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.

251 datasets · page 11 of 11

Showing all 11 on this page

Fraud & AML

Financial Transaction Fraud Detection

Transaction amounts, types, geolocation, merchant details

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.

21 cols

  • transaction_datetime
  • transaction_amount
  • transaction_type
  • is_fraud
  • +17
Open in factory
transaction_amount
89.45
transaction_type
purchase
is_fraud
false

Markets & investing

Portfolio Risk Exposure Overview

Risk scores, asset allocation, leverage, and overexposure flags

This dataset provides a comprehensive overview of risk exposure across investment portfolios, including risk scores, asset allocation, leverage, and overexposure flags. It enables financial analysts to identify portfolios with high risk, monitor asset concentration, and optimize allocations for better risk-adjusted returns. The dataset supports regulatory reporting, internal risk reviews, and strategic portfolio management.

16 cols

  • portfolio_name
  • risk_level
  • risk_score
  • asset_class_exposure_pct
  • +12
Open in factory
portfolio_name
Alpha Growth Fund
risk_level
Moderate
risk_score
42.7

Payments & messaging

Recurring Subscription Payment Analysis

Customer details, payment history, churn indicators, and retention actions

This dataset provides a comprehensive view of recurring finance subscription payments, including customer details, payment history, churn indicators, and retention actions. It is ideal for analyzing payment patterns, identifying churn risk, and optimizing customer retention strategies in subscription-based financial services.

20 cols

  • subscription_plan
  • payment_method
  • total_payments_made
  • failed_payment_count
  • +16
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subscription_plan
Basic
payment_method
credit_card
total_payments_made
19

Credit & lending

Loan Application Approval Trends

Loan applications, capturing applicant demographics, loan characteristics

This dataset provides detailed records of loan applications, capturing applicant demographics, loan characteristics, processing timelines, and final outcomes. It enables in-depth analysis of approval trends, risk factors, and process bottlenecks to support data-driven lending strategies and operational improvements.

20 cols

  • loan_amount
  • loan_type
  • loan_term_months
  • application_status
  • +16
Open in factory
loan_amount
32000
loan_type
auto
loan_term_months
60

Credit & lending

Overdraft Fee Occurrence Analysis

Checking account overdraft fee events, capturing transaction details

This dataset provides detailed logs of checking account overdraft fee events, capturing transaction details, account and customer identifiers, fee amounts, and customer segmentation. It enables financial institutions to identify high-risk customer segments, analyze patterns of overdraft occurrences, and develop targeted strategies to reduce fee events and improve customer financial health.

16 cols

  • fee_amount
  • event_datetime
  • account_balance_before
  • account_balance_after
  • +12
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fee_amount
35
account_balance_before
-12.5
account_balance_after
-64.5

Markets & investing

Daily Foreign Exchange Rate Fluctuations

Open, high, low, close, and change metrics

This dataset provides detailed daily foreign exchange rate information for global currency pairs, including open, high, low, close, and change metrics. It enables robust financial analysis, risk assessment, and strategic planning for businesses engaged in international trade or investment. The data is structured to support time series analysis, forecasting, and regulatory compliance.

12 cols

  • exchange_rate
  • rate_open
  • rate_high
  • rate_low
  • +8
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exchange_rate
0.9342
rate_open
0.934
rate_high
0.9361

Banking & transactions

Asset Depreciation Calculation Table

Acquisition details, depreciation methods, annual

This dataset provides detailed, year-by-year depreciation calculations for company assets, including acquisition details, depreciation methods, annual and accumulated depreciation, and book values. It enables precise financial reporting, tax compliance, and asset management by tracking each asset's lifecycle and depreciation schedule. The dataset is ideal for accountants, auditors, and financial analysts seeking transparency and accuracy in fixed asset accounting.

17 cols

  • asset_name
  • asset_category
  • depreciation_method
  • depreciation_year
  • +13
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asset_name
Forklift FLX200
asset_category
equipment
depreciation_method
straight_line

Banking & transactions

ATM Cash Withdrawal Patterns

Transaction amounts, timestamps, ATM locations, card and account types

This dataset provides detailed records of ATM cash withdrawal transactions, including transaction amounts, timestamps, ATM locations, card and account types, and ATM cash balances. It is ideal for analyzing cash demand patterns, optimizing ATM restocking schedules, and understanding customer withdrawal behaviors across different locations and time periods.

20 cols

  • atm_location_name
  • withdrawal_amount
  • atm_balance_after
  • atm_capacity
  • +16
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atm_location_name
Downtown Branch
withdrawal_amount
80
atm_balance_after
4920

Fraud & AML

Credit Card Transaction Fraud Flags

Credit card transaction records enriched with fraud suspicion flags

This dataset provides detailed credit card transaction records enriched with fraud suspicion flags, risk scores, and contextual information such as merchant, location, and transaction method. It is ideal for developing, training, and evaluating fraud detection models, as well as for analyzing transaction patterns and identifying emerging fraud tactics in the financial sector.

21 cols

  • card_number_hash
  • transaction_datetime
  • transaction_type
  • fraud_flag
  • +17
Open in factory
card_number_hash
CARDHASH001
transaction_type
purchase
fraud_flag
false

Expenses & invoicing

Expense Reimbursement Records

Detailed claim information, approval workflow, policy compliance

This dataset provides a comprehensive record of employee expense reimbursement claims, including detailed claim information, approval workflow, policy compliance, and payment tracking. It enables finance teams to efficiently validate, audit, and analyze expense claims for policy adherence and financial reporting. The dataset is ideal for process optimization, fraud detection, and compliance monitoring.

20 cols

  • expense_category
  • expense_date
  • employee_name
  • department
  • +16
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expense_category
travel
employee_name
Jessica Martin
department
Sales

Credit & lending

Loan Application Risk Analysis

Loan applications, capturing applicant demographics, financial profiles

This dataset provides a comprehensive view of loan applications, capturing applicant demographics, financial profiles, loan details, and subsequent loan performance. Designed for credit risk modeling and decision automation, it enables in-depth analysis of risk factors, default prediction, and portfolio management in the lending industry.

39 cols

  • loan_amount
  • loan_term_months
  • loan_purpose
  • application_status
  • +35
Open in factory
loan_amount
245800
loan_term_months
180
loan_purpose
home

Real codes. Never real customers.

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

Code systemDatasets using it, of 251
  1. ISO 4217currencies161USD · GBP · EUR
  2. ISO 3166countries144US · USA · FR
  3. SWIFT BICbank identifiers45DEUTDEFFXXX · BOFAUS3N · NWBKGB2L
  4. MCCmerchant categories115814 · 5411 · 5812
  5. IBANaccount numbers7DE89370400440532013000 · IT60X0542811101000000123456 · DE23100100101234567893
  6. SWIFT MTmessage types6MT103 · MT900 · MT202
  7. ISINsecurities4GB00B03MLX29 · DE000BASF111 · FR0000123456
  8. 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
  1. currency99
  2. transaction_id63
  3. customer_id55
  4. transaction_type49
  5. transaction_amount41

Not quite what you need? Describe it.

One sentence in. A dataset with that pattern out.

  • Online fraud 4×
  • Real merchant category codes
  • Duplicate rows
Preview 20 rows freeNo signup. No card.
Fraud rate by channel
0%2.5%5%1%Chip1%Contactless4%Online11%Phone
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.
channelmccamountis_fraud
Online541184.201
Chip581223.500
Online5999412.001
Online5999412.001

A pattern you asked for Everything else

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