Success.ai’s Company Financial Data for Banking & Capital Markets Professionals in the Middle East offers a reliable and comprehensive dataset designed to connect businesses with key stakeholders in the financial sector. Covering banking executives, capital markets professionals, and financial advisors, this dataset provides verified contact details, decision-maker profiles, and firmographic insights tailored for the Middle Eastern market.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach and strategic initiatives are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution empowers your organization to build meaningful connections in the region’s thriving financial industry.

Why Choose Success.ai’s Company Financial Data?

1. Verified Contact Data for Financial Professionals

   • Access verified email addresses, direct phone numbers, and LinkedIn profiles of banking executives, capital markets advisors, and financial consultants.
   • AI-driven validation ensures 99% accuracy, enabling confident communication and minimizing data inefficiencies.

2. Targeted Insights for the Middle East Financial Sector

   • Includes profiles from major Middle Eastern financial hubs such as Dubai, Riyadh, Abu Dhabi, and Doha, covering diverse institutions like banks, investment firms, and regulatory bodies.
   • Gain insights into region-specific financial trends, regulatory frameworks, and market opportunities.

3. Continuously Updated Datasets

   • Real-time updates reflect changes in leadership, market activities, and organizational structures.
   • Stay ahead of emerging opportunities and align your strategies with evolving market dynamics.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible data usage and compliance with legal standards.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with decision-makers and professionals in banking, investment management, and capital markets across the Middle East.
• 30M Company Profiles: Access detailed firmographic data, including organization sizes, revenue ranges, and geographic footprints.
• Leadership Contact Information: Connect directly with CEOs, CFOs, risk managers, and regulatory professionals driving financial strategies.
• Decision-Maker Insights: Understand key decision-makers’ roles and responsibilities to tailor your outreach effectively.

Key Features of the Dataset:

1. Decision-Maker Profiles in Banking & Capital Markets

   • Identify and connect with executives, portfolio managers, and analysts shaping investment strategies and financial operations.
   • Target professionals responsible for compliance, risk management, and operational efficiency.

2. Advanced Filters for Precision Targeting

   • Filter institutions by segment (retail banking, investment banking, private equity), geographic location, revenue size, or workforce composition.
   • Tailor campaigns to align with specific financial needs, such as digital transformation, customer retention, or risk mitigation.

3. Firmographic and Leadership Insights

   • Access detailed firmographic data, including company hierarchies, financial health indicators, and service specializations.
   • Gain a deeper understanding of organizational structures and market positioning.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and enhance engagement outcomes.

Strategic Use Cases:

1. Sales and Lead Generation

   • Offer financial technology solutions, consulting services, or compliance tools to banking institutions and investment firms.
   • Build relationships with decision-makers responsible for vendor selection and financial strategy implementation.

2. Market Research and Competitive Analysis

   • Analyze trends in Middle Eastern banking and capital markets to guide product development and market entry strategies.
   • Benchmark against competitors to identify market gaps, emerging niches, and growth opportunities.

3. Partnership Development and Vendor Evaluation

   • Connect with financial institutions seeking strategic partnerships or evaluating service providers for operational improvements.
   • Foster alliances that drive mutual growth and innovation.

4. Recruitment and Talent Solutions

   • Engage HR professionals and hiring managers seeking top talent in finance, compliance, or risk management.
   • Provide staffing solutions, training programs, or workforce optimization tools tailored to the financial sector.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality financial data at competitive prices, ensuring strong ROI for your outreach, marketing, and partners...

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.

Pakistan Mobile Phone Banking Transactions: Volume: Misc. Payments data was reported at 1.200 Unit mn in Dec 2019. This records an increase from the previous number of 1.100 Unit mn for Sep 2019. Pakistan Mobile Phone Banking Transactions: Volume: Misc. Payments data is updated quarterly, averaging 0.250 Unit mn from Sep 2016 (Median) to Dec 2019, with 14 observations. The data reached an all-time high of 1.200 Unit mn in Dec 2019 and a record low of 0.000 Unit mn in Mar 2017. Pakistan Mobile Phone Banking Transactions: Volume: Misc. Payments data remains active status in CEIC and is reported by State Bank of Pakistan. The data is categorized under Global Database’s Pakistan – Table PK.KA017: Payment System Statistics: Transactions. [COVID-19-IMPACT]

Vector Databases for Banking AI Market Outlook

According to our latest research, the global market size for Vector Databases for Banking AI stood at USD 1.27 billion in 2024, reflecting the rapid adoption of advanced database solutions within the financial sector. The market is projected to expand at a robust CAGR of 23.9% from 2025 to 2033, reaching an estimated value of USD 10.85 billion by 2033. This exceptional growth is primarily driven by the increasing integration of artificial intelligence and machine learning technologies in banking operations, which necessitate efficient, scalable, and high-performance data storage and retrieval systems. The demand for real-time analytics, fraud detection, and personalized banking experiences are further catalyzing the adoption of vector databases across global banking institutions.

