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The most used digital wallets in Brazil typically originate from outside the country, with only three apps reaching a MAU of more than five million. This is according to a ranking of the biggest apps that can function as a digital wallet for payments, based on a minimum of 50,000 monthly active users (MAU). Digital wallet use in Brazil is heavily tied to apps from both Latin America and Brazil itself. For example, Argentina's Mercado Libre - Argentina's e-commerce super app and home to the Mercado Pago payment method - had almost three times more users than Amazon. Indeed, digital wallets being used significantly more in Brazil POS than other payment types.

Research dataset and analysis for Payment Solutions including statistics, forecasts, and market insights

Context

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

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

Content

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

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

NOTE: Transactions which are detected as fraud are cancelled, so for fraud detection these columns (oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest ) must not be used.

Headers

This is a sample of 1 row with headers explanation:

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

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

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

amount - amount of the transaction in local currency.

nameOrig - customer who started the transaction

oldbalanceOrg - initial balance before the transaction

newbalanceOrig - new balance after the transaction.

nameDest - customer who is the recipient of the transaction

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

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

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

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

Past Research

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

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

Acknowledgements

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

Please refer to this dataset using the following citations:

PaySim first paper of the simulator:

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

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.

Techsalerator’s Business Technographic Data for Laos: Unlocking Insights into Laos' Technology Landscape

Techsalerator’s Business Technographic Data for Laos provides a detailed and comprehensive dataset essential for businesses, market analysts, and technology vendors seeking to understand and engage with companies operating within Laos. This dataset offers in-depth insights into the technological landscape, capturing and organizing data related to technology stacks, digital tools, and IT infrastructure used by businesses in the country.

Please reach out to us at info@techsalerator.com or visit Techsalerator Contact.

Top 5 Most Utilized Data Fields

• Company Name: This field lists the names of companies in Laos, allowing technology vendors to target potential clients and enabling analysts to assess technology adoption trends within specific businesses.

• Technology Stack: This field outlines the technologies and software solutions a company uses, such as enterprise resource planning (ERP) systems, customer management software, and cloud services. Understanding a company's technology stack is essential to evaluating its digital maturity and operational needs.

• Deployment Status: This field indicates whether the technology is currently deployed, planned for future deployment, or under evaluation. Vendors can leverage this information to assess technology adoption and identify opportunities among companies in Laos.

• Industry Sector: This field specifies the industry in which the company operates, such as agriculture, mining, or retail. Knowledge of the industry helps vendors tailor their products to sector-specific demands and emerging trends in Laos.

• Geographic Location: This field identifies the company's headquarters or primary operations within Laos. Geographic information is crucial for regional analysis and understanding localized technology adoption patterns across the country.

Top 5 Technology Trends in Laos

• Agricultural Technology: As agriculture is a key sector in Laos, businesses are increasingly adopting digital tools like smart farming technologies, irrigation systems, and crop monitoring software to enhance productivity and sustainability.

• Renewable Energy Technologies: Laos is harnessing its natural resources, particularly hydropower, to meet growing energy demands. There is increasing interest in solar power and other renewable energy solutions to diversify the energy mix.

• E-commerce and Digital Payments: The rapid rise of e-commerce is transforming Laos, with businesses embracing digital payment gateways, online marketplaces, and mobile banking services to reach a broader consumer base.

• Telecommunications and Connectivity: With growing internet penetration, telecommunications providers in Laos are expanding their infrastructure, introducing high-speed internet services, and deploying 4G and 5G technologies.

• Cloud Computing: Cloud-based solutions are becoming popular in Laos, particularly among businesses seeking cost-effective IT infrastructure to support operations in education, finance, and healthcare sectors.

Top 5 Companies with Notable Technographic Data in Laos

• BCEL Bank (Banque Pour Le Commerce Extérieur Lao): A leader in digital banking in Laos, BCEL is enhancing its offerings with online banking services, mobile apps, and robust cybersecurity solutions to meet growing consumer demands.

