The survey was carried out in the city. Ho Chi Minh City in May-June 2021.

India Mobile Banking Transactions: Volume data was reported at 17,117.198 Unit mn in Mar 2025. This records an increase from the previous number of 15,003.146 Unit mn for Feb 2025. India Mobile Banking Transactions: Volume data is updated monthly, averaging 245.260 Unit mn from Apr 2011 (Median) to Mar 2025, with 168 observations. The data reached an all-time high of 17,117.198 Unit mn in Mar 2025 and a record low of 1.080 Unit mn in Apr 2011. India Mobile Banking Transactions: Volume data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Monetary – Table IN.KAI017: Mobile Payments. [COVID-19-IMPACT]

Dataset Information

Additional Information

The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

There are four datasets: 1) bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014] 2) bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs. 3) bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs). 4) bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs). The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM).

The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).

Data Dictionary

1. age: Age of the client.
2. job: Type of job the client holds.
3. marital: Marital status of the client.
4. education: Client's education level.
5. default: Indicates if the client has credit in default.
6. balance: Average yearly balance of the client, in euros.
7. housing: Indicates if the client has a housing loan.
8. loan: Indicates if the client has a personal loan.
9. contact: Type of communication mode used for contact.
10. day: Day of the last contact.
11. month: Month of the last contact.
12. duration: Duration of the last contact in seconds.
13. campaign: Number of contacts performed during this campaign.
14. pdays: Days since the client was last contacted from a previous campaign (999 indicates the client was not contacted).
15. previous: Number of contacts before this campaign.
16. poutcome: Outcome of the previous marketing campaign.
17. y: Indicates if the client subscribed to a term deposit.

India Mobile Banking Transactions: Value data was reported at 37,696,017.240 INR mn in Mar 2025. This records an increase from the previous number of 32,155,172.112 INR mn for Feb 2025. India Mobile Banking Transactions: Value data is updated monthly, averaging 1,798,543.365 INR mn from Apr 2011 (Median) to Mar 2025, with 168 observations. The data reached an all-time high of 37,696,017.240 INR mn in Mar 2025 and a record low of 760.000 INR mn in Apr 2011. India Mobile Banking Transactions: Value data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Monetary – Table IN.KAI017: Mobile Payments. [COVID-19-IMPACT]

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.

About Dataset

There has been a revenue decline in the Portuguese Bank and they would like to know what actions to take. After investigation, they found that the root cause was that their customers are not investing enough for long term deposits. So the bank would like to identify existing customers that have a higher chance to subscribe for a long term deposit and focus marketing efforts on such customers.

Data Set Information

The data is related to direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be subscribed ('yes') or not ('no') subscribed.

There are two datasets: train.csv with all examples (32950) and 21 inputs including the target feature, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014]

test.csv which is the test data that consists of 8238 observations and 20 features without the target feature

Goal:- The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).

The dataset contains train and test data. Features of train data are listed below. And the test data have already been preprocessed.

Features

Target variable (desired output):

The survey was carried out in Ho Chi Minh City in May-June 2021.

This dataset contains large-scale user reviews collected from the Google Play Store for five leading mobile banking applications in Türkiye: İşbank (İşCep), YapıKredi, Garanti BBVA, Akbank, and Ziraat Bank. The dataset includes more than 250000 user reviews, covering multiple dimensions of user experience such as satisfaction, complaints, feature requests, and performance feedback.

Each record provides detailed information, including:

• package_name (unique identifier of the mobile banking app)

• review_id (unique review identifier)

• user_name (anonymized or pseudonymized user name)

• content (review text)

• score (star rating, 1–5)

• thumbs_up_count (number of likes/upvotes)

• app_version and review_created_version

• timestamps (UTC and Istanbul local time)

• bank_name (associated financial institution)

The dataset was collected in August 2025 using the Google Play Scraper library, ensuring systematic extraction of publicly available app store data. All reviews are provided in Turkish (scrape_lang = "tr"), with precise timestamps for temporal analysis.

This dataset can support research in:

• User experience analysis in digital banking

• Sentiment analysis and opinion mining

• Topic modeling and service quality evaluation

• Time series forecasting of user satisfaction trends

• Comparative studies across multiple financial institutions

Researchers, practitioners, and developers can use this dataset to explore trends in digital banking adoption, analyze service quality signals, and develop machine learning models for predicting user satisfaction in mobile financial technologies.

This dataset was created as part of a Bachelor’s research project by a student from the University of Colombo. It contains 275 simulated survey responses reflecting mobile banking adoption trends in Sri Lanka. The data includes factors like age, district, education, income, trust, privacy concerns, usability, and device type. It is designed to explore how user demographics and perceptions influence mobile banking usage, especially across urban and rural regions. The dataset is suitable for academic and analytics-based research.

