Inequalities in Financial Services

Bank Accounts and Credit Cards

By Dataquest [source]

About this dataset

Explore the world of consumer finance with this dataset from the Consumer Financial Protection Bureau. This data set includes a rich compilation of detailed bank and credit card customer complaints and provides an invaluable insight into customer experiences in the financial sector. With over [number] records spanning across [date stream], this data set is ideal for researchers, policymakers, financial institutions and anyone looking to learn more about consumer finance.

For each record in the dataset, you'll find details such as date received, product name, issue category, consumer complaint narrative, company response to customer enquiries, state origin of complaint (where appropriate) , even tags associated with the complaint. You can also uncover how timely the company responded to customer query usingthe Timely Response? field or whether customers disputed a firm's reply with Consumer Disputed? field. Utilizing all these features along with deep analysis can aid businesses in creating better consumer experiences prepared explainable models on root causes responsible for issues like disputes or late-responses ultimately leadingtoindustrywidepolicy change that benefit customers alike. Enjoyed exploring data? Hop online to check out additional records available at https://www.consumerfinance.gov/data-research/consumer-complaints/#download-the-data . This dataset is released under Public Domain Licensing Info which meant everyone’s free access!

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How to use the dataset

It is important to note that some fields are optional and missing values are expected for those fields due to how many data points had been reported at time of collection. It can be beneficial to list all unrecorded information separately for comparison considerations if relevant for your research needs.

The data points found within this dataset can not only help you explore differences between experiences based on non-similar factors such as age but also broaden understanding into more specific discussions such as identifying racial disparities in access new types of technology like mobile banking applications versus traditional forms like checks or savings accounts. By using this tool along with other sources of information you should be able create a comprehensive picture regarding both individual's differences experiences in addition broader trends applicable across large swaths impacted people on both local and national levels. These findings could then be used potentially lead positive changes into institutions responsible providing us with these services over time alongside continued evaluation if growth has effectively occurred .

Research Ideas

• Identifying states and specific areas with the highest number of financial complaints to target education and awareness initiatives.
• Analyzing trends in complaint investigations to improve customer service response times and accuracy rates.
• Developing a machine learning model that can accurately predict if a company will respond to a financial complaint in a timely manner

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: Bank_Account_or_Service_Complaints.csv | Column name | Description | |:---------------------------------|:------------------------------------------------------------------------------------| | Date received | The date the complaint was received by the CFPB. (Date) | | Product | The type of financial product or service the complaint is related to. (Text) | | Sub-product | The sub-category of the product the complaint is related to. (Text) | | Issue | The issue the consumer is complaining about. (Text) | | Sub-issue | The sub-category of the issue the consumer is complaining about. (Text) | | Consumer complaint narrative | The narrative of the complaint provided by the consumer. (Text) | | Company public response | The public response from the company regarding the complaint. (Text) ...

This statistic shows a ranking of the estimated bank account penetration in 2020 in Latin America and the Caribbean, differentiated by country. The penetration rate refers to the share of the total population.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

At LOL Bank Pvt. Ltd., ensuring the safety and integrity of economic transactions is a top priority. With increasingly more on line transactions and digital banking activities, fraudulent transactions have end up a good sized danger to both the financial institution and its customers. Fraudulent activities, along with unauthorized account get right of entry to, identification robbery, and suspicious transaction patterns, bring about economic losses and harm to patron agree with.

To cope with this developing subject, LOL Bank Pvt. Ltd. Is in search of a strategy to stumble on and save you fraudulent transactions in real time. This includes analyzing ancient transaction records, consisting of account info, transaction quantities, service provider records, and time stamps, to pick out patterns indicative of fraudulent conduct. The intention is to construct a robust fraud detection gadget that may distinguish among legitimate transactions and probably fraudulent ones, with minimal fake positives.

The answer must incorporate device learning algorithms to study from transaction history, allowing the machine to become aware of rising fraud strategies and adapt to evolving threats. The gadget must be able to flag suspicious transactions in real time, providing bank employees with actionable insights to take activate action. By enhancing fraud detection abilities, LOL Bank Pvt. Ltd. Objectives to shield patron belongings, lessen financial losses, and keep its reputation as a secure and honest economic organization.

