The Credit Card Transactions Dataset provides detailed records of credit card transactions, including information about transaction times, amounts, and associated personal and merchant details. This dataset has over 1.85M rows.

How This Dataset Can Be Used:

Fraud Detection : Use machine learning models to identify fraudulent transactions by examining patterns in transaction amounts, locations, and user profiles. Enhancing fraud detection systems becomes feasible by analyzing behavioral patterns.

Customer Segmentation : Segment customers based on spending patterns, location, and demographics. Tailor marketing strategies and personalized offers to these different customer segments for better engagement.

Transaction Classification : Classify transactions into categories such as grocery or entertainment to understand spending behaviors. This helps in improving recommendation systems by identifying transaction categories and preferences.

Geospatial Analysis : Analyze transaction data geographically to map spending patterns and detect regional trends or anomalies based on latitude and longitude.

Predictive Modeling : Build models to forecast future spending behavior using historical transaction data. Predict potential fraudulent activities and financial trends.

Behavioral Analysis : Examine how factors like transaction amount, merchant type, and time influence spending behavior. Study the relationships between user demographics and transaction patterns.

Anomaly Detection : Identify unusual transaction patterns that deviate from normal behavior to detect potential fraud early. Employ anomaly detection techniques to spot outliers and suspicious activities.

Download the Meat Freshness Image Dataset with 2,266 images labeled into Fresh, Half-Fresh, and Spoiled categories. Perfect for building AI models in food safety and quality control to detect meat freshness based on visual cues.

• Digital payments are evolving, but so are cyber criminals.

• According to the Data Breach Index, more than 5 million records are being stolen on a daily basis, a concerning statistic that shows - fraud is still very common both for Card-Present and Card-not Present type of payments.

• In today’s digital world where trillions of Card transaction happens per day, detection of fraud is challenging.

This Dataset sourced by some unnamed institute.

Feature Explanation:

distance_from_home - the distance from home where the transaction happened.

distance_from_last_transaction - the distance from last transaction happened.

ratio_to_median_purchase_price - Ratio of purchased price transaction to median purchase price.

repeat_retailer - Is the transaction happened from same retailer.

used_chip - Is the transaction through chip (credit card).

used_pin_number - Is the transaction happened by using PIN number.

online_order - Is the transaction an online order.

fraud - Is the transaction fraudulent.

Title: Credit Card Transactions Dataset for Fraud Detection (Used in: A Hybrid Anomaly Detection Framework Combining Supervised and Unsupervised Learning)Description:This dataset, commonly known as creditcard.csv, contains anonymized credit card transactions made by European cardholders in September 2013. It includes 284,807 transactions, with 492 labeled as fraudulent. Due to confidentiality constraints, features have been transformed using PCA, except for 'Time' and 'Amount'.This dataset was used in the research article titled "A Hybrid Anomaly Detection Framework Combining Supervised and Unsupervised Learning for Credit Card Fraud Detection". The study proposes an ensemble model integrating techniques such as Autoencoders, Isolation Forest, Local Outlier Factor, and supervised classifiers including XGBoost and Random Forest, aiming to improve the detection of rare fraudulent patterns while maintaining efficiency and scalability.Key Features:30 numerical input features (V1–V28, Time, Amount)Class label indicating fraud (1) or normal (0)Imbalanced class distribution typical in real-world fraud detectionUse Case:Ideal for benchmarking and evaluating anomaly detection and classification algorithms in highly imbalanced data scenarios.Source:Originally published by the Machine Learning Group at Université Libre de Bruxelles.https://www.kaggle.com/mlg-ulb/creditcardfraudLicense:This dataset is distributed for academic and research purposes only. Please cite the original source when using the dataset.

Dataset

This dataset was created by Arshiya Kishore

Released under MIT

Contents

Credit Card

## Overview

Credit Card is a dataset for object detection tasks - it contains Credit Card annotations for 944 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Twitter data: Tweets with keyword 'Credit Card Fraud Detection' for 7-14 Dec 2022 including RT retweets. You can download this data and more; visit our site for more data twtdata.com Please contact mark@twtdata.com if you need more data.

