Finance datasets

Banking transactions, loan data, market analysis

  • 248 ready-made datasets
  • 8 formats, Excel to Parquet
  • First download free
What the 248 cover1 square = 1 dataset
  • Payments & messaging101
  • Credit & lending44
  • Banking & transactions36
  • Fraud & AML31
  • Markets & investing18
  • Expenses & invoicing13
  • Everything else5
Grouped by dataset title and tags.

248 datasets · page 10 of 11

Showing all 24 on this page

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
Open in factory
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 & AML

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

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
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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
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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

Real codes. Never real customers.

Code systems found in the sample rows of all 248 finance datasets.

Code systemDatasets using it, of 248
  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 248
  1. currency99
  2. transaction_id62
  3. customer_id53
  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

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