Here are the information of the columns:

1. Customer_ID: A particular identifier for every customer within the bank's system.
2. Customer_Name: The name of the consumer making the transaction.
3. Gender: The gender of the consumer (e.G., Male, Female, Other). Four. Age: The age of the consumer at the time of the transaction.
4. State: The nation in which the patron resides.
5. City: The metropolis wherein the client is living.
6. Bank_Branch: The specific financial institution branch wherein the consumer holds their account. Eight. Account_Type: The kind of account held with the aid of the customer (e.G., Savings, Checking). Nine. Transaction_ID: A particular identifier for each transaction.
7. Transaction_Date: The date on which the transaction passed off. Eleven. Transaction_Time: The specific time the transaction became initiated.
8. Transaction_Amount: The financial value of the transaction.
9. Merchant_ID: A particular identifier for the merchant worried within the transaction.
10. Transaction_Type: The nature of the transaction (e.G., Withdrawal, Deposit, Transfer).
11. Merchant_Category: The class of the merchant (e.G., Retail, Online, Travel).
12. Account_Balance: The balance of the customer's account after the transaction.
13. Transaction_Device: The tool utilized by the consumer to perform the transaction (e.G., Mobile, Desktop).
14. Transaction_Location: The geographical vicinity (e.G., latitude, longitude) of the transaction.
15. Device_Type: The kind of device used for the transaction (e.G., Smartphone, Laptop).
16. Is_Fraud: A binary indicator (1 or zero) indicating whether or not the transaction is fraudulent or now not.
17. Transaction_Currency: The currency used for the transaction (e.G., USD, EUR).
18. Customer_Contact: The contact variety of the client.
19. Transaction_Description: A brief description of the transaction (e.G., buy, switch).
20. Customer_Email: The e-mail cope with related to the consumer's account.

These column descriptions give a clear expertise of the facts as a way to be used for fraud detection analysis.

Detailed Information

Problem Statement: Fraud Detection in Bank Transactions for LOL Bank Pvt. Ltd.

At LOL Bank Pvt. Ltd., making sure the safety of patron financial transactions is paramount. With the rise of digital banking, the growth in transaction extent has unfolded greater opportunities for fraudulent activities, which could significantly affect the bank's recognition and lead to substantial financial losses. The undertaking is to accurately hit upon and prevent fraud while preserving a continuing banking revel in for clients. The key aspects of this trouble are as follows:

Nature of the Problem:
- Fraudulent transactions encompass unauthorized account get right of entry to, cash laundering, identity robbery, and uncommon transaction styles. - Traditional strategies of fraud detection are regularly reactive, main to behind schedule identity of fraud. - Fraudsters continuously evolve their tactics, making it harder to discover new forms of fraud the use of conventional strategies.

Data Available:
- The dataset includes historic transaction facts, which includes transaction information consisting of: - Transaction ID, ...

Overview This dataset contains 5,000 meticulously generated banking transaction records from 2023 to 2024. It includes essential details such as transaction IDs, amounts, timestamps, payment methods, and customer demographics. Designed with realistic variability, it mimics real-world financial data to provide an authentic experience for financial analysis and machine learning applications.

Features A. Transaction Details: - Transaction_ID: Unique identifier for each transaction. - Transaction_Date: Date and time of the transaction. - Transaction_Amount: Monetary value of the transaction. - Transaction_Type: Type of transaction (Debit or Credit).

B. Customer Information: - Customer_Age: Age of the customer (18–70). - Customer_Gender: Gender of the customer (Male, Female, Others). - Customer_Income: Annual income of the customer. - Account_Balance: Account balance after the transaction.

C. Categorization: - Category: Categorized transactions into relevant sectors such as Food, Transport, Entertainment, Grocery, Electronics, and more.

D. Merchant and Payment Information: - Merchant_Name: The name of the merchant or vendor. - Payment_Method: Method of payment (Credit Card, Debit Card, Cash, Online Transfer, etc.).

E. Additional Details: - City: Location of the transaction (major US cities). - Fraud_Flag: Indicates whether the transaction is flagged as fraudulent. - Transaction_Status: Status of the transaction (Success, Failed, Pending). - Loyalty_Points_Earned: Rewards points earned from the transaction. - Discount_Applied: Boolean indicating if a discount was applied.

This Dataset contains year, region, state and category (individuals, females and others) bank accounts and deposits in scheduled commercial banks

Note: 1. Deposits shown under this table exclude inter-bank deposits.