The surge in digital transformation initiatives within the banking sector is a key growth factor for the Vector Databases for Banking AI market. As banks strive to enhance operational efficiency, improve customer engagement, and streamline compliance processes, there is a growing reliance on AI-powered solutions that require robust data infrastructure. Vector databases, with their ability to handle high-dimensional data and support complex similarity searches, are being increasingly deployed to support use cases such as fraud detection, risk management, and customer analytics. The proliferation of digital payment systems, mobile banking, and online transactions has further amplified the need for scalable data platforms capable of processing vast volumes of unstructured and semi-structured data in real time, thereby fueling market expansion.

Another significant driver is the escalating sophistication of financial crimes and the corresponding need for advanced security measures. Banks and financial institutions are leveraging AI-driven vector databases to detect anomalies, identify suspicious patterns, and mitigate risks associated with fraudulent activities. These databases enable rapid analysis of transactional data, behavioral patterns, and network relationships, empowering banks to respond proactively to emerging threats. Moreover, regulatory pressures and compliance requirements are prompting banks to invest in technologies that can ensure data integrity, traceability, and transparency, all of which are facilitated by modern vector database architectures. The integration of vector databases with AI models not only enhances the accuracy of fraud detection but also reduces false positives, leading to improved operational outcomes.

The growing emphasis on personalized banking services and customer-centric strategies is also propelling the adoption of vector databases in the banking AI landscape. Financial institutions are increasingly utilizing AI algorithms to segment customers, predict needs, and deliver tailored product recommendations. Vector databases play a crucial role in enabling these capabilities by providing efficient storage and retrieval of high-dimensional customer data, facilitating real-time analytics, and supporting natural language processing applications. As competition intensifies in the banking industry, the ability to leverage data-driven insights for customer acquisition, retention, and cross-selling is becoming a key differentiator, thereby driving further investments in vector database technologies.

From a regional perspective, North America currently dominates the Vector Databases for Banking AI market, owing to the presence of leading financial institutions, advanced technological infrastructure, and a strong focus on innovation. Europe and Asia Pacific are also witnessing significant growth, driven by rising digitalization, increasing adoption of AI in banking, and favorable regulatory environments. Emerging markets in Latin America and the Middle East & Africa are gradually catching up, supported by government initiatives to promote financial inclusion and digital banking. While regional dynamics vary, the overarching trend is a global shift towards data-centric banking operations, underpinned by the adoption of next-generation database solutions.

Component Analysis

The Component segment of the Vector Databases for Banking AI market is bifurcated into Software and Services, each playing a pivotal role in shaping the market landscape. The software segment primarily includes vector database management systems, data indexing engines, and integrat

Product Analytics for Digital Banking Market Outlook

According to our latest research, the global market size for Product Analytics for Digital Banking reached USD 2.74 billion in 2024, with a robust CAGR of 17.9% anticipated during the forecast period. By 2033, the market is projected to reach USD 9.42 billion, reflecting the accelerating adoption of advanced analytics tools in digital banking environments. The primary growth factor driving this expansion is the increasing demand for personalized customer experiences and data-driven decision-making, as financial institutions strive to differentiate themselves in a highly competitive, digital-first marketplace.

Several key growth drivers are propelling the Product Analytics for Digital Banking Market forward. The rapid digital transformation across the banking sector is compelling institutions to adopt sophisticated analytics solutions to understand and optimize every aspect of the customer journey. As customers increasingly interact with banks through digital channels, banks are leveraging analytics to capture granular data on user behaviors, preferences, and pain points. This wealth of data enables banks to tailor offerings, enhance user satisfaction, and drive higher engagement rates. Furthermore, the surge in mobile banking and the proliferation of digital payment platforms are generating vast datasets that require advanced analytics for actionable insights, thereby fueling market growth.

