*********** received the highest customer satisfaction score among the largest banks in the United States as of 2024, with a score reaching *** points out of 1,000. JPMorgan Chase, the largest U.S. bank, ranked second, and TD Bank and U.S. Bank followed, both above the industry average. Wells Fargo, Bank of America, and Citigroup received the lowest scores. Most important factors for bank customers worldwide According to a survey conducted by Statista among more than ****** bank customers across ** nations worldwide, trust is the most important factor when customers think about their banks. More than half of all respondents highlighted trust as the most important factor. Banks seem to understand this and put focus on increasing their trustworthiness, which can be seen by the high level of customer satisfaction with the trustworthiness of their banks. More in-depth information can be found Statista's global bank customer satisfaction survey. Largest banks in the U.S. There are several aspects to consider when determining the largest banks in the United States, but JPMorgan Chase consistently stands out as a leader. Across key financial metrics, such assets, market capitalization, market share, deposits, revenue, and net income, JPMorgan Chase tops the list. CET1 ratio and total capital ratio seem to be two of the few key performance indicators where JPMorgan Chase did not rank first in 2024.

The "**Banks Reviews Customer Dataset**" boasts a vast collection of over 1000+ data of user-generated reviews and ratings spanning various banks. It serves as a valuable asset for data scientists, providing a comprehensive view of customer satisfaction, regional banking trends, and the underlying factors that shape banking experiences. This dataset empowers researchers and analysts to uncover meaningful insights within the financial industry, all through the lens of genuine customer feedback, facilitating informed decision-making and data-driven strategies for the banking sector.

Key Features

1. Data Scientists: Utilize the dataset for sentiment analysis, uncover customer satisfaction trends, and create data-driven insights for banking industry improvements.
2. Researchers: Explore historical customer feedback, analyze regional banking patterns, and conduct comparative studies to contribute valuable insights to the financial sector.
3. Banking Professionals: Monitor customer reviews and ratings to enhance customer service, identify areas for improvement, and ensure a better banking experience.
4. Policy Analysts: Use the dataset to assess the impact of policies, monitor economic trends, and support evidence-based decision-making for financial regulations.
5. Interdisciplinary Collaboration: Foster collaboration between data scientists, researchers, banking professionals, and policy analysts to conduct comprehensive studies that benefit research, policy development, and the financial industry as a whole.
   Please upvote and show your support if you find this dataset valuable for your research or analysis. Your feedback and contributions help make this dataset more accessible to the Kaggle community. Thank you!

According to a survey conducted by Statista among more than 50,000 consumers across 34 markets worldwide, bank customers in general were satisfied with their banks in 2025. Based on a rating's system of one being very dissatisfied and five being very satisfied, the global average satisfaction score was just below *, a slight decrease from the previous year's findings. Thailand and South Africa had the highest satisfaction scores, with **** and ****, respectively. More in-depth information can be found in the 2025 global bank customer satisfaction survey

Banks employ various strategies to attract and retain their customer base, such as cheap overdrafts, in-credit interest and no withdrawal charges. While the number of new and active customers can be easily observed, customer satisfaction is trickier. Knowing how customers feel about the service received can help banks adjust to the dynamics of an increasingly competitive market. Customer satisfaction for leading banks in the UK According to the Which? customer satisfaction survey, as of November 2024, three digital banks, First Direct, Monzo Bank, and Starling Bank had the highest customer satisfaction score. According to the survey, 83 percent of these banks' customers were satisfied with the banks' services and products, and willing to recommend them to their friends. Investment in selected European countries Among the services that aim at making banking more customer-oriented and effortless is the current account switch service (CASS). CASS allows customers to change their bank account hassle-free, redirecting transactions and transferring payment arrangements. As of the second quarter of 2024, nine out of 20 banks observed increased their customer base following the CASS process. The highest gain-to-loss ratios were recorded by Danske Bank and Santander, gaining respectively 5.29 and 3.27 times more new customers than the ones lost to other banks.

Customers Reviews on Banks ⭐️

The Reviews on Banks Dataset is a comprehensive collection of 20,000 the most recent customer reviews on 48 US banks. This dataset containing diverse reviews on multiple banks, can be useful for sentiment analysis, assessing geographical variations in customer satisfaction, and exploring customer preferences through textual data. Understanding customer sentiments and preferences helps banks improve their services and address any issues raised by… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/customers-reviews-on-banks.