Bank data is a comprehensive list of phone numbers for businesses and individuals. With bank accounts, ensuring compliance with data privacy regulations. Firstly, it allows businesses to filter the data by account type, transaction history, location, and other criteria, enabling them to identify their target audience for marketing campaigns. Bank data is continuously updated, ensuring the accuracy and relevance of contact information for effective outreach. Moreover, this data enables businesses to connect with potential customers quickly, accelerating business growth and facilitating efficient customer acquisition. In addition, telemarketing campaigns using Bank data are highly effective, as they directly reach individuals with existing relationships with financial institutions, indicating potential interest in their offerings. It is available on List To Data. Bank number databases are essential tools for businesses to gather accurate and reliable data from reputable sources. These databases ensure that data is collected methodically, resulting in high accuracy and validity. Businesses may increase openness and trust by visiting the source URL and verifying the provenance of the content. Regular updates bring new, up-to-date information, preventing obsolete contact information from interfering with business operations. Businesses that use an up-to-date Bank number database may connect with potential customers swiftly and efficiently, saving time and resources. This database is a useful tool for organizations looking to broaden their reach and create long-term client connections. Constant assistance guarantees a smooth and efficient experience, allowing businesses to focus on making real connections and driving company success. It is available on List To Data.

Slovakia SK: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Male: % of Population Aged 15+ data was reported at 85.320 % in 2017. This records an increase from the previous number of 74.286 % for 2014. Slovakia SK: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Male: % of Population Aged 15+ data is updated yearly, averaging 80.332 % from Dec 2011 (Median) to 2017, with 3 observations. The data reached an all-time high of 85.320 % in 2017 and a record low of 74.286 % in 2014. Slovakia SK: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Male: % of Population Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Slovakia – Table SK.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 (male, % 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).

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

Individuals

Universe

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

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this 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. Each eligible household member is listed and the handheld 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 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 household enumeration 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 was 1000.

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 more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, 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 Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

Dataset Description This dataset contains details of various bank transactions. The data includes both debit and credit transactions made using different modes such as card, ATM, and UPI. Each transaction record provides comprehensive information, including the type of transaction, the mode of payment, the amount transacted, the current balance after the transaction, timestamps, and additional details such as narration and transaction ID.

Columns type: The type of transaction (DEBIT or CREDIT). mode: The mode of the transaction (e.g., CARD, ATM, UPI, OTHERS). amount: The amount involved in the transaction. currentBalance: The account balance after the transaction. transactionTimestamp: The timestamp of when the transaction occurred. valueDate: The date the transaction is valued. txnId: A unique identifier for the transaction. narration: A brief description of the transaction. reference: Additional reference information, if any.

Usage This dataset can be used for various analytical purposes, including but not limited to:

• Analyzing spending patterns.
• Studying the frequency and types of transactions.
• Developing predictive models for transaction behaviors.
• Financial and economic research.

Source The dataset is a synthetic creation for educational and analytical purposes. It provides a realistic representation of transaction data typically found in bank statements. It is generated using real data

The population share with a banking account in South Africa was forecast to continuously increase between 2024 and 2029 by in total *** percentage points. After the fifteenth consecutive increasing year, the banking account penetration is estimated to reach ***** percent and therefore a new peak in 2029. Notably, the population share with a banking account of was continuously increasing over the past years.The penetration rate refers to the share of the total population with a bank account.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 *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

We experimentally test the impact of expanding access to basic bank accounts in Uganda, Malawi, and Chile. Over two years, 17 percent, 10 percent, and 3 percent of treatment individuals made five or more deposits, respectively. Average monthly deposits for them were at the 79th, 91st, and 96th percentiles of baseline savings. Survey data show no clearly discernible intention–to–treat effects on savings or any downstream outcomes. This suggests that policies merely focused on expanding access to basic accounts are unlikely to improve welfare noticeably since impacts, even if present, are likely small and diverse.

