This paper explores patterns of financial transactions at the individual level in order to establish the effects of mobile money’s usage in a variety of country case examples. Data from the Financial Inclusion Insights program was analyzed for Bangladesh, India, Kenya, Nigeria, Pakistan, Tanzania, and Uganda, to establish differences between individuals who use mobile money services and their non-user counterparts. This analysis builds on previous research into the household level effects of the widely popular M-PESA services in Kenya to see if financial transaction patterns can be replicated in other country data. Contrary to previous literature, m-money usership was not a consistent predictor of transaction frequency and transaction distance for the country cases where data was available. To examine m-money’s potential as a complement or substitute to formal banking, usage frequency of bank account services was regressed on m-money usership, which was interacted with personal bank account ownership. Findings suggest that m-money encouraged bank account usage in the country samples where m-money was less prevalent overall, and discouraged bank account usage in the country samples where it was more prevalent. Overall, this study finds considerable difference in the effects of mobile money by country, as well as discrepant effects when interacted with bank account ownership.

The online banking penetration rate in Africa was forecast to continuously increase between 2024 and 2029 by in total 5.2 percentage points. After the fifteenth consecutive increasing year, the online banking penetration is estimated to reach 13.25 percent and therefore a new peak in 2029. Notably, the online banking penetration rate of was continuously increasing over the past years.Shown is the estimated percentage of the total population in a given region or country, which makes use of online banking.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the online banking penetration rate in countries like North America and Europe.

Context

iX Mobile Banking Prediction Challenge

Content

This data was imported from the zindi platform link

The train set contains ~100 000 and the test contains ~45 000 survey responses from around Africa and the world.

Train.csv - contains the target. This is the dataset that you will use to train your model.

Test.csv- resembles Train.csv but without the target-related columns. This is the dataset on which you will apply your model to.

SampleSubmission.csv - shows the submission format for this competition, with the ‘ID’ column mirroring that of Test.csv and the ‘target’ column containing your predictions. The order of the rows does not matter, but the names of the ID must be correct.

VariableDefinitions.csv - A file that contains the definitions of each column in the dataset. For columns(FQ1 - FQ37), Value 1 - Yes, 2 - No, 3 - Don’t Know 4 - refused to answer

Abstract

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

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

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

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

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

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

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

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

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

Sample size for South Africa is 1014.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

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

The African Data for Entrepreneurs Initiative uses household level datasets available at the World Bank to create summary indicators useful for entrepreneurs in Africa creating or growing their businesses. There are two main sources of data: Listening to Africa and National Household Survey Datasets.

Listening to Africa
L2A is a collaboration with national statistical offices and NGOs in sub-Saharan Africa to pilot the use of mobile phones to regularly collect information on living conditions. The approach combines face-to-face surveys with follow up mobile phone interviews to collect data that allows welfare monitoring.

L2A surveys for Senegal, Madagascar, and Malawi were adapted specifically to collect data that would be valuable for entrepreneurs and thus these data have a richer set of indicators.

National Household Survey Datasets
Countries national statistical offices regularly carry out general household surveys to collect information about the living standards of households more broadly. While these datasets are more broadly available, they may have less information that would be particularly relevant for entrepreneurs.

South Africa ZA: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Primary Education Or Less: % of Population Aged 15+ data was reported at 63.998 % in 2017. This records an increase from the previous number of 60.658 % for 2014. South Africa ZA: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Primary Education Or Less: % of Population Aged 15+ data is updated yearly, averaging 60.658 % from Dec 2011 (Median) to 2017, with 3 observations. The data reached an all-time high of 63.998 % in 2017 and a record low of 42.551 % in 2011. South Africa ZA: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Primary Education Or Less: % of Population Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank.WDI: Bank Account Ownership. Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (primary education or less, % of population ages 15+).; ; Demirguc-Kunt et al., 2018, Global Financial Inclusion Database, World Bank.; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Dataset Overview

Introduction

This dataset presents a synthetic representation of mobile money transactions, meticulously crafted to mirror the complexities of real-world financial activities while integrating fraudulent behaviors for research purposes. Derived from a simulator named PaySim, which utilizes aggregated data from actual financial logs of a mobile money service in an African country, this dataset aims to fill the gap in publicly available financial datasets for fraud detection studies. It encompasses a variety of transaction types including CASH-IN, CASH-OUT, DEBIT, PAYMENT, and TRANSFER over a simulated period of 30 days, providing a comprehensive environment for evaluating fraud detection methodologies. By addressing the intrinsic privacy concerns associated with financial transactions, this dataset offers a unique resource for researchers and analysts in the field of financial security and fraud detection, scaled to 1/4 of the original dataset size for efficient use within the Kaggle platform. Please note that transactions marked as fraudulent have been nullified, emphasizing the importance of non-balance columns for fraud analysis. This dataset is a contribution to the field from the "Scalable resource-efficient systems for big data analytics" project, funded by the Knowledge Foundation in Sweden.