Another significant factor contributing to the marketÂ’s expansion is the growing emphasis on regulatory compliance and risk management. As financial regulations become more stringent, banks are turning to product analytics to ensure adherence to compliance standards and mitigate fraud risks. Analytics platforms help banks detect anomalies, monitor transaction patterns, and flag suspicious activities in real time, which is crucial for maintaining trust and security in digital banking operations. Additionally, the integration of artificial intelligence and machine learning in analytics solutions is enhancing predictive capabilities, allowing banks to anticipate customer needs, reduce churn, and proactively address operational inefficiencies.

The increasing focus on customer retention and lifetime value is also a pivotal growth driver for the Product Analytics for Digital Banking Market. Banks are utilizing analytics to segment customers, identify at-risk accounts, and implement targeted retention strategies. By analyzing feature adoption, engagement metrics, and conversion rates, banks can optimize product offerings and deliver personalized experiences that foster long-term loyalty. The shift from traditional banking to digital-only models, particularly among younger demographics, is intensifying the need for continuous innovation in analytics-driven customer engagement. Consequently, banks are investing heavily in analytics platforms to stay ahead of evolving customer expectations and competitive pressures.

In today's digital banking landscape, Multichannel Analytics is becoming increasingly vital as banks strive to provide a seamless customer experience across various platforms. By integrating data from multiple channels such as mobile apps, online banking, and in-branch interactions, banks can gain a holistic view of customer behavior. This comprehensive insight allows financial institutions to identify trends, optimize customer journeys, and deliver personalized services that meet the evolving needs of their clients. As customers engage with banks through diverse touchpoints, the ability to analyze and leverage this multichannel data becomes a key differentiator in enhancing customer satisfaction and loyalty.

From a regional perspective, North America currently leads the global Product Analytics for Digital Banking Market, accounting for the largest share in 2024. This dominance is attributed to the high adoption rate of digital banking technologies, a mature financial services ecosystem, and significant investments in analytics infrastructure. Europe and Asia Pacific are also witnessing substantial growth, with Asia Pacific expected to register the highest CAGR during the forecast period. The rapid digitalization of banking services in emerging economies, coupled with increasing smartphone penetration and favorable regulatory frameworks, is drivi

Synthetic Data for Banking Customer Analytics Market Outlook

According to our latest research, the global market size for Synthetic Data for Banking Customer Analytics reached USD 1.17 billion in 2024, reflecting robust adoption across the financial sector. The market is poised for remarkable growth, projected to attain USD 8.94 billion by 2033, expanding at a compelling CAGR of 25.1% during the forecast period. This impressive trajectory is underpinned by the accelerating demand for privacy-preserving data solutions, advanced analytics, and regulatory compliance in banking environments.

The exponential growth of the Synthetic Data for Banking Customer Analytics Market is primarily driven by the increasing emphasis on data privacy and security within the banking sector. With stringent data protection regulations such as GDPR and CCPA, banks and financial institutions are under mounting pressure to safeguard customer information while still harnessing the power of data analytics. Synthetic data offers a compelling solution by enabling organizations to generate realistic yet anonymized datasets, thus facilitating advanced analytics and machine learning model development without exposing sensitive customer data. This approach not only ensures compliance with global data privacy laws but also reduces the risk of data breaches, which is a critical concern for financial institutions. The adoption of synthetic data is further accelerated by the growing need for secure data sharing and collaboration between banks, fintech partners, and third-party analytics providers.

Another major growth factor is the rapid digital transformation sweeping through the banking industry. The proliferation of digital banking channels, mobile payments, and online financial services has resulted in an unprecedented surge in data volumes. Banks are increasingly leveraging artificial intelligence and machine learning to extract actionable insights from this data, enabling them to enhance customer experience, optimize operations, and deliver personalized services. Synthetic data plays a pivotal role in this transformation by providing scalable and diverse datasets that can be used to train and validate AI models, especially in scenarios where real-world data is limited, incomplete, or subject to privacy constraints. This capability is particularly valuable for applications such as fraud detection, credit risk analysis, and customer segmentation, where high-quality data is essential for accurate model performance.