• Lao Telecom: As one of the largest telecom providers in Laos, Lao Telecom is expanding its digital infrastructure by investing in high-speed internet, 4G/5G networks, and data centers to support the country’s connectivity needs.

• Électricité du Laos (EDL): The primary electricity provider in Laos, EDL is investing in renewable energy projects such as hydropower and solar to meet the country’s sustainable energy goals and reduce dependency on traditional energy sources.

• Unitel Laos: A key player in the telecommunications space, Unitel is advancing mobile and internet services across the country, playing a critical role in improving digital connectivity for businesses and individuals.

• Lao Brewery Co. Ltd: One of the largest beverage companies in the country, Lao Brewery is adopting advanced manufacturing technologies and supply chain management systems to optimize production and distribution.

Accessing Techsalerator’s Business Technographic Data

For those interested in accessing Techsalerator’s Business Technographic Data for Laos, please contact info@techsalerator.com with your specific needs. Techsalerator offers customized quotes based on the required number of data fields and records, with datasets available for delivery within 24 hours. Ongoing access options can also be arranged upon request.

Included Data Fields

• Company Name
• Technology Stack
• Deployment Status
• Industry...

Days-of-Inventory-On-Hand-Turnover 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.

This dataset contains the raw survey responses and the PLS-SEM output used in a study examining the factors influencing the continued use of mobile banking services in Indonesia. A total of 509 responses were collected through an online questionnaire using a convenience sampling method. The constructs were based on the UTAUT and Status Quo Bias models within the Dual Factor Theory (DFT) framework. The dataset includes both the original response data and the structural model output files used for hypothesis testing.

Mobile payment systems have experienced rapid growth, but accurate forecasting remains challenging due to market dynamics and complex adoption factors. This paper proposes a Hybrid ARIMA-LSTM-Transformer model that combines time series forecasting, sequential learning, and attention mechanisms to address these challenges. Experimental results across five datasets demonstrate our model’s superior performance with MAE of 0.075, RMSE of 0.121, and R2 score of 0.948, outperforming traditional approaches. The model’s high accuracy and adaptability make it valuable for real-world applications in digital economy planning and mobile payment market analysis.

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.

Techsalerator’s Business Technographic Data for Vietnam: Unlocking Insights into Vietnam's Technology Landscape

Techsalerator’s Business Technographic Data for Vietnam provides a detailed and comprehensive dataset essential for businesses, market analysts, and technology vendors seeking to understand and engage with companies operating within Vietnam. This dataset offers in-depth insights into the technological landscape, capturing and organizing data related to technology stacks, digital tools, and IT infrastructure used by businesses in the country.

Please reach out to us at info@techsalerator.com or visit Techsalerator Contact.

Top 5 Most Utilized Data Fields

• Company Name: This field lists the names of companies in Vietnam, enabling technology vendors to target potential clients and allowing analysts to assess technology adoption trends within specific businesses.

• Technology Stack: This field outlines the technologies and software solutions a company uses, such as accounting systems, customer management software, and cloud services. Understanding a company's technology stack is key to evaluating its digital maturity and operational needs.

• Deployment Status: This field indicates whether the technology is currently deployed, planned for future deployment, or under evaluation. Vendors can use this information to assess the level of technology adoption and interest among companies in Vietnam.

• Industry Sector: This field specifies the industry in which the company operates, such as manufacturing, retail, or finance. Knowing the industry helps vendors tailor their products to sector-specific demands and emerging trends in Vietnam.

• Geographic Location: This field identifies the company's headquarters or primary operations within Vietnam. Geographic information aids in regional analysis and understanding localized technology adoption patterns across the country.

Top 5 Technology Trends in Vietnam

• E-commerce Expansion: With a rapidly growing digital consumer base, Vietnamese companies are increasingly investing in e-commerce platforms, digital marketing, and online payment systems to capture a larger market share and enhance customer experience.

• Fintech Innovations: Vietnam’s fintech sector is experiencing significant growth, with businesses adopting advanced financial technologies such as mobile payment solutions, digital wallets, and blockchain to improve financial transactions and services.