Dataset Details

PaySim synthesizes mobile money transactions using data derived from a month's worth of financial logs from a mobile money service operating in an African country. These logs were provided by a multinational company that offers this financial service across more than 14 countries globally.

This synthetic dataset has been scaled to one-quarter the size of the original dataset and is specifically tailored for Kaggle.

Important Note: Transactions identified as fraudulent are annulled. Hence, for fraud detection analysis, the following columns should not be utilized: oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest.

Dataset Structure

• step: Represents a unit of time in the real world, with 1 step equating to 1 hour. The total simulation spans 744 steps, equivalent to 30 days.
• type: Transaction types include CASH-IN, CASH-OUT, DEBIT, PAYMENT, and TRANSFER.
• amount: The transaction amount in the local currency.
• nameOrig: The customer initiating the transaction.
• oldbalanceOrg: The initial balance before the transaction.
• newbalanceOrig: The new balance after the transaction.
• nameDest: The transaction's recipient customer.
• oldbalanceDest: The initial recipient's balance before the transaction. Not applicable for customers identified by 'M' (Merchants).
• newbalanceDest: The new recipient's balance after the transaction. Not applicable for 'M' (Merchants).
• isFraud: Identifies transactions conducted by fraudulent agents aiming to deplete customer accounts through transfers and cash-outs.
• isFlaggedFraud: Flags large-scale, unauthorized transfers between accounts, with any single transaction exceeding 200,000 being considered illegal.

Previous Research and Acknowledgments

This dataset has been generated through multiple runs of the PaySim simulator, each simulating a month of real-time transactions over 744 steps. Each run produced approximately 24 million financial records across the five transaction categories.

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

For citations and further references, please use:

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

ZA: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: % of Population Aged 15+ data was reported at 69.218 % in 2017. This records a decrease from the previous number of 70.317 % for 2014. ZA: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: % of Population Aged 15+ data is updated yearly, averaging 69.218 % from Dec 2011 (Median) to 2017, with 3 observations. The data reached an all-time high of 70.317 % in 2014 and a record low of 53.645 % in 2011. ZA: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: % of Population Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank.WDI: Bank Account Ownership. Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (% age 15+).; ; Demirguc-Kunt et al., 2018, Global Financial Inclusion Database, World Bank.; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

My new report: Mobile Money Fraud Detection - April 2024

Hello dear all.

Mobile money is booming around the world, particularly in Africa. Companies, including telecommunications, are taking advantage of the growth of this technology in order to achieve better turnover.

Populations, for their part, often excluded from standard banking services, see Mobilier Money as an advantageous and efficient means that they use on a daily basis, for various reasons.

While this segment is becoming very important and gradually gaining ground, malicious actors intend to take advantage of it to carry out fraudulent operations. These are operations such as: - Scamming - ⁠The diversion of transfers - account hacking etc…

These malicious activities constitute a growing threat which is a real headache for Mobile Money service users and Mobile Money companies alike.

The solution is actually to verify each transaction, to ensure its authenticity, in order to avoid any fraudulent transactions. It is possible and tenable when you have a portfolio of less than 500,000 users, with thousands of staff taking care of it 24/7.

It becomes impossible to take care of it like this, when we have 6 million, 8, 9 million users, who use Mobile Money services all the time (more than 450,000 transactions per hour), carrying out multiple operations different (transfer, credit, withdrawals, deposit, online payment etc.). And there, monitoring of the regularity of transactions must be automated. And that’s the whole challenge.

Nowadays, companies like Orange and Mtn use sophisticated transaction monitoring algorithms to limit the number of fraudulent transactions. However, the share of fraudulent transactions still remains considerable and the individuals behind the crime linked to Mobile Money are constantly updating their attack techniques, and it is therefore a permanent challenge for mobile money operators, who must review their systems constantly in order to update them.

Today, it is possible to use Machine Learning and Deep Learning (data science) models to systematically detect cases of fraud, including those linked to Mobile Money.

In this report, I develop 5 Machine Learning models and a sixth, Deep Learning, in order to systematically detect cases of fraud linked to Mobile Money. The aim is to use the power of data science to solve a worrying problem. The goal is simple, to anticipate cases of Mobile Money fraud in real time. Via dedicated algorithms.