The market’s momentum is further fueled by the growing recognition of synthetic data’s value in supporting innovation and accelerating time-to-market for new banking products and services. By enabling banks to simulate various customer behaviors, test new algorithms, and validate compliance frameworks without relying on actual customer records, synthetic data empowers institutions to innovate quickly and securely. This agility is crucial in a highly competitive financial landscape where customer expectations are evolving rapidly and regulatory requirements are constantly changing. Additionally, the integration of synthetic data with advanced analytics platforms and cloud-based deployment models is making it easier for banks of all sizes to adopt and scale these solutions, further driving market growth.

From a regional perspective, North America currently dominates the Synthetic Data for Banking Customer Analytics Market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, is at the forefront of adoption due to its advanced financial infrastructure, robust regulatory environment, and significant investments in AI and data analytics. Europe is also witnessing strong growth, driven by strict data privacy regulations and the increasing digitalization of banking services. Meanwhile, Asia Pacific is emerging as a high-growth region, propelled by rapid fintech innovation, expanding digital banking penetration, and supportive government initiatives aimed at fostering data-driven financial ecosystems. Latin America and the Middle East & Africa are gradually catching up, with increasing awareness and investments in synthetic data solutions for banking analytics.

By 2018, the Malaysian mobile banking services made the third largest amount of banking transactions following credit card and internet banking. Besides, it is significantly contributing to the banking industry by providing greater ease of transactions to the banking consumers. This study examines factors affecting the intention to use and the actual use of mobile banking services in Malaysia. Two main theories of unified theory of acceptance and use of technology – extended version (UTAUT2) and the model of perceived risk are used to propose a modified framework. Using questionnaires, the data are collected from 504 respondents. The data is analyzed by SPSS and PLS-SEM to acquire the findings. The findings of the study reveals that the independent variables can explain 55.3% variance in mobile banking use and 60.3% variance in Intention to use variables. Moreover, it demonstrates that common factors that have affected significantly on the actual use and intention to use of mobile banking are habit, facilitating condition, interface design quality. Whereas Perceived risk and intentional use are found to have significant impacts on only the use of mobile banking, while effort expectancy is found to have only significant impact on only the intention to use.

Pakistan Payment Systems Infrastructure: Mobile Phone Banking Users data was reported at 7,358,548.000 Unit in Dec 2019. This records an increase from the previous number of 6,354,992.000 Unit for Sep 2019. Pakistan Payment Systems Infrastructure: Mobile Phone Banking Users data is updated quarterly, averaging 2,275,378.500 Unit from Jun 2010 (Median) to Dec 2019, with 26 observations. The data reached an all-time high of 7,358,548.000 Unit in Dec 2019 and a record low of 759,382.000 Unit in Sep 2010. Pakistan Payment Systems Infrastructure: Mobile Phone Banking Users data remains active status in CEIC and is reported by State Bank of Pakistan. The data is categorized under Global Database’s Pakistan – Table PK.KA015: Payment System Statistics: Infrastructure.

Success.ai offers a cutting-edge solution for businesses and organizations seeking Company Financial Data on private and public companies. Our comprehensive database is meticulously crafted to provide verified profiles, including contact details for financial decision-makers such as CFOs, financial analysts, corporate treasurers, and other key stakeholders. This robust dataset is continuously updated and validated using AI technology to ensure accuracy and relevance, empowering businesses to make informed decisions and optimize their financial strategies.

Key Features of Success.ai's Company Financial Data:

Global Coverage: Access data from over 70 million businesses worldwide, including public and private companies across all major industries and regions. Our datasets span 250+ countries, offering extensive reach for your financial analysis and market research.

Detailed Financial Profiles: Gain insights into company financials, including revenue, profit margins, funding rounds, and operational costs. Profiles are enriched with key contact details, including work emails, phone numbers, and physical addresses, ensuring direct access to decision-makers.

Industry-Specific Data: Tailored datasets for sectors such as financial services, manufacturing, technology, healthcare, and energy, among others. Each dataset is customized to meet the unique needs of industry professionals and analysts.

Real-Time Accuracy: With continuous updates powered by AI-driven validation, our financial data maintains a 99% accuracy rate, ensuring you have access to the most reliable and up-to-date information available.

Compliance and Security: All data is collected and processed in strict adherence to global compliance standards, including GDPR, ensuring ethical and lawful usage.

Why Choose Success.ai for Company Financial Data?

Best Price Guarantee: We pride ourselves on offering the most competitive pricing in the industry, ensuring you receive unparalleled value for comprehensive financial data.

