In the fourth quarter of 2024, Polish bank PKO BP reached **** million personal bank accounts, maintaining its position as the leader among all banks in Poland. Pekao SA and Santander BP followed with **** and **** million accounts, respectively.

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to Aug 2025 about savings, personal, rate, and USA.

Nearly four in *** relationships in Poland in 2023 did not have a joint bank account, even though there was a joint budget. Eight percent of couples had neither a joint bank account nor a joint household budget.

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.

In 2021, about ** percent of Indians above 15 years owned an account at a bank. This was a significant change from only ** percent in 2011. This growth suggests a move towards financial inclusion of marginalized groups within the country - from women, to the out-of-labor force, less educated and the poor.

Graph and download economic data for Personal Saving (PSAVE) from Q1 1947 to Q2 2025 about savings, personal, GDP, and USA.

Household Saving Rate in the United States decreased to 4.60 percent in August from 4.80 percent in July of 2025. This dataset provides - United States Personal Savings Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Overview

This dataset contains synthetic data representing the operations of a banking collections department. It includes information about customers, their loan details, payment histories, and risk assessments. The dataset is designed for data analysis, machine learning, and visualization tasks.

Dataset Characteristics

• Number of Records: 1,000
• Number of Fields: 18
• File Format: CSV

Column Description Here’s the column description in your requested format:

Fields

1. Customer_ID:
   A unique identifier for each customer (e.g., CUST00001).

2. Name:
   The name of the customer (fictional).

3. Account_Number:
   A unique account number associated with the customer.

4. Account_Type:
   The type of account held by the customer. Possible values:

   • Savings
   • Current
   • Credit

5. Loan_Type:
   The type of loan taken by the customer. Possible values:

   • Home Loan
   • Car Loan
   • Personal Loan

6. Loan_Amount:
   The total loan amount issued to the customer, in the range of $5,000 to $500,000.

7. Outstanding_Amount:
   The remaining balance yet to be paid by the customer.

8. EMI_Amount:
   The monthly installment amount calculated based on the outstanding amount and tenure.

9. Due_Date:
   The date on which the EMI was due.

10. Payment_Status:
    Status of the EMI payment. Possible values:

    • Paid
    • Missed
    • Partially Paid

11. Collection_Agent:
    The name of the agent responsible for collecting dues from the customer.

12. Last_Payment_Date:
    The date when the last payment was made. It may be null if no payment was made.

13. Payment_Delay_Days:
    The number of days by which the payment was delayed. Zero if payments are made on time.

14. Region:
    The geographical region of the customer. Possible values:

    • North
    • South
    • East
    • West

15. Contact_Number:
    The contact number of the customer (fictional).

16. Email:
    The email address of the customer (fictional).

17. Customer_Score:
    A score representing the customer’s creditworthiness, ranging from 300 to 850.

18. Risk_Level:
    A categorical variable indicating the customer’s risk level. Possible values:

    • Low
    • Medium
    • High

Use Cases

This dataset is ideal for: - Predicting payment defaults using machine learning. - Analyzing customer credit behavior. - Visualizing loan repayment patterns by region or loan type. - Assessing the effectiveness of collection agents.

If you need further customization or additional features, let me know!

Explore Thera Bank's customer dataset (Bank.xls) with 5000 entries, revealing insights into demographics and past personal loan campaign responses. Dive into the challenge of optimizing personal loan conversions with a focus on retaining depositors. Kaggle your way through data-driven strategies for Thera Bank's success.

Data Description:

Age: Customer's age in completed years. Experience:Number of years of professional experience. Income: Annual income of the customer in thousands ($000). ZIPCode:Home Address ZIP code. Family:Family size of the customer. CCAvg: Average spending on credit cards per month in thousands ($000). Education: Education Level - 1: Undergrad; 2: Graduate; 3: Advanced/Professional. Mortgage: Value of the house mortgage if any in thousands ($000). Personal Loan: Binary variable indicating whether the customer accepted the personal loan offered in the last campaign. Securities Account: Binary variable indicating whether the customer has a securities account with the bank. CD Account: Binary variable indicating whether the customer has a certificate of deposit (CD) account with the bank. Online: Binary variable indicating whether the customer uses internet banking facilities. CreditCard:Binary variable indicating whether the customer uses a credit card issued by TheraBank.

This dataset provides values for PERSONAL SAVINGS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Data Description: The file Bank.xls contains data on 5000 customers. The data include customer demographic information (age, income, etc.), the customer's relationship with the bank (mortgage, securities account, etc.), and the customer response to the last personal loan campaign (Personal Loan). Among these 5000 customers, only 480 accepted the personal loan that was offered to them in the earlier campaign.

