This dataset provides a detailed look into transactional behavior and financial activity patterns, ideal for exploring fraud detection and anomaly identification. It contains 2,512 samples of transaction data, covering various transaction attributes, customer demographics, and usage patterns. Each entry offers comprehensive insights into transaction behavior, enabling analysis for financial security and fraud detection applications.

Key Features:

• TransactionID: Unique alphanumeric identifier for each transaction.
• AccountID: Unique identifier for each account, with multiple transactions per account.
• TransactionAmount: Monetary value of each transaction, ranging from small everyday expenses to larger purchases.
• TransactionDate: Timestamp of each transaction, capturing date and time.
• TransactionType: Categorical field indicating 'Credit' or 'Debit' transactions.
• Location: Geographic location of the transaction, represented by U.S. city names.
• DeviceID: Alphanumeric identifier for devices used to perform the transaction.
• IP Address: IPv4 address associated with the transaction, with occasional changes for some accounts.
• MerchantID: Unique identifier for merchants, showing preferred and outlier merchants for each account.
• AccountBalance: Balance in the account post-transaction, with logical correlations based on transaction type and amount.
• PreviousTransactionDate: Timestamp of the last transaction for the account, aiding in calculating transaction frequency.
• Channel: Channel through which the transaction was performed (e.g., Online, ATM, Branch).
• CustomerAge: Age of the account holder, with logical groupings based on occupation.
• CustomerOccupation: Occupation of the account holder (e.g., Doctor, Engineer, Student, Retired), reflecting income patterns.
• TransactionDuration: Duration of the transaction in seconds, varying by transaction type.
• LoginAttempts: Number of login attempts before the transaction, with higher values indicating potential anomalies.

This dataset is ideal for data scientists, financial analysts, and researchers looking to analyze transactional patterns, detect fraud, and build predictive models for financial security applications. The dataset was designed for machine learning and pattern analysis tasks and is not intended as a primary data source for academic publications.

In recent years, we have been exploring computational models to classify bank accounts in combating illegal pyramid selling. The department of economic investigation provides us with plenty of transaction data of real bank accounts. An account contains a lot of transaction records, each of which includes bilateral transaction accounts, timestamp, amount of money and transaction direction, etc. We sample out the transaction records belonging to 10145 bank accounts to form out dataset for training our model. There are 9270 normal accounts and 875 accounts involving a MLM organization respectively. The number of transaction records generated by the normal accounts run up to 6732730 and the fraud records created by MLM members amount to 275804 rows. These MLM members are manually annotated as ``illegal'' by economic investigators. Before training the models, we filtered out some noisy data, i.e. deleting the duplicate records, incomplete records and the records whose transaction amounts no more than 50. Therefore, 1371914 records is filtered out from the set of normal accounts' transaction records and 91341 records created by illegal accounts are deleted. In general, more than 5 million transaction records are used after denoising.

Dataset

This dataset was created by bhadresh savani

Contents

Synthetic Bank Transaction Process

Models: Petri net, Large, Stand-alone and SESE-aided Decomposed

Logs: Large, with and without noise, two particular scenarios (see paper).

Additional: Model diagram, decomposition diagram, activity re-naming.

Inspiration

Many projects require datasets about bank transactions to test their systems. Unfortunately, it is hard to find a dataset that would have transaction product categorization which is important for many analytical projects.

Content

There you have 4 datasets. Clients - basic information about bank users. Categories - standart transaction categories which are being by many banks worldwide. Transactions - the core of our dataset, basic information about transactions like who is the second account of transaction, category, amount, etc. Subscriptions - information about subscriptions, in other words, transactions which are made automatically.

Characteristics of the dataset.

Research on methods and techniques in financial fraud detection field.