AI-Validated Accuracy: Our advanced AI algorithms meticulously verify every data point to ensure precision and reliability, helping you avoid costly errors in your financial decision-making.

Customized Data Solutions: Whether you need data for a specific region, industry, or type of business, we tailor our datasets to align perfectly with your requirements.

Scalable Data Access: From small startups to global enterprises, our platform caters to businesses of all sizes, delivering scalable solutions to suit your operational needs.

Comprehensive Use Cases for Financial Data:

1. Strategic Financial Planning:

Leverage our detailed financial profiles to create accurate budgets, forecasts, and strategic plans. Gain insights into competitors’ financial health and market positions to make data-driven decisions.

1. Mergers and Acquisitions (M&A):

Access key financial details and contact information to streamline your M&A processes. Identify potential acquisition targets or partners with verified profiles and financial data.

1. Investment Analysis:

Evaluate the financial performance of public and private companies for informed investment decisions. Use our data to identify growth opportunities and assess risk factors.

1. Lead Generation and Sales:

Enhance your sales outreach by targeting CFOs, financial analysts, and other decision-makers with verified contact details. Utilize accurate email and phone data to increase conversion rates.

1. Market Research:

Understand market trends and financial benchmarks with our industry-specific datasets. Use the data for competitive analysis, benchmarking, and identifying market gaps.

APIs to Power Your Financial Strategies:

Enrichment API: Integrate real-time updates into your systems with our Enrichment API. Keep your financial data accurate and current to drive dynamic decision-making and maintain a competitive edge.

Lead Generation API: Supercharge your lead generation efforts with access to verified contact details for key financial decision-makers. Perfect for personalized outreach and targeted campaigns.

Tailored Solutions for Industry Professionals:

Financial Services Firms: Gain detailed insights into revenue streams, funding rounds, and operational costs for competitor analysis and client acquisition.

Corporate Finance Teams: Enhance decision-making with precise data on industry trends and benchmarks.

Consulting Firms: Deliver informed recommendations to clients with access to detailed financial datasets and key stakeholder profiles.

Investment Firms: Identify potential investment opportunities with verified data on financial performance and market positioning.

What Sets Success.ai Apart?

Extensive Database: Access detailed financial data for 70M+ companies worldwide, including small businesses, startups, and large corporations.

Ethical Practices: Our data collection and processing methods are fully comp...

Net-Income Time Series for Digital China Information Service Co Ltd. Digital China Information Service Group Company Ltd. provides financial technology products and solutions in China and internationally. The company offers consulting, software products, fintech solution implementation, and cloud infrastructure services. It also provides financial software products comprising Sm@rtOneBank, a banking solution that addresses core banking, general ledger, payment, trade finance, credit management, counter system, e-banking, mobile banking, and ECIF requirements of universal, retail, corporate, and digital banks; Sm@rtGalaxy4.0, a cloud native financial PaaS platform used for ecological support for the construction of middle offices, including operation, maintenance, development, and management; Sm@rtGL, a transaction-grade general ledger system featuring concurrency, data volume, and requiring efficiency and flexibility; Sm@rtEMSP, an enterprise microservice platform that serves as a financial architecture software solution for the financial industry offering extensive and flexible reusable capabilities, concentrated/centralized management of basic platform components, and unified technical capability; Sm@rtEnsemble, a core banking system that serves as a banking business processing system; and Sm@rtTeller X, an integrated smart counter system used for human-machine interaction and business scenario capabilities. In addition, the company offers computer system integration; surveying and mapping; software, hardware, and technology development; network optimization; investment management; information technology; and technical services, as well as sells financial equipment. It serves financial institutions, regional institutions, banks, and fintech providers and partners. The company was formerly known as Digital China Information Service Company Ltd. and changed its name to Digital China Information Service Group Company Ltd. in August 2023. Digital China Information Service Group Company Ltd. is headquartered in Beijing, the People's Republic of China.

Malaysia Electronic Payments: Channels: Value: Mobile Banking data was reported at 48,348.663 MYR mn in 2017. This records an increase from the previous number of 33,160.791 MYR mn for 2016. Malaysia Electronic Payments: Channels: Value: Mobile Banking data is updated yearly, averaging 852.126 MYR mn from Dec 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 48,348.663 MYR mn in 2017 and a record low of 4.455 MYR mn in 2005. Malaysia Electronic Payments: Channels: Value: Mobile Banking data remains active status in CEIC and is reported by Bank Negara Malaysia. The data is categorized under Global Database’s Malaysia – Table MY.KA061: Payment System Statistics: Annual.