Domain: Banking

Context This case is about a bank (Thera Bank) whose management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio with minimal budget.

Objective The classification goal is to predict the likelihood of a liability customer buying personal loans.

Attribute Information ID : Customer ID Age : Customer's age in completed years Experience : #years of professional experience Income : Annual income of the customer ZIP Code : Home Address ZIP code. Family : Family size of the customer CCAvg : Avg. spending on credit cards per month Education : Education Level. 1. Undergrad 2. Graduate 3. Advanced/Professional Mortgage : Value of house mortgage if any.

Personal Loan : Did this customer accept the personal loan offered in the last campaign?

Securities Account : Does the customer have a securities account with the bank? CD Account : Does the customer have a certificate of deposit (CD) account with the bank? Online : Does the customer use internet banking facilities? Credit card : Does the customer use a credit card issued

About ** percent of the poorest ** percent reported to have accounts in financial institutions across India in 2017. This was a significant increase compared to only ** percent in 2011. Similarly, Indians among the richest ** percent saw about ** percent increase in bank account ownership from 2011 to 2017.

Nearly ***percent of Gen Z bank account holders indicated that they worried about their financial future in the United Kingdom (UK) in the second quarter of 2025, according to Statista Consumer Insights. A slightly higher share of respondents born between 1995 and 2012 indicated that they were well-informed about their financial situation. The share of respondents who expressed interest in new financial topics, such as crypto or NFTs, was relatively low at ** percent.

Roughly a ***** of Gen Z bank account holders indicated that they worried about their financial future in the U.S. in the second quarter of 2025, according to Statista Consumer Insights. On the other hand, over ** percent of the respondents born between 1995 and 2012 indicated that they were well-informed about their financial situation. There was a relatively low share of respondents who expressed interest in new financial topics, such as crypto or NFTs.

Around a ***** of all bank account holders indicated that they were well-informed about their financial situation in the U.S. in the third quarter of 2025, according to Statista's Consumer Insights. In Statista's survey, ** percent of respondents expressed doubts about their financial future. In terms of attitude towards mobile finance, ** percent of the respondents could imagine dealing with financial transactions exclusively via smartphone.

This statistic presents the likelihood of consumers allowing their retail banks to share the current account balance data with third party organizations in the United Kingdom (UK) in 2015. Nearly two thirds of respondents, 63 percent, stated that they were unlikely to share this type of information.

This statistic presents a timeline with the share of adults with a bank or mobile money service account in El Salvador between 2011 and 2021. In 2021, approximately ** percent of the population had an account at a bank or other financial institution, up from ** percent in 2017.

Personal savings in the United States reached a value of 975 billion U.S. dollars in 2024, marking a slight increase compared to 2023. Personal savings peaked in 2020 at nearly 2.7 trillion U.S. dollars. Those figures remained very high until 2021. The excess savings during the COVID-19 pandemic in the U.S. and other countries were the main reason for that increase, as the measures implemented to contain the spread of the virus had an impact on consumer spending. Saving before and after the 2008 financial crisis During the periods of growth and certain economic stability in the pre-2008 crisis period, there were falling savings rates. People were confident the good times would stay and felt comfortable borrowing money. Credit was easily accessible and widely available, which encouraged people to spend money. However, in times of austerity, people generally tend to their private savings due to a higher economic uncertainty. That was also the case in the wake of the 2008 financial crisis. Savings and inflation The economic climate of high inflation and rising Federal Reserve interest rates in the U.S. made it increasingly difficult to save money in 2022. Not only does inflation affect the ability of people to save, but reversely, consumer behavior also affects inflation. On the one hand, prices can increase when the production costs are higher. That can be the case, for example, when the price of West Texas Intermediate crude oil or other raw materials increases. On the other hand, when people have a lot of savings and the economy is strong, high levels of consumer demand can also increase the final price of products.

This statistic presents the share of global internet users who are concerned about criminals hacking into their personal bank accounts as of *************, sorted by region. During the survey period it was found that ** percent of internet users in Europe were concerned about criminals hacking into their personal bank accounts. Overall, ** percent of global internet users agreed with the statement.

Around ********* of all bank account holders indicated that they were well-informed about their financial situation in Finland in the second quarter of 2025, according to Statista's Consumer Insights. At the same time, ***percent of the respondents expressed doubts about their financial future. In terms of attitude toward mobile finance, ** percent of the respondents could imagine dealing with financial transactions excusively via smartphone.