Finance17 cols · 200 rows
This dataset provides detailed, labeled records of simulated credit card transactions, including transaction amounts, merchant and cardholder information, and fraud indicators. It is ideal for developing and benchmarking machine learning models aimed at detecting fraudulent activity and reducing financial risk in payment systems. The inclusion of transaction context and cardholder demographics supports advanced analytics and feature engineering.
| merchant_category | transaction_type | entry_mode |
|---|
| grocery | purchase | chip |
| coffee_shop | purchase | contactless |
| electronics | purchase | online |
transaction_idcard_numbertransaction_datetime+11 more
Finance21 cols · 200 rows
This dataset contains simulated credit card transaction records, including detailed information on transaction amounts, merchant details, geolocation, device usage, and fraud labels. It is designed for training and evaluating fraud detection models, supporting the identification of both typical and anomalous transaction patterns. The dataset is ideal for fintech AI development, security analytics, and research into payment fraud behaviors.
| merchant_name | merchant_category | transaction_type |
|---|
| QuickMart | grocery | purchase |
| FashionBay | apparel | purchase |
| ElectroZone | electronics | purchase |
transaction_idtransaction_datetimecard_number+15 more
Finance19 cols · 200 rows
This dataset contains detailed synthetic payment transaction records, each labeled with ground-truth indicators of fraud. It includes transaction metadata, customer and merchant identifiers, payment methods, device and location context, and fraud reasons, making it ideal for developing and benchmarking machine learning models for payment fraud detection and risk mitigation.
| currency | transaction_status | fraud_label |
|---|
| USD | completed | false |
| GBP | completed | false |
| INR | failed | true |
transaction_idtransaction_datetimeamount+13 more
Finance19 cols · 200 rows
This dataset provides a detailed, feature-rich record of synthetic banking transactions, including transaction metadata, account and merchant information, contextual behavioral features, and fraud labels. It is ideal for developing, training, and benchmarking machine learning models for fraud detection and anomaly analysis in financial services.
| transaction_type | merchant_category | fraud_type |
|---|
| purchase | groceries | – |
| refund | electronics | fake_merchant |
| purchase | clothing | – |
transaction_idaccount_idtransaction_datetime+13 more
Insurance20 cols · 200 rows
This dataset contains detailed synthetic records of medical insurance claims, including patient demographics, provider information, claim amounts, service dates, and labeled indicators of fraudulent activity. Designed for machine learning and analytics, it enables robust research and development of fraud detection models in healthcare and insurance. The dataset supports granular analysis of claim patterns, provider behaviors, and patient demographics to identify and prevent fraudulent claims.
| claim_status | patient_gender | provider_specialty |
|---|
| approved | female | Family Practice |
| approved | male | Psychiatry |
| approved | female | Cardiology |
claim_idpatient_idprovider_id+14 more
Insurance27 cols · 200 rows
This synthetic insurance claims dataset provides detailed records of individual claims, including customer demographics, policy details, incident descriptions, and a fraud label for supervised learning. Designed for fraud detection and claim triage automation, the dataset enables advanced analytics and machine learning model development for the insurance industry.
| claim_type | incident_type | claim_status |
|---|
| auto | collision | approved |
| property | fire | in_review |
| health | prescription | submitted |
claim_idpolicy_idcustomer_id+21 more
Insurance26 cols · 100 rows
This synthetic insurance claim fraud detection dataset contains detailed records of claims, including incident specifics, claimant demographics, policy details, and fraud indicators. Designed for developing and testing machine learning models, it enables insurers and researchers to identify patterns of fraudulent activity and improve risk assessment strategies.
| incident_type | claim_status | policy_type |
|---|
| accident | under_review | auto |
| theft | submitted | home |
| fire | approved | home |
claim_idpolicy_idclaimant_id+20 more
Finance21 cols · 200 rows
This dataset provides detailed, labeled records of financial transactions, including transaction amounts, types, geolocation, merchant details, and fraud indicators. Designed for robust fraud detection model development and benchmarking, it supports advanced analytics and machine learning in banking and payment processing. The inclusion of comprehensive transaction attributes and fraud labels makes it ideal for supervised learning and anomaly detection research.
| currency | merchant_category | transaction_type |
|---|
| USD | grocery | purchase |
| GBP | restaurant | purchase |
| USD | pharmacy | purchase |
transaction_idaccount_idtransaction_datetime+15 more
Finance17 cols · 206 rows
This dataset provides granular, masked credit card transaction records including transaction amounts, merchant details, timestamps, authorization codes, and fraud flags. It supports robust analysis for fraud detection, merchant risk assessment, and payment trends across regions and card types. The dataset is ideal for financial institutions, payment processors, and analytics teams seeking actionable insights into card-based payments.
| merchant_name | card_type | transaction_status |
|---|
| FreshMart Grocery | Visa | approved |
| Burger Express | MasterCard | approved |
| TechZone Electronics | Visa | approved |
transaction_idmasked_card_numbertransaction_amount+11 more
Finance27 cols · 200 rows
This dataset simulates detailed financial statement records for public and private companies, enriched with fraud risk indicators and audit outcomes. It is designed for developing and benchmarking machine learning models to detect financial statement fraud, with comprehensive fields for financial metrics, suspicious activity counts, and risk scoring. The dataset is ideal for forensic analysis, risk assessment, and audit research in the finance industry.
| company_name | fiscal_period | company_type |
|---|
| Horizon Retail Ltd. | FY | Private |
| BlueSky Biotech PLC | Q2 | Public |
| Atlas Mining Corp. | Q1 | Private |
record_idcompany_idfiscal_year+21 more