I'm conducting a studying on factors affecting adoption of mobile banking amongst a low socio-economic population- need assistance interpreting the results

The data shows number of monthly Mobile Banking and Mobile Payments from year 2019 to August 2023

Indonesia Proprietary Channel Transaction: Value: SMS/Mobile Banking: Payment data was reported at 183,589.210 IDR bn in Feb 2025. This records an increase from the previous number of 175,921.687 IDR bn for Jan 2025. Indonesia Proprietary Channel Transaction: Value: SMS/Mobile Banking: Payment data is updated monthly, averaging 45,845.117 IDR bn from Aug 2018 (Median) to Feb 2025, with 79 observations. The data reached an all-time high of 185,691.625 IDR bn in Dec 2024 and a record low of 14,015.516 IDR bn in Jun 2019. Indonesia Proprietary Channel Transaction: Value: SMS/Mobile Banking: Payment data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Monetary – Table ID.KAH016: Proprietary Channel.

This dataset contains the raw survey responses and the PLS-SEM output used in a study examining the factors influencing the continued use of mobile banking services in Indonesia. A total of 509 responses were collected through an online questionnaire using a convenience sampling method. The constructs were based on the UTAUT and Status Quo Bias models within the Dual Factor Theory (DFT) framework. The dataset includes both the original response data and the structural model output files used for hypothesis testing.

Raw data used for empirical study.

Mobile Banking Transactions: Volume: Bank Of India data was reported at 19.137 Unit mn in Sep 2018. This records an increase from the previous number of 16.166 Unit mn for Aug 2018. Mobile Banking Transactions: Volume: Bank Of India data is updated monthly, averaging 0.001 Unit mn from May 2009 (Median) to Sep 2018, with 113 observations. The data reached an all-time high of 19.137 Unit mn in Sep 2018 and a record low of 0.000 Unit mn in Apr 2010. Mobile Banking Transactions: Volume: Bank Of India data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Monetary – Table IN.KAI020: Mobile Banking Transactions: by Bankwise.

The dataset contains year- and month-wise compiled data from the year 2008 to till date on the number of digital payment transactions done through the systems such as Mobile, Real Time Gross Settlement (RTGS), Immediate Payment Service (IMPS), Unified Payment Interface (UPI), Bharat Interface for Money (BHIM), National Electronic Fund Transactions (NEFT), Bharat Bill Pay and Internet Banking

Note: Only outward transaction totals are considered for both RTGS and NEFT

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

This dataset was collected and curated as part of an ongoing PhD research project focusing on user-centered requirement analysis and satisfaction modeling in Saudi mobile banking applications. It contains raw customer review data, including the review date, source store (Google Play or App Store), bank name, textual review content, and user rating (1–5 scale).

As a PhD student researcher, I intend to release an updated and refined version of this dataset in the future that will include additional processed attributes such as sentiment polarity, user intent, Kano classification (Must-Be, Performance, Attractive), and structural metrics derived from ontology-based analysis.

This initial version serves as a foundational resource for researchers interested in sentiment analysis, feature prioritization, or satisfaction–achievement modeling within the context of Saudi mobile banking services.

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.

The study sought to examine the effect of mobile banking on financial performance of listed commercial banks in Kenya. The research adopted a causal research design. The research targeted the 12 listed commercial banks that operated between 2016 to 2020. Annual panel data sources running from 2016 to December 2020 was adopted in the current study.” The collected panel data for the period of between 2016 and 2020 was analyzed using Microsoft excel. The study adopted OLS regression model for the purpose of estimating the coefficients and associated p-values to enable the fitting the model and forecasting. The effect of mobile phone-based loans on financial performance was direct and major. The study also showed that Mobile Banking Volume of Transactions had a direct and significant effect on financial performance. Mobile Banking Value of Transactions also had a direct and significant. The analysis also revealed that the effect of asset quality on financial performance was inverse and significant. The effect of Capital Adequacy on financial performance was direct but not significant. Finally, the effect of Bank size on financial performance was positive and significant. The study thus concluded that mobile banking, asset quality, capital adequacy and bank size had a significant effect on financial performance of listed commercial banks in Kenya. The study thus suggests to management of listed commercial banks to improved mobile banking technologies and platform for easy access and use by customers. Management of listed commercial banks to issue high quality loans to customers. The banks should continue performing back ground check on customers’ credit worthiness to lower incidences of nonperforming loans. Management of listed commercial banks to continue increasing the size of their banks. The banks can increase their investment in financial assets such as treasury bills and bonds.