Finance datasets

Banking transactions, loan data, market analysis

251 ready-made datasets
8 formats, Excel to Parquet
First download free

What the 251 cover1 square = 1 dataset

Payments & messaging101
Credit & lending44
Banking & transactions39
Fraud & AML31
Markets & investing18
Expenses & invoicing13
Everything else5

Grouped by dataset title and tags.

Showing all 24 on this page

Credit & lending

Loan Application Document Checklist

Status, verification, and compliance notes

This dataset offers a detailed, structured checklist of required and submitted documents for each loan application, including status, verification, and compliance notes. It enables financial institutions to automate workflow, ensure regulatory compliance, and streamline the loan approval process with full auditability. Ideal for compliance teams, workflow automation, and analytics on loan processing efficiency.

14 cols

document_name
document_type
document_expiry_date
is_required
+10

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document_name: Government Issued ID
document_type: identification
is_required: true

Banking & transactions

Banking Transaction Sequence Patterns

Transaction types, amounts, channels, geolocation, and fraud indicators

This dataset provides detailed, synthetic banking transaction records across customer accounts, including transaction types, amounts, channels, geolocation, and fraud indicators. Designed for advanced sequence modeling, fraud detection, and behavioral segmentation, it enables deep analysis of financial activity patterns and risk assessment. The rich set of attributes supports both supervised and unsupervised machine learning applications in financial analytics.

17 cols

transaction_datetime
transaction_type
transaction_amount
balance_after_transaction
+13

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transaction_type: deposit
transaction_amount: 2450
balance_after_transaction: 12450

Credit & lending

Loan Default Prediction Records

Loan applications, borrower demographics, payment histories

This dataset provides detailed records of loan applications, borrower demographics, payment histories, and default outcomes, enabling comprehensive credit risk analysis and predictive modeling. It includes key features such as loan terms, payment behavior, applicant financials, and default indicators, making it highly valuable for developing credit scoring systems and risk management tools.

29 cols

loan_amount
loan_term_months
loan_purpose
current_loan_status
+25

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loan_amount: 18750
loan_term_months: 36
loan_purpose: personal

Credit & lending

Credit Card Application Approvals

Detailed applicant profiles, financial information, application outcomes

This dataset provides comprehensive records of credit card applications, including detailed applicant profiles, financial information, application outcomes, and decision rationales. It is ideal for developing and evaluating machine learning models for credit risk assessment, approval automation, and regulatory compliance analysis.

25 cols

credit_score
existing_credit_cards
requested_credit_limit
application_status
+21

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credit_score: 756
existing_credit_cards: 2
requested_credit_limit: 12000

Fraud & AML

Financial Transaction Fraud Labels

Customer demographics, merchant information, device characteristics

This dataset contains detailed records of financial transactions, including customer demographics, merchant information, device characteristics, and explicit fraud labels. It is designed for machine learning-driven fraud detection, risk analysis, and real-time alerting, enabling robust analytics and model development for financial institutions and payment processors.

19 cols

transaction_datetime
is_fraud
fraud_label_source
fraud_report_datetime
+15

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is_fraud: false
amount: 156.27
currency: USD

Banking & transactions

Banking Customer Support Chat Logs

Full transcripts, participant identifiers, timestamps, issue categories

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.

15 cols

chat_start_time
chat_end_time
chat_duration_seconds
customer_message_count
+11

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chat_duration_seconds: 90
customer_message_count: 2
customer_satisfaction_rating: 4

Banking & transactions

Online Banking Login Attempt Logs

User identifiers, authentication methods, device and location data

This dataset provides comprehensive, timestamped logs of online banking login attempts, including user identifiers, authentication methods, device and location data, and the outcome of each attempt. It is designed for security monitoring, anomaly detection, and compliance auditing in financial technology environments, enabling deep analysis of user behavior and potential threats.

16 cols

login_channel
username
success
failure_reason
+12

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login_channel: web
username: jdoe
success: true

Payments & messaging

Consumer Finance Late Payment Events

Payment amounts, overdue durations, categorized reasons for lateness

This dataset provides detailed records of overdue consumer finance payments, including payment amounts, overdue durations, categorized reasons for lateness, and collections process status. It enables comprehensive risk modeling, collections workflow optimization, and regulatory compliance analysis for financial institutions managing consumer credit portfolios.

14 cols

payment_status
late_payment_reason
event_date
days_overdue
+10

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payment_status: paid
late_payment_reason: forgot_to_pay
days_overdue: 3

Banking & transactions

Bank Call Center Interactions

Timestamps, agent and customer details, interaction topics, sentiment

This dataset provides granular records of customer interactions with banking call centers, including timestamps, agent and customer details, interaction topics, sentiment, resolution status, and full transcripts. It is ideal for customer experience analytics, agent performance monitoring, and training conversational AI models to improve banking services.