• Smart Manufacturing: The manufacturing sector in Vietnam is embracing Industry 4.0 technologies, including automation, IoT, and AI-driven analytics, to enhance productivity, efficiency, and competitiveness in the global market.

• Cloud Computing and SaaS: Cloud-based solutions and Software-as-a-Service (SaaS) offerings are gaining traction, providing Vietnamese businesses with scalable and flexible IT infrastructure that supports remote work and digital transformation initiatives.

• Cybersecurity Enhancements: As digital activities increase, so does the need for robust cybersecurity measures. Companies in Vietnam are investing in advanced security solutions, including threat detection systems and data protection tools, to safeguard their operations and customer data.

Top 5 Companies with Notable Technographic Data in Vietnam

• Vietcombank: A leading financial institution, Vietcombank is implementing cutting-edge digital banking solutions, including mobile banking apps and secure online transaction systems, to enhance customer service and operational efficiency.

• Vingroup: As a major conglomerate, Vingroup leverages advanced technologies across its diverse business segments, including real estate, retail, and healthcare, integrating smart technologies and digital platforms into its operations.

• FPT Corporation: A major IT services and software development company, FPT is at the forefront of digital transformation in Vietnam, offering solutions in cloud computing, AI, and cybersecurity to both domestic and international clients.

• Masan Group: A leading consumer goods and retail company, Masan Group is adopting digital tools and e-commerce platforms to optimize its supply chain, enhance customer engagement, and drive business growth.

• VNPT: Vietnam’s largest telecommunications provider, VNPT is expanding its network infrastructure and investing in advanced technologies such as 5G and IoT to improve connectivity and support the digital economy.

Accessing Techsalerator’s Business Technographic Data

For those interested in accessing Techsalerator’s Business Technographic Data for Vietnam, please contact info@techsalerator.com with your specific needs. Techsalerator offers customized quotes based on the required number of data fields and records, with datasets available for delivery within 24 hours. Ongoing access ...

Time Series Database for Financial Services Market Outlook

As per our latest research, the global Time Series Database for Financial Services market size in 2024 reached USD 1.85 billion, demonstrating robust growth driven by the increasing adoption of real-time analytics and data-driven decision-making in the financial sector. The market is expected to expand at a CAGR of 13.2% from 2025 to 2033, reaching a forecasted value of USD 5.44 billion by 2033. The primary growth factor for this market is the escalating volume of financial transactions and the growing need for high-frequency data analysis, which is crucial for risk management, fraud detection, and algorithmic trading across global financial institutions.

One of the most significant growth drivers for the Time Series Database for Financial Services market is the exponential rise in digital transactions and the proliferation of fintech solutions. Financial institutions are increasingly leveraging time series databases to process and analyze vast streams of transactional data in real time. This capability is essential for supporting complex applications such as algorithmic trading, which relies on millisecond-level data precision to execute trades and manage portfolios efficiently. The surge in mobile banking, online payments, and digital wallets has further amplified the demand for scalable and high-performance databases that can handle the velocity, volume, and variety of financial data generated every second. As financial services become more digitized, the need for robust data infrastructure continues to intensify, propelling the market forward.

Another critical factor fueling market growth is the regulatory environment and the increasing emphasis on compliance and risk management. Financial institutions are under mounting pressure to comply with stringent regulations imposed by global authorities, which necessitate comprehensive data tracking, auditing, and reporting capabilities. Time series databases offer an efficient way to store and retrieve historical data, making it easier for banks, investment firms, and insurance companies to demonstrate compliance and quickly respond to regulatory inquiries. Moreover, the integration of advanced analytics and artificial intelligence with time series databases enables organizations to detect anomalies, predict risks, and automate compliance workflows, thereby reducing operational costs and mitigating potential penalties.