Envestnet®| Yodlee®'s Bank Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

In synthetica synthetica synthetica synthetica synthetica dicta synthetica syntheticas syntheticas syntheticas enuntiationes factas artificiose generatas designabat ad simulata documenta realia nummaria. Varias transactiones tabulas, dies, summas et singulas rationes componit, quae ad formas rerum et contentorum reales mundi speculorum structas est. Haec dataset usus est ad formandum et aestimandum Documenti AI systemata in operibus sicut agnitio characteris optici (OCR), extractio notitiarum et analysis documenti, praebens ambitum moderatum sine intimis quaestionibus actualis notitiae nummariae.

The dataset contains year- and month-wise compiled data on number of Internet Banking mandates (different types of transactions) which have been performed and received by sponsor and destination banks, respectively, through National Automated Clearing House (NACH) . The dataset also includes additional details such as number and percentage of mandates which were accepted, business declined, technical declined, which received response and no response from the customers and the mandates which were timed out, etc.

Notes: 1. For the purpose of the dataset, the Sponsor Banks are those banks from which Mandates (different types of transactions) have been performed and the Destination Banks are those banks which have received Mandates

1. Business Decline (BD) transactions are those transaction which are declined due to business rules such as duplicate transactions or Tag ID not associated with issuer Bank, etc.

2. Technical Decline (TD) transactions are those transaction which are decline due to any technical reason such as bank ID is empty or not in correct format or exception code not in Database or not in correct format, etc.

Analysis of ‘Fraud detection bank dataset 20K records binary ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/volodymyrgavrysh/fraud-detection-bank-dataset-20k-records-binary on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Banks are often exposed to fraud transactions and constantly improve systems to track them.

Content

Bank dataset that contains 20k+ transactions with 112 features (numerical)

--- Original source retains full ownership of the source dataset ---

This particular dataset is provided by National Bank of Greece and contains data regarding transactions made from the deposit accounts of a thousand of the bank’s customers.

The important information regarding the transactions are contained in said dataset. These refer to the date, amount, type and category of each transaction, as well as the channel and account ID associated with it. The ID of the customer is masked for private data protection purposes.

The Department of Public Services and Procurement Canada, in its role as Receiver General for Canada, is responsible for the management and safeguarding of all federal government money. The Receiver General uses a centralized banking system (Government Banking System or GBS) to record the inflow of funds. Data is stored in the GBS detailing the dates and amounts for deposits made to financial institutions. This dataset, entitled “Deposits to Financial Institutions”, provides details extracted from the Government Banking System of all deposits made to Financial Institutions and their subsequent receipt at the Bank of Canada. Updates will be posted quarterly. The data has been divided into yearly files spanning one fiscal year, from April 1st to March 31st

Dataset

This dataset was created by Paradeveloper

Released under CC BY-SA 4.0

Contents

Results of SPSS on accuracy.

High Frequency Indicator: The dataset contains year-, month- and bank-wise compiled data from the year 2021 to till date on the transactions performed (responses) and failed (returns) by debit sponsor banks through National Automated Clearing House (NACH) system

Notes:

1. NACH Credit is an electronic payment service used by an institution for affording credits to a large number of beneficiaries in their bank accounts for the payment of dividend, interest, salary, pension etc. by raising a single debit to the bank account of the user institution
2. Business Declines (BD) are declined transactions due to a customer entering an invalid pin, incorrect beneficiary account etc. or due to other business reasons such as exceeding per transaction limit, exceeding permitted count of transactions per day, exceeding amount limit for the day etc.
3. Technical Declines (TD) transactions are those transactions are declined due to any technical reasons such as bank ID is empty or not in correct format or exception code not in Database or not in correct format, etc

The confusion matrix of the three methods.

The numbers in parenthesses refer to the corresponding average results on training set.

This dataset contains detailed synthetic bank transaction records, each labeled with spending categories such as groceries, travel, and utilities. It includes transaction metadata, merchant details, recurrence information, and account associations, making it ideal for developing and benchmarking personal finance management tools, automated expense categorization, and financial analytics solutions.

Values of the second route parameters.