22 cols

interaction_datetime
call_duration_seconds
interaction_type
bank_branch_code
+18

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call_duration_seconds: 95
interaction_type: inquiry
bank_branch_code: BR001

Credit & lending

Microloan Application Approval Logs

Detailed decision reasons, risk scores, fintech innovation indicators

This dataset provides comprehensive logs of microloan application approvals and rejections, including detailed decision reasons, risk scores, fintech innovation indicators, and processing times. It enables in-depth analysis of lending criteria, risk assessment models, and the impact of fintech innovations on loan decisions, supporting both operational optimization and regulatory compliance.

20 cols

loan_amount_approved
application_status
approval_reason_code
application_date
+16

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loan_amount_approved: 150
application_status: approved
approval_reason_code: RISK_OK

Fraud & AML

Financial Transaction Fraud Patterns

Financial transactions, labeled as either legitimate or fraudulent

This dataset provides detailed records of financial transactions, labeled as either legitimate or fraudulent, with comprehensive contextual information such as account, merchant, location, device, and channel data. It is designed to support the development and evaluation of fraud detection algorithms, enabling advanced pattern analysis and risk assessment in financial services. The inclusion of both fraudulent and legitimate cases makes it ideal for supervised machine learning and anomaly detection research.

22 cols

transaction_datetime
transaction_type
is_fraud
fraud_type
+18

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transaction_type: purchase
is_fraud: false
amount: 57.42

Credit & lending

AI-Driven Loan Default Scenarios

Borrower and loan records, enriched with macroeconomic scenario variables for robust

This synthetic dataset provides detailed, time-stamped borrower and loan records, enriched with macroeconomic scenario variables for robust loan default prediction modeling. It is designed for fintech machine learning experiments, enabling analysis of borrower behavior and loan performance under varied economic conditions. The dataset supports scenario-based risk modeling, stress testing, and algorithm benchmarking.

21 cols

loan_amount
loan_term_months
loan_purpose
defaulted
+17

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loan_amount: 25000
loan_term_months: 60
loan_purpose: auto

Fraud & AML

Credit Card Fraud Patterns

Detailed information on transaction amounts, merchant details, geolocation

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.

21 cols

card_number
is_fraud
fraud_type
transaction_datetime
+17

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card_number: ****8923
is_fraud: false
merchant_name: QuickMart

Credit & lending

Small Business Loan Risk Inputs

Business and owner profiles, financials, collateral

This dataset provides detailed records of small business loan applications, approvals, and repayment performance, including business and owner profiles, financials, collateral, and loan lifecycle events. It is ideal for developing and validating credit scoring models, SME risk assessment, and portfolio monitoring in lending institutions.

33 cols

business_name
business_industry
business_year_established
business_annual_revenue
+29

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business_name: Blueberry Retail Shop
business_industry: Retail
business_year_established: 2016

Credit & lending

Credit Card Spend Pattern Clusters

Credit card transaction records

This dataset contains anonymized credit card transaction records, enriched with behavioral cluster assignments and key transaction attributes such as merchant category, transaction type, and customer demographics. Designed for segmentation and marketing analytics, it enables organizations to identify spending patterns, target customer segments, and optimize marketing strategies.

15 cols

cluster_label
card_type
transaction_datetime
amount
+11

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cluster_label: Grocery Shopper
card_type: Visa
amount: 87.54

Fraud & AML

Financial Transaction Fraud Features

Transaction metadata, account and merchant information

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.

19 cols

transaction_datetime
transaction_amount
transaction_type
previous_transactions_24h
+15

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transaction_amount: 54.33
transaction_type: purchase
previous_transactions_24h: 1

Markets & investing

Algorithmic Trading Market Snapshots

Market snapshots across multiple instruments and exchanges

This dataset provides granular, high-frequency market snapshots across multiple instruments and exchanges, capturing bid/ask quotes, trade data, and engineered features such as spread, mid-price, and order book imbalance. It is ideal for quantitative researchers, algorithmic traders, and data scientists seeking to train models, backtest trading strategies, or detect market anomalies in real or simulated environments.

17 cols

symbol
exchange
bid_price
bid_size
+13

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symbol: AAPL
exchange: NASDAQ
bid_price: 171.3

Credit & lending

Bank Loan Application Approvals

Applicant demographics, financial background, loan request details

This dataset contains detailed synthetic records of bank loan applications, including applicant demographics, financial background, loan request details, and final approval or denial outcomes. It is ideal for developing and benchmarking predictive models for credit risk assessment, as well as for analyzing approval patterns and fairness in lending decisions.