ABSTRACT Despite the alleged benefits of m-banking, its acceptance has been short of industry expectations. One plausible explanation may be consumers' initial lack of trust in available services. The objective of the study is to investigate the effect of trust in the intention to use m-banking in the Brazilian context, specifically among users of the city of Rio de Janeiro. Therefore, we developed and tested a model that relates trust and its antecedents (familiarity, ease of use, perceived usefulness, safety, privacy and innovativeness) with the intention to use m-banking. We got a sample of 272 users of financial mobile apps and through structural equation modeling the hypotheses were tested. The results confirmed most of the proposed hypotheses, and we found significant relationships between the construct trust and other constructs, which significantly influence the intended use of banking services via m-banking.

NIBSS Fraud Dataset: Nigerian Banking Transactions for ML Research

🚨 The First Comprehensive Nigerian Financial Fraud Detection Dataset

Welcome to the most extensive publicly available dataset for fraud detection research in the Nigerian banking sector. This meticulously crafted synthetic dataset contains 1,000,000 financial transactions specifically calibrated to reflect real Nigerian banking patterns using official NIBSS (Nigerian Interbank Settlement System) 2023 fraud landscape statistics.

🎯 Why This Dataset Matters

Nigeria processes over ₦600 trillion in electronic payments annually (55% growth in 2023), yet sophisticated fraud detection mechanisms remain underdeveloped. This dataset bridges that critical gap by providing researchers, data scientists, and financial institutions with:

• Realistic fraud patterns based on actual NIBSS reported statistics
• Comprehensive feature engineering with 38 carefully designed variables
• Ethical synthetic data ensuring privacy compliance while maintaining statistical validity
• Production-ready framework for immediate implementation in Nigerian banking contexts

📊 Dataset Specifications

Core Statistics

• Total Transactions: 1,000,000 synthetic records
• Fraud Rate: 0.30% (3,000 fraud cases) - calibrated to NIBSS 2023 data
• Features: 38 engineered variables across temporal, behavioral, and risk categories
• Data Quality: 100% complete records, no missing values
• Format: CSV with comprehensive metadata

Real-World Calibration (NIBSS 2023 Ground Truth)

🔬 Feature Engineering Excellence

Temporal Features (12 variables)

• Cyclic Encodings: Hour, day, month, quarter transformations
• Rolling Windows: 24h, 7d, 30d transaction aggregations
• Velocity Metrics: Transaction frequency and acceleration patterns
• Business Hours: Nigerian banking hour indicators

Behavioral Analytics (15 variables)

• Spending Patterns: Amount deviation ratios and statistical z-scores
• Transaction Velocity: Acceleration and deceleration indicators
• Channel Behavior: Multi-channel usage patterns and switching analysis
• Location Consistency: Geographic stability metrics
• Historical Context: Customer transaction history patterns

Risk Assessment (11 variables)

• Composite Risk Scores: Multi-factor risk aggregation
• Merchant Categories: Industry-specific risk classifications
• Channel Risk: Platform-specific fraud indicators
• Anomaly Detection: Statistical outlier identification
• Cross-Channel Correlation: Inter-platform risk assessment

🏆 Proven Research Results

This dataset has been extensively validated through academic research with statistically significant results:

Model Performance (Bootstrap Validated, n=100)

Feature Importance Insights

• Amount vs Mean Ratio: Primary fraud indicator across all models
• 24-Hour Transaction Aggregations: Critical behavioral patterns
• Velocity Metrics: Key anomaly detection features
• Channel-Specific Indicators: Platform risk differentiation

🎓 Academic Rigor

This dataset is the cornerstone of a comprehensive BSc Statistics dissertation (University of Lagos, 2024/2025) that includes:

• Statistical Validation: Bootstrap confidence intervals with 95% significance
• Cross-Validation: 5-fold stratified validation maintaining fraud distribution
• Economic Analysis: Cost-benefit optimization using Nigerian banking cost structures
• Interpretability: SHAP analysis for model transparency
• Reproducibility: Fixed random seeds and comprehensive documentation

💼 Real-World Applications

Immediate Use Cases

• Academic Research: Fraud detection algorithm development and comparison
• Banking Implementation: Production-ready fraud scoring systems
• Fintech Development: Risk assessment for Nigerian payment platforms
• Regulatory Compliance: NIBSS-aligned fraud prevention frameworks
• **Educational ...