This dataset had adapted from 'Credit Card Churn Prediction: https://www.kaggle.com/datasets/anwarsan/credit-card-bank-churn ' for visualization in our university project. We have modified customer information, spending behavior, and also added revenue targets.

Scenario 🕶️ In 2019, the marketing team launched a campaign to attract millennial customers (born 1980-1996) with the goal of increasing revenue and enhancing the brand's appeal to a younger audience.
As the BI team, your task is to create a dashboard for users. 1. The Vice President of Sales wants to view the performance of the credit business. 2. The marketing team is interested in understanding customer segments and customer spending to measure Customer Lifetime Value (CLV) and Marketing Cost per Acquired Customer (MCAC).

⚠️Note: This is just a suggestion to guide the creation of the dashboard

Example in Tableau

Executive summary https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10099382%2F508a2d2d89dabdfd368743f86c2a71e1%2Fexecutive%20overview.JPG?generation=1696110593484137&alt=media" alt=""> Customer behavior https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10099382%2F1e4a1f62a25eab3c6707d002243894c7%2Fcustomer_behaviour.JPG?generation=1696110689732332&alt=media" alt="">

This dataset contains a cleaned version of this dataset from UCI machine learning repository on credit card approvals.

Missing values have been filled and feature names and categorical names have been inferred, resulting in more context and it being easier to use.

Your task is to predict which people in the dataset are successful in applying for a credit card.

Synthetic Credit Card Default Dataset

  High-fidelity synthetic dataset for financial AI research, created with Syncora.ai






  ✅ What's in This Repo?

This repository includes:

✅ Synthetic Credit Card Default Dataset (CSV) → Download Here ✅ Jupyter Notebook for Analysis & Modeling → Open Notebook ✅ Instructions for generating your own synthetic data using Syncora API

  📘 About This Dataset

This dataset contains realistic, fully synthetic credit card… See the full description on the dataset page: https://huggingface.co/datasets/syncora/synthetic_credit_card_default.

The Credit Card Statistics provide data in relation to monthly credit card transactions. A breakdown of the number of credit cards issued to Irish residents is also provided.

Credit Cards Info Extraction

## Overview

Credit Cards Info Extraction is a dataset for object detection tasks - it contains Text annotations for 840 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Context

There is a lack of public available datasets on financial services and specially in the emerging mobile money transactions domain. Financial datasets are important to many researchers and in particular to us performing research in the domain of fraud detection. Part of the problem is the intrinsically private nature of financial transactions, that leads to no publicly available datasets.

We present a synthetic dataset generated using the simulator called PaySim as an approach to such a problem. PaySim uses aggregated data from the private dataset to generate a synthetic dataset that resembles the normal operation of transactions and injects malicious behaviour to later evaluate the performance of fraud detection methods.

Content

PaySim simulates mobile money transactions based on a sample of real transactions extracted from one month of financial logs from a mobile money service implemented in an African country. The original logs were provided by a multinational company, who is the provider of the mobile financial service which is currently running in more than 14 countries all around the world.

This synthetic dataset is scaled down 1/4 of the original dataset and it is created just for Kaggle.

NOTE: Transactions which are detected as fraud are cancelled, so for fraud detection these columns (oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest ) must not be used.

Headers

This is a sample of 1 row with headers explanation:

1,PAYMENT,1060.31,C429214117,1089.0,28.69,M1591654462,0.0,0.0,0,0

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.

Past Research

There are 5 similar files that contain the run of 5 different scenarios. These files are better explained at my PhD thesis chapter 7 (PhD Thesis Available here http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12932.

We ran PaySim several times using random seeds for 744 steps, representing each hour of one month of real time, which matches the original logs. Each run took around 45 minutes on an i7 intel processor with 16GB of RAM. The final result of a run contains approximately 24 million of financial records divided into the 5 types of categories: CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.