According to a survey conducted by Statista among more than 50,000 consumers across 34 markets worldwide, consumers in general were satisfied with the customer services offered by their banks. According to the respondents, customer service was the third most important aspect of banking. The global customer service satisfaction rating stood at **** out of five, with Indonesia scoring highest at approximately *** index points. More in-depth information can be found in the 2025 global bank customer satisfaction survey

About the Data

Central banks collecting information about customer satisfaction with the services provided by different banks. Also collects the information about the complaints. Bank users give ratings and write reviews about the services on central bank websites. These reviews and ratings help banks evaluate services provided and take necessary action to improve customer service. While ratings are useful to convey the overall experience, they do not convey the context which led a reviewer to that experience. If we look at only the rating, it is difficult to guess why the user rated the service as 4 stars. However, after reading the review, it is not difficult to identify that the review talks about good 'service' and 'experience'.

Data Schema

The data is detailed dump of customer reviews /complaints (~500) of different services at different banks.

• Date - Day of the review posted
• Stars - 1-5 rating for the business
• Reviews - Reviews/Complaints by customers
• Bank Name - Name of the Bank of the customer

What can be done with the data?

The objective of the case study is to analyze customer reviews and predict customer satisfaction with the reviews

According to a survey conducted by Statista, the top four banks among the largest banks in Europe with the highest average customer satisfaction score came from the United Kingdom. HSBC UK and lloyds Bank received the highest satisfaction score from domestic customers, with an average customer satisfaction score of **** each, followed by NatWest and Barclays, with scores of **** and ****, respectively. The banks included in this ranking are the largest banks in Europe in terms of total assets.

In the 2025 customer satisfaction rankings for British banks, online banks dominated the top positions. Monzo emerged as the leader, with ** percent of its customers saying they were likely to recommend the bank to friends and family. Chase and Starling Bank followed closely behind, each achieving a recommendation rate of ***percent.

According to a survey conducted by Statista among more than 50,000 consumers across 34 markets worldwide, consumers in general were most satisfied with the trustworthiness and digital services of their bank. Terms and conditions, which was the third most valued aspect of the banking service, scored the lowest satisfaction rate of **** index points. More in-depth information can be found in the 2025 global bank customer satisfaction survey.

This dataset provides detailed records of digital banking customer support chat sessions, including full transcripts, participant identifiers, timestamps, issue categories, resolution outcomes, and customer satisfaction ratings. Designed for customer experience analysis and automated agent training, it enables deep insights into support interactions, common issues, and service quality trends.

This dataset provides comprehensive performance metrics for individual bank branches, including account activity, transaction volumes, and customer feedback scores over specific reporting periods. It enables detailed analysis of operational efficiency, customer satisfaction, and issue resolution, supporting data-driven management decisions and branch benchmarking.

Dataset Overview for XYZ Multistate Bank:

This dataset is for XYZ Multistate Bank and contains various columns that capture key aspects of customer behavior and attributes. Each column provides valuable insights into the factors influencing customer churn, with the goal of predicting which customers are most likely to leave the bank. Below is an explanation of each column and its relevance to customer retention.

1. RowNumber:
The "RowNumber" column corresponds to the unique record number for each customer entry. It has no impact on the outcome of customer churn but is used to identify and organize data within the dataset. Since it doesn't contain any meaningful information related to customer behavior, it is not relevant for churn prediction and can be excluded in analysis.

2. CustomerId:
The "CustomerId" column consists of randomly generated identifiers for each customer. While this ID helps to uniquely distinguish each customer, it has no impact on the likelihood of a customer leaving the bank. As a categorical feature, it does not contribute to the analysis of churn and can be omitted when building predictive models.

3. Surname:
The "Surname" column holds the last names of customers. Although this information is useful for identification purposes, it does not have a direct relationship with customer churn. Since a customer's surname is not an influencing factor in their decision to stay or leave the bank, it is not considered relevant for churn prediction and can be disregarded.

4. CreditScore:
"CreditScore" is an important variable that can significantly affect customer churn. Customers with higher credit scores are generally considered more financially stable and less likely to leave the bank, as they are less likely to face issues with financial institutions. Therefore, this feature can provide valuable insights into customer retention and should be included in churn analysis.

5. Geography:
"Geography" refers to the geographical location of the customer, which can influence their likelihood of leaving the bank. Customers living in different regions may have varying experiences with the bank’s services, fees, or offerings, making this an important factor to explore. Understanding regional differences helps tailor retention strategies for specific locations and improve overall customer satisfaction.

6. Gender:
"Gender" is an interesting demographic factor to consider in churn prediction. While gender itself may not directly affect the likelihood of a customer leaving, it could correlate with other behavioral patterns or preferences that influence retention. Analyzing gender in combination with other features may reveal potential insights, making it worthwhile to examine as part of the churn model.

