich contextual information such as device, location, channel, and session sequence. It enables comprehensive analysis of user behavioral patterns, session flows, and transaction outcomes for engagement optimization and fraud detection. The dataset is ideal for building predictive models, anomaly detection systems, and user segmentation strategies in digital finance.

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.

Cost-of-Goods-Sold-Including-Depreciation-and-Amortization Time Series for Shift4 Payments Inc. Shift4 Payments, Inc. engages in the provision of software and payment processing solutions in the United States and internationally. The company offers payments platform, which provides omni channel card acceptance; and processing solutions across multiple payment types, including credit, debit, contactless card, Europay, MasterCard and Visa, QR Pay, and mobile wallets, as well as alternative payment methods, such as Apple Pay, Google Pay, Alipay, and WeChat Pay. It provides technology solutions, such as SkyTab POS, which provides purpose-built POS workstations; SkyTab Mobile, which provides pay-at-the-table, order-at-the-table, delivery, customer feedback, and email marketing solutions; SkyTab Venue, which provides mobile ordering, countertop POS, self-service kiosk, and digital wallet solutions; Lighthouse, a cloud-based suite of business intelligence tools that includes customer engagement, social media management, online reputation management, scheduling and product pricing, as well as extensive reporting and analytics; The Giving Block, a cryptocurrency donation marketplace; Shift4Shop, an ecommerce platform that creates a webstore and tools to manage product catalog, order fulfillment and inventory management, search engine optimization, and secure hosting; and Marketplace that enables integrations into third-party applications, as well as loyalty and inventory management. In addition, the company provides merchant operations and support services, including underwriting, onboarding, and activation; training; risk management; and support. It also provides software partner operations and support services, including software integrations and compliance management; partner support; and partner services. The company distributes its products through independent software vendors, internal sales and support network, enterprises, and value-added resellers. The company was founded in 1999 and is headquartered in Center Valley, Pennsylvania.