28 cols

applicant_marital_status
applicant_employment_status
applicant_annual_income
applicant_credit_score
+24

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applicant_marital_status: single
applicant_employment_status: employed
applicant_annual_income: 86000

Banking & transactions

ATM Cash Refill Forecasts

Transaction amounts, timestamps, ATM locations, and operator information

This dataset provides detailed records of ATM cash withdrawals and refill events, including transaction amounts, timestamps, ATM locations, and operator information. It enables comprehensive analysis of cash usage patterns and supports predictive modeling for optimal ATM cash management, reducing operational costs and minimizing cash-out risks.

16 cols

atm_location_name
refill_amount
atm_balance_after_transaction
transaction_type
+12

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atm_location_name: Downtown Branch
refill_amount: 0
atm_balance_after_transaction: 13040

Banking & transactionsTop 10 most opened

Bank Transaction Category Classification

Bank transaction records, each labeled with spending categories such as groceries

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.

18 cols

transaction_datetime
merchant_category
category
transaction_type
+14

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merchant_category: Supermarket
category: groceries
transaction_type: debit

Banking & transactions

ATM Transaction Usage Patterns

Transaction counts, total and average amounts, unique card usage

This dataset provides daily aggregated cash withdrawal and deposit activity for each ATM, including transaction counts, total and average amounts, unique card usage, and a flag for suspicious activity. The data is ideal for analyzing ATM usage patterns, forecasting cash demand, and monitoring for potential fraud across different locations. Detailed location fields enable geographic and branch-level insights.

17 cols

transaction_date
total_withdrawal_count
total_withdrawal_amount
total_deposit_count
+13

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total_withdrawal_count: 96
total_withdrawal_amount: 27389.5
total_deposit_count: 12

Credit & lending

Loan Default Probability Dataset

Demographic, financial, and credit information

This dataset provides detailed synthetic profiles of loan applicants and their loan applications, including demographic, financial, and credit information, along with default outcomes and predicted default probabilities. It is ideal for developing, benchmarking, and validating credit scoring and risk assessment models, and supports a wide range of analytics in financial services.

24 cols

loan_amount
loan_term_months
loan_purpose
previous_defaults
+20

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loan_amount: 275000
loan_term_months: 240
loan_purpose: home

Fraud & AMLTop 10 most opened

Credit Card Fraud Detection

Transaction amounts, merchant and cardholder information

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.

17 cols

card_number
is_fraud
transaction_datetime
transaction_amount
+13

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card_number: 4532519236845291
is_fraud: false
transaction_amount: 37.18

Credit & lending

Credit Application Approval Outcomes

Applicant demographics, financial profiles, application details

This dataset provides detailed records of credit application outcomes, including applicant demographics, financial profiles, application details, and decision rationales. It enables comprehensive analysis of approval and rejection trends, supports risk model optimization, and helps financial institutions refine their credit decision processes.

15 cols

credit_amount_requested
credit_amount_approved
applicant_income
applicant_employment_status
+11

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credit_amount_requested: 15000
credit_amount_approved: 14000
applicant_income: 65000

Real codes. Never real customers.

Code systems found in the sample rows of all 251 finance datasets.

Code systemDatasets using it, of 251

ISO 4217currencies161USD · GBP · EUR
ISO 3166countries144US · USA · FR
SWIFT BICbank identifiers45DEUTDEFFXXX · BOFAUS3N · NWBKGB2L
MCCmerchant categories115814 · 5411 · 5812
IBANaccount numbers7DE89370400440532013000 · IT60X0542811101000000123456 · DE23100100101234567893
SWIFT MTmessage types6MT103 · MT900 · MT202
ISINsecurities4GB00B03MLX29 · DE000BASF111 · FR0000123456
ISO 20022payment messages3ACCP · pacs.008

A person from the Identity Theft Cases datasetGenerated

victim_first_name: Jessica
victim_last_name: Mendoza
victim_date_of_birth: 1988-03-25
victim_gender: female

Generated

Most common columnsDatasets, of 251

currency99
transaction_id63
customer_id55
transaction_type49
transaction_amount41

Not quite what you need? Describe it.

One sentence in. A dataset with that pattern out.

Your sentence

Online fraud 4×
Real merchant category codes
Duplicate rows

Preview 20 rows freeNo signup. No card.

Fraud rate by channel

Sample rows for: Card payments from 5,000 customers, where online payments are 4× more likely to be fraud, with real merchant category codes and a few duplicate rows.
channel	mcc	amount	is_fraud
Online	5411	84.20	1
Chip	5812	23.50	0
Online	5999	412.00	1
Online	5999	412.00	1

A pattern you asked for Everything else

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