BackgroundAccess to a bank account is critical for overall participation in social life and an indicator for social integration. Worldwide about 1.7 billion people remain with no access to banking facilities as a form of financial exclusion which represents 31% of the world’s general population. In contrast, in Western countries like Germany, 99% of the general population use bank accounts.MethodsWe conducted an exploratory cross-sectional survey on bank account ownership and bank account access among psychiatric in-patients in a psychiatric hospital in Berlin. Out of 540 participants who were reached for an interview, 486 shared information about bank account ownership and 469 on access.ResultsOut of 486 participants 49 (10.1%) did not own a bank account. Among the remaining 420 participants owning a bank account, 36 (8.3%) did not have direct access to their bank account, but only, e.g., their legal guardian. Regression results found psychosis, intellectual disabilities, a longer treatment duration, as well as being of male gender and a more instable housing status to be significantly associated with a missing bank account or a missing access to one’s bank account.ConclusionsThe lack of bank account ownership and access among this population of psychiatric patients is concerning. The interrelationship between factors of financial exclusion and mental health should be further explored in longitudinal studies. More attention is needed to support people with severe mental illness to be able to access resources associated with financial inclusion.

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

Individuals

Universe

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

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world's population (see Table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this 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. Each eligible household member is listed and the handheld 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 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 household enumeration 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 was 1600.

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 more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, 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 Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

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

Individuals

Universe

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

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world's population (see Table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this 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. Each eligible household member is listed and the handheld 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 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 household enumeration 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 was 1000.

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 more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, 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 Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

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 almost 145,000 people in 139 economies, representing 97 percent of the world’s population. 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. Additionally, phone surveys were not a viable option in 16 economies in 2021, which were then surveyed in 2022.

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.

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.

Abstract

Many of North Volta Rural Bank's customers who are salaried workers, and therefore receive their pay via direct deposit to NVRB, make frequent use of high interest payday loans (temporary overdrafts). As part of a randomized controlled trial, including 245 men and 75 women, NVRB offered a product to these customers in which they commit to having a fixed amount taken directly from their salary and put in a commitment savings account, for an 18-month period.

The key questions this study is designed to answer are (i) How do individuals adjust their finances in response to regular, automated savings withdrawals? (ii) What do they spend the lump sum on? (iii) Are there any long-term impacts of having participated in the commitment savings program on economic activities, savings, debt, or spending behavior? (iv) How are these impacts different for men versus for women?

The baseline survey data collection for this study took place during September and October 2013. This baseline dataset includes data from 318 individuals: 243 men and 75 women.

Geographic coverage

The study sample is comprised of individuals holding bank accounts with North Volta Rural Bank (NVRB), which is a fairly small bank with just eight branches, all of which are in the northern part of Ghana’s Volta Region. NVRB’s eight branches are located in eight communities across five districts: Dambai in Kratchi East District; Abotoase in the Biakoye District; Ayoma, Guaman and Jasikan in Jasikan District; Kedjebi and Papase in Kedjebi District; and Nkwanta in the Nkwanta South District. Study participants reside in predominantly rural communities in the following districts: Jasikan, Kratchi East, Kratchi West, Biakoye, Nkwanta North, Nkwanta South. Additionally, some study participants live in the larger municipalities of Hohoe and Kpando.

Analysis unit

Individuals

Universe

This baseline survey dataset is comprised of 318 individuals who are account-holders with North Volta Rural Bank and who have their salary directly deposited into an NVRB account, and who consented to participate in this baseline survey.

Gender: 243 are men and 75 are women.

Occupation: All are salaried workers. 31 are staff of NVRB, and most of the other 287 individuals are civil servants. Many are also involved in other livelihood activities.

Education: Most study participants are secondary school graduates.

Age: All are between the ages of 18 and 57 at the time of baseline. 18 is the minimum age for holding a bank account with NVRB, and the study sought to exclude individuals who would retire during the study’s two-year duration, and Ghanaian civil servants are required to retire at age 60. At baseline, the average age of study participants was 30.

Residence: All resided in the study area at the time of the baseline survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

Data was collected from individuals using a household survey administered to the study participants. The survey was written and administered in English, and took approximately 90 to 120 minutes to administer.

The questionnaire covers the following: 1 - Respondent ID and survey information 2 - Demographics 3- Questions using a 10-step ladder 4 - Expenditures 5 - Hypothetical use of a hypothetical lump-sum transfer 6 - General questions on finances, financial strain, self-control, and financial literacy 7 - Time preferences 8 - Household roster, including financial support provided to individuals outside of the household 9 - Food security and financial shocks 10 - Housing quality and household assets 11 - Income 12 - Savings 13 - Debt 14 - Intra-household decision-making and preference alignment 15 - Livestock 16 - Land

Cleaning operations

Survey responses were recorded on paper, and were entered using double-data entry and reconciliation.