7. Age:
The "Age" column is a key factor in understanding customer behavior. Typically, older customers are less likely to churn because they tend to be more established with their financial institutions and may have a greater sense of loyalty. In contrast, younger customers may be more likely to switch banks, especially if they are seeking better services or offers. This feature is essential for predicting churn and should be analyzed in detail.

8. Tenure:
"Tenure" refers to the number of years a customer has been with the bank. Longer-tenured customers are often more loyal and less likely to leave the bank. The correlation between tenure and churn is strong, as established relationships tend to make customers less susceptible to leaving. This is a critical factor for churn prediction and should be given high consideration when modeling customer retention.

9. Balance:
The "Balance" column reflects the amount of money a customer holds in their bank account. Customers with higher balances are typically more invested in the bank and are less likely to leave. In contrast, customers with low balances may be more willing to switch to other financial institutions offering better rates or services. This feature plays a significant role in churn prediction, as financial stakes are directly tied to loyalty.

10. NumOfProducts:
"NumOfProducts" refers to the number of products (e.g., savings accounts, loans, credit cards) that a customer has with the bank. Customers with multiple products are usually more invested in the bank, making them less likely to leave. The greater the number of products, the higher the customer's commitment to the bank, making this feature highly relevant in understanding churn patterns and developing retention strategies.

11. HasCrCard:
"HasCrCard" indicates whether or not a customer holds a credit card with the bank. Having a credit card typically reduces the likelihood of customer churn, as credit cards are a widely used financial product that locks customers into a long-term relatio...

Unlock insights into Moroccan banking customer experiences! 🇲🇦

This dataset contains scraped and cleaned Google Maps reviews for banks across all cities in Morocco. Collected as part of a collaborative student/freelancer project, it’s perfect for sentiment analysis, market research, or academic projects.

What’s Inside?

• 2 Versions:
  • Raw Data: As scraped from Google Maps.
  • Cleaned Data: Filtered to exclude non-bank businesses (e.g., cash services, unrelated entries).
• Columns:
  City, Business Name, Address, Phone Number, Website, Google Map ID, Review Text, Timestamp, Stars.
• Cities Sourced from data.gov.ma: Ensured comprehensive coverage of Moroccan regions.

Methodology:

1. City Identification: Used official data from data.gov.ma to target cities with banks.
2. Search Strategy: Queried “bank in [city name]” on Google Maps to compile business links.
3. Scraping: Extracted business details (name, address, etc.) and latest reviews using Python + Playwright (automation) and BeautifulSoup (parsing).
4. Cleaning: Removed duplicates and non-bank entries for accuracy.

Potential Use Cases:

• 📈 Sentiment Analysis: Analyze customer satisfaction trends.
• 🗺️ Geospatial Visualization: Map bank ratings by city/region.
• 🔍 Competitor Analysis: Compare bank reputations.
• 🎓 Academic Projects: Practice NLP, data cleaning, or visualization.

Tech Stack:

• Python 🐍
• Playwright (for browser automation)
• BeautifulSoup (HTML parsing)
• Pandas (data cleaning)

Why This Dataset?

• First-of-its-kind: Focused on Moroccan banks.
• Ready-to-use: Cleaned version requires minimal preprocessing.
• Transparent: Raw data included for reproducibility.

License: CC0: Public Domain (Free to use, modify, and share).

In 2025, the ***************************************** earned the highest customer satisfaction score for mobile and online banking services among Canada’s largest banks. TD Bank ranked second in mobile app satisfaction, while the Bank of Montreal took second place for online banking satisfaction.

hanshan1988/customers-reviews-on-banks-sampled-pt25 dataset hosted on Hugging Face and contributed by the HF Datasets community

The xls document describes a set of 16659 customer reviews from the Internet regarding the Post Bank. The website banki.ru acted as a data source. We compiled the dataset to monitor the level of trust of bank customers in its banking service. The data presents text reviews for 2013 - 2019 and includes, with or without ratings.

In 2025, Royal Bank of Canada (RBC) had the highest satisfaction score among retail banking customers of Canada's largest and midsize banks. This is according to a survey that was carried out in early 2025 among over 2,500 respondents. RBC received a score of *** (out of 1,000). It was followed by CIBC, with a score of ***. The average ranking of the presented banks amounted to *** points.

This Dataset contains complete data on customer recalls for different banking companies, the data is not clean so before using it you will need to do exploratory data analysis for more complex models. If you are using simpler models you can simply take the column with the stars and the feedback. (You can see my example code with this dataset). Good luck @💯 !!!