Technological advancements and the rise of cloud computing are also pivotal in shaping the growth trajectory of the Time Series Database for Financial Services market. Cloud-based deployment models have democratized access to high-performance databases, enabling even small and medium-sized enterprises to leverage sophisticated data management capabilities without significant upfront investments. The scalability, flexibility, and cost-efficiency offered by cloud solutions are attracting a diverse range of financial service providers, from traditional banks to innovative fintech startups. Furthermore, the integration of time series databases with big data platforms and machine learning tools is unlocking new opportunities for real-time analytics, personalized financial services, and predictive modeling, all of which contribute to the sustained expansion of the market.

From a regional perspective, North America continues to dominate the global Time Series Database for Financial Services market, accounting for the largest revenue share in 2024. This leadership position is attributed to the presence of major financial hubs, advanced IT infrastructure, and early adoption of cutting-edge technologies by leading banks and investment firms. However, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid digital transformation, increasing investments in fintech, and the rising adoption of cloud-based solutions in countries such as China, India, and Singapore. Europe is also witnessing substantial growth, supported by stringent regulatory frameworks and the increasing focus on data-driven financial services. Latin America and the Middle East & Africa are gradually catching up, with financial institutions in these regions investing in modern database solutions to enhance operational efficiency and customer experience.

In the evolving landscape of financial services, <a href="https://growthmarketreports.com/report/managed-temporal-services-market" target="_blank&

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.

Change-In-Cash Time Series for Hengbao Co Ltd. Hengbao Co.,Ltd. engages in the financial technology, Internet of Things, digital security and digital services in China and internationally. The company offers card payment and security services; blockchain finance, digital asset wallet, and supply chain financial platform; mobile communications and the internet of things, including sim, esim, and M2M card. It also provides smart password key, electronic token, Bluetooth USBKEY, mobile smart terminal mobile phone shield, and other products; and tax security products, such as tax control disk, transmission disks, tax control Ukey, and PSAM cards. In addition, the company offers bluetooth secure reading and writing terminal, mPOS, mobile POS, and face recognition payment terminal; and one card cloud and mobile payment platform services. Further, it serves its products to financial industry, communications industry, mobile payment cloud platform, automotive, and composite material solutions. The company was founded in 1996 and is headquartered in Danyang, China.

Payment Fraud Detection AI Market Outlook

According to our latest research, the global Payment Fraud Detection AI market size reached USD 9.8 billion in 2024, demonstrating robust momentum driven by rapid digital transformation and increasing sophistication of fraudulent activities. The market is projected to expand at a CAGR of 19.2% from 2025 to 2033, reaching a forecasted value of USD 46.1 billion by 2033. This remarkable growth is primarily fueled by the urgent need for advanced, real-time fraud detection solutions as organizations face escalating threats in online transactions and digital payments.

One of the most significant growth factors propelling the Payment Fraud Detection AI market is the exponential rise in online transactions and digital payment channels, particularly in the wake of the global shift toward cashless economies. As consumers and businesses increasingly embrace e-commerce, mobile banking, and contactless payments, the volume and complexity of digital transactions have surged. This expansion has inadvertently created a fertile ground for sophisticated cybercriminals, compelling financial institutions, retailers, and payment processors to invest heavily in AI-powered fraud detection technologies. These solutions leverage machine learning and advanced analytics to identify anomalous patterns, adapt to evolving fraud tactics, and provide real-time alerts, thereby minimizing financial losses and enhancing consumer trust.

Another pivotal driver is the regulatory landscape, which is becoming increasingly stringent regarding data security and consumer protection. Governments and regulatory bodies across the globe are enforcing stricter compliance standards, such as the General Data Protection Regulation (GDPR) in Europe and the Payment Card Industry Data Security Standard (PCI DSS) worldwide. These regulations mandate robust fraud prevention mechanisms, pushing organizations to adopt state-of-the-art AI-driven detection systems. The ability of AI algorithms to process vast datasets, recognize subtle fraud indicators, and automate risk assessment processes positions them as indispensable tools in achieving regulatory compliance while maintaining operational efficiency.