Pakistan Mobile Phone Banking Transactions: Volume: Inter-Bank Fund Transfers data was reported at 4.300 Unit mn in Dec 2019. This records an increase from the previous number of 3.400 Unit mn for Sep 2019. Pakistan Mobile Phone Banking Transactions: Volume: Inter-Bank Fund Transfers data is updated quarterly, averaging 1.500 Unit mn from Sep 2016 (Median) to Dec 2019, with 14 observations. The data reached an all-time high of 4.300 Unit mn in Dec 2019 and a record low of 0.300 Unit mn in Sep 2016. Pakistan Mobile Phone Banking Transactions: Volume: Inter-Bank Fund Transfers data remains active status in CEIC and is reported by State Bank of Pakistan. The data is categorized under Global Database’s Pakistan – Table PK.KA017: Payment System Statistics: Transactions. [COVID-19-IMPACT]

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. 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 by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size in Tanzania was 1,008 individuals.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

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 Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

Return-On-Assets Time Series for Industrial and Commercial Bank of China. Industrial and Commercial Bank of China Limited, together with its subsidiaries, provides banking products and services in the People's Republic of China and internationally. It operates through Corporate Banking, Personal Banking, and Treasury Operations segments. The Corporate Banking segment offers financial products and services, such as deposit-taking activities, trade financing, corporate wealth management, custody, and various corporate intermediary services, as well as corporate loans to corporations, government agencies, and financial institutions. Its Personal Banking segment provides financial products and services, including deposit-taking activities, personal loans, card business, personal wealth management services, and various personal intermediary services to individual customers. The Treasury Business segment is involved in the money market transactions, investment securities, and foreign exchange transactions business. It also offers personal and corporate internet and mobile banking; and debit and credit cards. In addition, the company is involved in the fund raising, fund sales, asset management, and other businesses; financial leasing; insurance businesses, such as life, health, and accident insurance, as well as reinsurance; debt-for-equity swaps; and issuance of wealth management products, wealth management advisory, and consulting services. Industrial and Commercial Bank of China Limited was incorporated in 1984 and is headquartered in Beijing, the People's Republic of China.

Pretax-Margin Time Series for Industrial and Commercial Bank of China. Industrial and Commercial Bank of China Limited, together with its subsidiaries, provides banking products and services in the People's Republic of China and internationally. It operates through Corporate Banking, Personal Banking, and Treasury Operations segments. The Corporate Banking segment offers financial products and services, such as deposit-taking activities, trade financing, corporate wealth management, custody, and various corporate intermediary services, as well as corporate loans to corporations, government agencies, and financial institutions. Its Personal Banking segment provides financial products and services, including deposit-taking activities, personal loans, card business, personal wealth management services, and various personal intermediary services to individual customers. The Treasury Business segment is involved in the money market transactions, investment securities, and foreign exchange transactions business. It also offers personal and corporate internet and mobile banking; and debit and credit cards. In addition, the company is involved in the fund raising, fund sales, asset management, and other businesses; financial leasing; insurance businesses, such as life, health, and accident insurance, as well as reinsurance; debt-for-equity swaps; and issuance of wealth management products, wealth management advisory, and consulting services. Industrial and Commercial Bank of China Limited was incorporated in 1984 and is headquartered in Beijing, the People's Republic of China.

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Pakistan Mobile Phone Banking Transactions: Value: Intra-Bank Funds Transfers data was reported at 167.900 PKR bn in Dec 2019. This records an increase from the previous number of 122.200 PKR bn for Sep 2019. Pakistan Mobile Phone Banking Transactions: Value: Intra-Bank Funds Transfers data is updated quarterly, averaging 55.350 PKR bn from Sep 2016 (Median) to Dec 2019, with 14 observations. The data reached an all-time high of 167.900 PKR bn in Dec 2019 and a record low of 8.900 PKR bn in Sep 2016. Pakistan Mobile Phone Banking Transactions: Value: Intra-Bank Funds Transfers data remains active status in CEIC and is reported by State Bank of Pakistan. The data is categorized under Global Database’s Pakistan – Table PK.KA017: Payment System Statistics: Transactions. [COVID-19-IMPACT]

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