Acknowledgements

This work is part of the research project ”Scalable resource-efficient systems for big data analytics” funded by the Knowledge Foundation (grant: 20140032) in Sweden.

Please refer to this dataset using the following citations:

PaySim first paper of the simulator:

E. A. Lopez-Rojas , A. Elmir, and S. Axelsson. "PaySim: A financial mobile money simulator for fraud detection". In: The 28th European Modeling and Simulation Symposium-EMSS, Larnaca, Cyprus. 2016

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4560787%2F1bf7d8acca3f6ca6adbae87c95df1f33%2F1_MIXrCZ0QAVp6qoElgWea-A.jpg?generation=1697784111548502&alt=media" alt="">

Data is the new oil, and this dataset is a wellspring of knowledge waiting to be tapped😷!

Don't forget to upvote and share your insights with the community. Happy data exploration!🥰

** For more related datasets: ** https://www.kaggle.com/datasets/rajatsurana979/fifafcmobile24 https://www.kaggle.com/datasets/rajatsurana979/most-streamed-spotify-songs-2023 https://www.kaggle.com/datasets/rajatsurana979/comprehensive-credit-card-transactions-dataset https://www.kaggle.com/datasets/rajatsurana979/hotel-reservation-data-repository https://www.kaggle.com/datasets/rajatsurana979/percent-change-in-consumer-spending https://www.kaggle.com/datasets/rajatsurana979/fast-food-sales-report/data

Description: Welcome to the world of credit card transactions! This dataset provides a treasure trove of insights into customers' spending habits, transactions, and more. Whether you're a data scientist, analyst, or just someone curious about how money moves, this dataset is for you.

Features: - Customer ID: Unique identifiers for every customer. - Name: First name of the customer. - Surname: Last name of the customer. - Gender: The gender of the customer. - Birthdate: Date of birth for each customer. - Transaction Amount: The dollar amount for each transaction. - Date: Date when the transaction occurred. - Merchant Name: The name of the merchant where the transaction took place. - Category: Categorization of the transaction.

Why this dataset matters: Understanding consumer spending patterns is crucial for businesses and financial institutions. This dataset is a goldmine for exploring trends, patterns, and anomalies in financial behavior. It can be used for fraud detection, marketing strategies, and much more.

Acknowledgments: We'd like to express our gratitude to the contributors and data scientists who helped curate this dataset. It's a collaborative effort to promote data-driven decision-making.

Let's Dive In: Explore, analyze, and visualize this data to uncover the hidden stories in the world of credit card transactions. We look forward to seeing your innovative analyses, visualizations, and applications using this dataset.

Locating Card

## Overview

Locating Card is a dataset for object detection tasks - it contains Credit Card annotations for 300 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

Graph and download economic data for Large Bank Consumer Credit Card Balances: Utilization: Active Accounts Only: 75th Percentile (RCCCBACTIVEUTILPCT75) from Q3 2012 to Q2 2025 about FR Y-14M, utilities, consumer credit, large, balance, percentile, loans, consumer, banks, depository institutions, and USA.

PII Cards Annotated

## Overview

PII Cards Annotated is a dataset for object detection tasks - it contains Images VguN Images XQxY Images NjZM annotations for 1,660 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This Dataset contains year, month, bank-type and bank-wise total value and volume of card payments and cash withdrawal transactions of credit and debit Cards at point of sale (PoS), ATMs and online during each month

About Dataset

Context

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

Dataset Creation Details

Entity relationship diagram (ERD) is used to create provided CSV files. Here is the Schema

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2976541%2F912198aacb3f0bd8aa895b2d5b5a867d%2FScreenshot%202023-07-26%20at%206.12.39%20PM.png?generation=1690375418366165&alt=media" alt="">

Using the above database model as a blueprint, created a database schema for each of your tables and relationships. Specify data types, primary keys, foreign keys, and any other constraints.

CreditCard_POS_workspace

## Overview

CreditCard_POS_workspace is a dataset for object detection tasks - it contains CreditCard POS annotations for 600 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).