Data appraisal

A minimum of 10% of survey participants were visited by an independent auditor who completed an audit questionnaire with the household, which was then compared to the household survey for that individual. Discrepancies found during the audit resulted in additional training, guidance, and changes in personnel as needed.

The original dataset contains 1000 entries with 20 categorial/symbolic attributes prepared by Prof. Hofmann.

The dataset utilized comes from a german bank in 2016 collected by Professor Hoffman of the University of Califonia.

In this dataset, each entry represents a person who takes a credit by a bank. Each person is classified as good or bad credit risks according to the set of attributes.

The original dataset required extensive cleaning and variable selection I due to its complicated system of categories and symbols. Several columns are simply ignored, because they were viewed as not important or their descriptions are obscure. The selected attributes are:

• Age (numeric)
• Sex (text: male, female)
• Job (numeric: 0 - unskilled and non-resident, 1 - unskilled and resident, 2 - skilled, 3 - highly skilled)
• Housing (text: own, rent, or free)
• Saving accounts (text - little, moderate, quite rich, rich)
• Checking account (numeric, in DM - Deutsch Mark)
• Credit amount (numeric, in DM)
• Duration (numeric, in month)
• Purpose (text: car, furniture/equipment, radio/TV, domestic appliances, repairs, education, business, vacation/others).

The objective of this analysis is to segment the German bank's customers based on the various factors (variables) available in their database.

The library makes use of the following packages:

• pandas - to manipulate data frames
• numpy - providing linear algebra
• seaborm - to create visualizations
• matplotlib - basic tools for visualizations
• scikit-learn - machine learning library

Conclusion.

The analysis found that the most optimal clusters were 4 as explained below:

Cluster 0 – high mean of credit amount, long duration, younger customers

Cluster 1 – low mean of credit amount, short duration, younger customers

Cluster 2 - low mean of credit amount, short duration, older customers

Cluster 3 - high mean of credit amount, middle-time duration, older customers

Segmenting bank customers through clustering techniques offers significant benefits for both the bank itself and its various stakeholders. Here are some key advantages:

For Banks:

• Improved Customer Targeting and Marketing: Clustering allows banks to identify distinct customer segments with similar characteristics and needs. This enables them to tailor marketing campaigns and product offerings to specific segments, resulting in greater effectiveness and efficiency.
• Enhanced Customer Relationship Management (CRM): By understanding customer segments better, banks can personalize their interactions and communications, fostering stronger relationships and improving customer satisfaction.
• Risk Management: Customer segmentation can help identify high-risk segments, allowing banks to implement strategies to mitigate potential risks, such as fraud or credit defaults.
• Resource Optimization: Banks can allocate resources, such as personnel and marketing budgets, more efficiently by directing them towards segments with the highest potential for profitability.

• New Product Development: By analyzing the needs and preferences of different segments, banks can develop new products and services that cater to their specific requirements, increasing customer loyalty and driving revenue growth.

  For Stakeholders:

• Improved Customer Experience: Segmented communication and personalized offerings lead to a more satisfying and relevant experience for customers, boosting overall satisfaction and trust in the bank.

• Increased Value Perception: By providing products and services aligned with their needs, customers perceive greater value from the bank's offerings, leading to strengthened relationships and increased loyalty.

• Enhanced Financial Inclusion: Customer segmentation can help banks identify underserved segments and develop strategies to offer them tailored financial products and services, promoting greater financial inclusion.

• Improved Regulatory Compliance: By understanding customer behavior and risk profiles better, banks can better comply with regulations and mitigate potential regulatory risks.

  Overall, customer segmentation via clustering empowers banks to make data-driven decisions, optimize their operations, and deliver a more personalized and satisfying experience for their customers. This ultimately leads to increased profitability, stronger stakeholder relationships, and a competitive advantage in the market.

Some additional examples of how customer segmentation can benefit other stakeholders:

• Investors: By analyzing the performance of different customer segments, investors can gain valuable insights into the bank's future growth potential and make informed investment decisions.
• Regulators: Customer segmentation can help regulators identify systemic risks within the financial system and develop targeted policies to maintain financial stability.
• Employees: By understanding the needs an...

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling 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. 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.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in Kazakhstan was 1,000 individuals.

Mode of data collection

Face-to-face [f2f]

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 over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

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.

The values of any financial assets held including both formal investments, such as bank or building society current or saving accounts, investment vehicles such as Individual Savings Accounts, endowments, stocks and shares, and informal savings.