Additionally, the evolution of artificial intelligence itself is accelerating adoption rates. Modern AI models, particularly those utilizing deep learning and neural networks, are capable of handling complex, high-volume datasets typical of payment ecosystems. These technologies not only enhance detection accuracy but also reduce false positives, which have historically been a challenge for traditional rule-based systems. The integration of AI with other emerging technologies, such as blockchain and behavioral biometrics, further amplifies the effectiveness of fraud prevention strategies, enabling a proactive rather than reactive approach to payment security.

From a regional perspective, North America continues to dominate the Payment Fraud Detection AI market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The region's leadership is attributed to its advanced digital infrastructure, high penetration of digital payment platforms, and a mature regulatory environment. However, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization in emerging economies, increasing e-commerce activities, and heightened awareness of cybersecurity threats. Latin America and the Middle East & Africa are also experiencing steady adoption, albeit at a relatively nascent stage, as financial inclusion initiatives and mobile payment adoption gather pace.

Component Analysis

The Payment Fraud Detection AI market by component is segmented into Software and Services, both of which play critical roles in the deployment and operation of advanced fraud detection systems. Software solutions form the backbone of this market, encompassing a wide array of products such as fraud analytics platforms, anomaly detection engines, and real-time risk assessment tools. These software offerings are designed to seamlessly integrate with existing payment infrastructures, leveraging machine learning algorithms to monitor transactions, detect suspicious activities, and automate response mechanisms. The continuous evolution of AI software, particularly the adoption of deep learning and natural language processing, is enabling organizations to stay ahead of increasingly sophisticated fra

Key characteristics of the five datasets used in this study.

Mobile payments and household consumption by income group.

Current-Ratio Time Series for Hengbao Co Ltd. Hengbao Co.,Ltd. engages in the financial technology, Internet of Things, digital security and digital services in China and internationally. The company offers card payment and security services; blockchain finance, digital asset wallet, and supply chain financial platform; mobile communications and the internet of things, including sim, esim, and M2M card. It also provides smart password key, electronic token, Bluetooth USBKEY, mobile smart terminal mobile phone shield, and other products; and tax security products, such as tax control disk, transmission disks, tax control Ukey, and PSAM cards. In addition, the company offers bluetooth secure reading and writing terminal, mPOS, mobile POS, and face recognition payment terminal; and one card cloud and mobile payment platform services. Further, it serves its products to financial industry, communications industry, mobile payment cloud platform, automotive, and composite material solutions. The company was founded in 1996 and is headquartered in Danyang, China.

Minority-Interest-Expense Time Series for Hengbao Co Ltd. Hengbao Co.,Ltd. engages in the financial technology, Internet of Things, digital security and digital services in China and internationally. The company offers card payment and security services; blockchain finance, digital asset wallet, and supply chain financial platform; mobile communications and the internet of things, including sim, esim, and M2M card. It also provides smart password key, electronic token, Bluetooth USBKEY, mobile smart terminal mobile phone shield, and other products; and tax security products, such as tax control disk, transmission disks, tax control Ukey, and PSAM cards. In addition, the company offers bluetooth secure reading and writing terminal, mPOS, mobile POS, and face recognition payment terminal; and one card cloud and mobile payment platform services. Further, it serves its products to financial industry, communications industry, mobile payment cloud platform, automotive, and composite material solutions. The company was founded in 1996 and is headquartered in Danyang, China.

Change-Receivables Time Series for Hengbao Co Ltd. Hengbao Co.,Ltd. engages in the financial technology, Internet of Things, digital security and digital services in China and internationally. The company offers card payment and security services; blockchain finance, digital asset wallet, and supply chain financial platform; mobile communications and the internet of things, including sim, esim, and M2M card. It also provides smart password key, electronic token, Bluetooth USBKEY, mobile smart terminal mobile phone shield, and other products; and tax security products, such as tax control disk, transmission disks, tax control Ukey, and PSAM cards. In addition, the company offers bluetooth secure reading and writing terminal, mPOS, mobile POS, and face recognition payment terminal; and one card cloud and mobile payment platform services. Further, it serves its products to financial industry, communications industry, mobile payment cloud platform, automotive, and composite material solutions. The company was founded in 1996 and is headquartered in Danyang, China.