The data here is made up of more than 6 million lines (observations) and 11 variables, from Mobile Money transactions in an African country.

I continue to train my algorithms (Machine Learning and Deep Learning) in order to refine the quality of their predictions. As is customary.

Enjoy reading and feel free to provide comments.

Etienne Landry

The Central Bank of Kenya compiles a series of statistics of various bank rates ( Forex Exchange Rates, Interbank Rates, Interbank Rates & Volumes, Daily KES Interbank Activity Report, Repo and Reverse Repo , Horizontal Repo Market , Central Bank Rate ) Monthly Diaspora Remittances, Macroeconomic Statistics( Balance of Payment Statistics, Monetary Statistics, Inflation Rates, Government Finance Statistics, National accounts Statistics, Exchange Rates, Interest Rates ) and payment systems statistics (KEPSS/RTGS Statistics, Automated Clearing House, Payment Cards, Mobile Payments), Consumer price indices and others (Central Government Revenue Grants ,Central Government Expenditures Issues of Treasury Bills Issues of Treasury Bonds, Domestic Debt by Instrument, Public Debt, Loan, Lending, Mortgage, Bank interest rates

The set includes data files and Matlab code used in the production of outputs for banking network analysis. The code includes references to the source data files, whose names match the corresponding names in the code. The methodology is described in the PhD thesis, 'Systemic risk in banking and insurance with practical application to South African financial institutions' in Chapters 2-4.The three primary files, GivenSystemArticle_OverTimePhD_sorted_data.m, GivenSystemArticle_Repeats_on_variablesTP2.m and SystematicRiskCalcsPhD2001data_other_quantiles.m call the other Matlab files as sub-routines. All data used in the analysis is publicly available, primarily from the BA900 returns of the South African Reserve Bank (https://www.resbank.co.za/en/home/what-we-do/statistics/releases/banking-sector-information/banks-ba900-economic-returns). CET1 data is derived from the quarterly and annual reports of individual banks.

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

Techsalerator’s Business Technographic Data for Central African Republic: Unlocking Insights into Central African Republic's Technology Landscape

Techsalerator’s Business Technographic Data for the Central African Republic (CAR) offers a comprehensive and detailed collection of information crucial for businesses, market analysts, and technology vendors seeking to understand and engage with companies operating in CAR. This dataset provides an in-depth exploration of the technological landscape, capturing and categorizing data related to technology stacks, digital tools, and IT infrastructure within Central African businesses.

For inquiries, please reach out to us at info@techsalerator.com or visit Techsalerator Contact.

Top 5 Most Utilized Data Fields

1. Company Name: This field lists the names of companies being analyzed in CAR. Understanding the companies helps technology vendors target their solutions effectively and enables market analysts to evaluate technology adoption trends within specific businesses.

2. Technology Stack: This field details the technologies and software solutions a company utilizes, such as ERP systems, CRM software, and cloud services. Knowledge of a company’s technology stack is crucial for understanding its operational capabilities and technology needs.

3. Deployment Status: This field indicates whether the technology is currently in use, planned for deployment, or under evaluation. This status helps vendors gauge the level of interest and current adoption among businesses in CAR.

4. Industry Sector: This field identifies the industry sector in which the company operates, such as telecommunications, agriculture, or trade. Segmenting by industry sector helps vendors tailor their offerings to specific market needs and trends within CAR.

5. Geographic Location: This field provides the geographic location of the company's headquarters or primary operations within CAR. This information is vital for regional market analysis and understanding local technology adoption patterns.

Top 5 Technology Trends in Central African Republic

1. Mobile Technology and Connectivity: Mobile technology is crucial in CAR, where it plays a significant role in communication and financial transactions. Companies are increasingly leveraging mobile platforms for various services, including mobile banking and digital communication.

2. Cybersecurity: With the rise of digital threats, cybersecurity is becoming a priority in CAR. Businesses are focusing on threat detection, data protection, and cybersecurity measures to safeguard against potential risks.

3. Cloud Computing: Cloud computing adoption is growing in CAR, offering businesses flexibility and cost efficiency. This trend is prominent in sectors such as finance and public services, where cloud solutions help improve operational efficiency.

4. E-Government and Digital Services: The CAR government is advancing digital transformation through e-government initiatives. This includes digital service portals and online systems aimed at improving public service delivery and efficiency.

5. Renewable Energy: Given CAR's energy needs and environmental considerations, renewable energy technologies, especially solar power, are gaining attention. These technologies are vital for supporting sustainable development and energy access in remote regions.

Top 5 Companies with Notable Technographic Data in Central African Republic

1. Orange CAR: A leading telecommunications provider in CAR, Orange CAR is prominent in mobile connectivity and digital services, enhancing communication and financial transactions.

2. MTN Central Africa: Another major player in the telecom sector, MTN Central Africa is known for its extensive network coverage and investment in mobile technology and broadband services across CAR.

3. BGFIBank: A significant financial institution, BGFIBank is integrating modern banking technologies, including online banking and mobile apps, to improve customer experience and operational efficiency.

4. Electricity Company of Central Africa (ECA): As the primary electricity provider, ECA is exploring renewable energy solutions and smart grid technologies to improve energy distribution and sustainability in CAR.

5. Sodecoton: Operating in the agriculture sector, Sodecoton is leveraging technology for crop management, supply chain optimization, and other aspects of agricultural production.

Accessing Techsalerator’s Business Technographic Data

For access to Techsalerator’s Business Technographic Data for the Central African Republic, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide a customized quote based on the number of data fields and records needed, with the dataset available for delivery within 24 hours. Ongoing access opt...

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

Techsalerator’s Business Technographic Data for South Africa provides a detailed and comprehensive dataset essential for businesses, market analysts, and technology vendors seeking to understand and engage with companies operating within South Africa. 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 South Africa, 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 South Africa.

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

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

Top 5 Technology Trends in South Africa

• Fintech Innovations: South Africa is experiencing rapid growth in the fintech sector, with companies adopting digital payment solutions, blockchain technology, and mobile banking platforms to enhance financial inclusion and streamline transactions.

• Renewable Energy and Smart Grids: With a growing focus on sustainability, there is an increasing adoption of renewable energy technologies such as solar and wind power, as well as advancements in smart grid technology to improve energy efficiency and reliability.

• E-commerce Growth: The e-commerce sector is expanding rapidly in South Africa, with businesses leveraging online platforms, digital marketing strategies, and payment gateways to cater to a growing online consumer base.

• Cybersecurity Measures: As digital transformation accelerates, so does the need for robust cybersecurity. South African companies are investing in advanced security solutions, threat detection, and data protection measures to safeguard against cyber threats.

• Cloud Computing Adoption: Cloud-based solutions are becoming increasingly popular in South Africa, offering businesses scalable and flexible IT infrastructure options. This trend is particularly evident in sectors like healthcare, finance, and education.

Top 5 Companies with Notable Technographic Data in South Africa

• Standard Bank: A leading financial institution in South Africa, Standard Bank is at the forefront of fintech innovations, implementing advanced digital banking solutions, mobile apps, and cybersecurity measures to enhance customer experience.

• MTN South Africa: As a major telecom provider, MTN is driving connectivity with high-speed internet, mobile services, and investments in 5G technology, contributing to the country's digital infrastructure development.

• Sasol: A global integrated chemicals and energy company, Sasol is incorporating advanced technologies such as AI and IoT into its operations, focusing on innovation and efficiency in the energy sector.

• Shoprite Holdings: A leading retailer in South Africa, Shoprite is leveraging e-commerce platforms and digital solutions to enhance customer engagement, streamline operations, and expand its market reach.

• Discovery Limited: Known for its innovative approach in the insurance and healthcare sectors, Discovery is adopting digital health technologies, data analytics, and telemedicine solutions to improve service delivery and customer experience.

Accessing Techsalerator’s Business Technographic Data

For those interested in accessing Techsalerator’s Business Technographic Data for South Africa, 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 ...

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 163 companies listed on the Kuwait Stock Exchange (XKUW) in Kuwait. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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

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

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

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

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

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

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

Kuwait Stock Exchange (KSE) - Price Index: The main index that tracks the performance of all companies listed on the Kuwait Stock Exchange (KSE), providing insights into the Kuwaiti equity market.

Kuwaiti Dinar (KWD): The official currency of Kuwait. It is widely used for transactions and serves as the backbone of the country's financial system.

National Bank of Kuwait (NBK): The largest and one of the oldest banks in Kuwait, offering a wide range of banking and financial services.

Kuwait Finance House (KFH): A leading Islamic bank in Kuwait, providing Sharia-compliant banking services and products to individuals and businesses.

Zain Group (ZAIN): A telecommunications company based in Kuwait, with operations in multiple countries across the Middle East and North Africa, providing mobile and data services.

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

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Data fields included:

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

Q&A:

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

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

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

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

1. How does Techsalerator collect this data?

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

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

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

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wire transfers, facilitating a convenient and secure payment process.

1. How do I receive the data?

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

Techsalerator’s Business Technographic Data for Kenya offers an extensive and detailed dataset essential for businesses, market analysts, and technology vendors looking to understand and engage with companies operating within Kenya. This dataset provides valuable insights into the technology stacks, digital tools, and IT infrastructure used by Kenyan businesses across various industries.

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Top 5 Most Utilized Data Fields

• Company Name: This field lists the names of companies in Kenya, allowing technology vendors to identify potential clients and enabling analysts to assess technology adoption trends across different sectors.

• Technology Stack: This field highlights the technologies and software solutions companies in Kenya use, including customer management software, cloud services, and ERP systems. Understanding a company’s technology stack is crucial for evaluating its digital capabilities and needs.

• Deployment Status: This field indicates whether the technology is currently deployed, planned for future deployment, or under consideration. Vendors can use this information to gauge the level of technology adoption and readiness among Kenyan companies.

• Industry Sector: This field categorizes companies by industry, such as agriculture, finance, or telecommunications. Knowing the sector helps vendors tailor their products to sector-specific needs and emerging trends in Kenya’s economy.

• Geographic Location: This field identifies the company’s headquarters or primary operations within Kenya. Geographic data is key for analyzing regional technology adoption patterns and targeting growth areas.

Top 5 Technology Trends in Kenya

• Fintech and Mobile Payments: Kenya is a global leader in mobile payments, with platforms like M-Pesa transforming the financial sector. This trend is driving a rapid shift toward digital banking, mobile wallets, and financial inclusion.

• Agritech: With agriculture being a critical part of Kenya’s economy, agritech innovations such as digital platforms for market access, weather prediction tools, and IoT devices for farm management are increasingly adopted to enhance productivity.

• E-commerce and Digital Trade: As online shopping grows, Kenyan businesses are embracing e-commerce platforms, logistics tech, and digital payments to serve local and international markets, fueling digital trade across the region.

• Cybersecurity: With increasing reliance on digital infrastructure, Kenyan companies are investing in cybersecurity solutions such as firewalls, encryption, and secure communication tools to safeguard data and mitigate cyber threats.

• Renewable Energy Technologies: Kenya is at the forefront of renewable energy in Africa, with businesses and industries increasingly turning to solar, wind, and geothermal energy solutions to power operations and reduce carbon footprints.

Top 5 Companies with Notable Technographic Data in Kenya

• Safaricom: Kenya’s leading telecom provider, Safaricom, is a pioneer in mobile technology, driving innovation in mobile payments (M-Pesa), cloud services, and connectivity solutions that shape the country’s digital transformation.

• Equity Bank: As one of the largest financial institutions in East Africa, Equity Bank is heavily invested in fintech solutions, mobile banking apps, and cybersecurity measures to provide seamless and secure banking services.

• KenGen (Kenya Electricity Generating Company): A key player in Kenya’s energy sector, KenGen is advancing renewable energy through geothermal and hydroelectric projects, incorporating cutting-edge technologies to boost efficiency.

• Twiga Foods: A leading agritech company, Twiga Foods uses digital platforms to streamline the supply chain for fresh produce, connecting farmers with retailers and wholesalers through an efficient, tech-enabled distribution network.

• Jumia Kenya: As the leading e-commerce platform in Kenya, Jumia leverages digital marketing, mobile apps, and advanced logistics technology to offer a seamless online shopping experience for both customers and vendors.

Accessing Techsalerator’s Business Technographic Data

For those interested in accessing Techsalerator’s Business Technographic Data for Kenya, 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 Sector
• Geographic Location
• IT Infrastructure
• S...

Central African Republic CF: Account: Female: % Aged 15+ data was reported at 3.412 % in 2011. Central African Republic CF: Account: Female: % Aged 15+ data is updated yearly, averaging 3.412 % from Dec 2011 (Median) to 2011, with 1 observations. The data reached an all-time high of 3.412 % in 2011 and a record low of 3.412 % in 2011. Central African Republic CF: Account: Female: % Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Banking Indicators. Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (female, % age 15+). [ts: data are available for multiple waves].; ; Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank.; Weighted average;

Central African Republic CF: Account: Primary Education or Less: % Aged 15+ data was reported at 1.096 % in 2011. Central African Republic CF: Account: Primary Education or Less: % Aged 15+ data is updated yearly, averaging 1.096 % from Dec 2011 (Median) to 2011, with 1 observations. The data reached an all-time high of 1.096 % in 2011 and a record low of 1.096 % in 2011. Central African Republic CF: Account: Primary Education or Less: % Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Banking Indicators. Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (primary education or less, % age 15+). [ts: data are available for multiple waves].; ; Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank.; Weighted average;