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.

Showing all 24 on this page

Credit & lending

Credit Risk Assessment Profiles

Personal information, financial history, credit scores

This dataset provides detailed profiles of credit applicants, including personal information, financial history, credit scores, loan application details, and risk assessments. It is designed to support financial institutions in evaluating creditworthiness, modeling risk, and making informed lending decisions. The dataset is ideal for predictive analytics, regulatory compliance, and portfolio management.

33 cols

credit_score
credit_history_length_years
risk_assessment_score
risk_category
+29

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credit_score: 728
credit_history_length_years: 8.2
risk_assessment_score: 0.18

Markets & investing

Stock Portfolio Allocation Dataset

Asset class breakdowns, risk profiles

This dataset provides detailed simulated stock portfolio allocations, including asset class breakdowns, risk profiles, and return metrics for investor accounts. It is ideal for financial research, portfolio optimization studies, and risk-return analysis, offering granular insights into investment strategies and outcomes.

14 cols

portfolio_total_value
allocation_date
investor_profile
asset_class_equity_pct
+10

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portfolio_total_value: 18650.58
investor_profile: conservative
asset_class_equity_pct: 12

Banking & transactions

Fintech App User Engagement

Feature usage, transaction volumes, device types, and premium status

This dataset provides detailed, user-level engagement and transaction metrics for a fintech app, including feature usage, transaction volumes, device types, and premium status. It enables startups to analyze user behavior, optimize product features, and track engagement trends for targeted improvements and growth strategies.

13 cols

feature_engagement_count
feature_engagement_last_datetime
is_premium_user
app_version
+9

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feature_engagement_count: 540
is_premium_user: true
app_version: v2.7.1

Banking & transactions

Bank Transaction Network Graph

Timestamps, amounts, account types, and risk flags

This dataset provides granular records of financial transactions between accounts, including timestamps, amounts, account types, and risk flags. It is optimized for network graph analysis to uncover money flow patterns, identify suspicious activities, and map relationships across banking entities. Ideal for financial crime detection, compliance monitoring, and network risk assessment.

14 cols

transaction_type
amount
currency
status
+10

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transaction_type: payment
amount: 1050.75
currency: USD

Expenses & invoicing

Corporate Expense Reimbursement Analysis

Employee, approver, payment, and vendor details

This dataset provides comprehensive records of corporate expense reimbursements, including employee, approver, payment, and vendor details. It is designed to streamline finance operations, enable robust policy compliance monitoring, and support advanced fraud detection analytics for organizations of any size.

25 cols

expense_category
expense_amount
expense_date
employee_name
+21

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expense_category: Meals
expense_amount: 48.25
employee_name: Sarah Lin

Banking & transactionsTop 10 most opened

Bank Customer Segmentation Dataset

Demographics, account details, product holdings, financial metrics

This dataset contains rich, structured information about bank customers, including demographics, account details, product holdings, financial metrics, and segmentation labels. It is ideal for financial institutions seeking to personalize marketing, manage risk, and identify cross-selling opportunities through data-driven customer segmentation and profiling.

28 cols

segment
account_type
account_balance
num_accounts
+24

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segment: young professional
account_type: checking
account_balance: 5670.43

Markets & investing

Stock Price Volatility Features

Daily-level historical stock price data

This dataset provides granular, daily-level historical stock price data, calculated volatility features over multiple time windows, and annotated significant events impacting stocks. It is ideal for financial modeling, risk analytics, and event-driven market research, enabling robust analysis of price dynamics and volatility patterns.

13 cols

open_price
close_price
high_price
low_price
+9

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open_price: 192.45
close_price: 191.88
high_price: 193.22

Banking & transactionsTop 10 most opened

Digital Wallet Transaction Dataset

Top-ups, peer-to-peer transfers, and merchant payments

This dataset provides granular, transaction-level data on digital wallet usage, including top-ups, peer-to-peer transfers, and merchant payments. It features rich contextual information such as user, wallet, merchant, device, and location details, making it ideal for payment analytics, fraud detection, and fintech product development.

18 cols

transaction_type
transaction_datetime
amount
currency
+14

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transaction_type: top-up
amount: 150.75
currency: USD

Credit & lending

Loan Approval Decision Dataset

Applicant demographics, financial profiles, loan details

This dataset provides a comprehensive record of historical loan applications, including applicant demographics, financial profiles, loan details, and approval decisions. It is ideal for developing and validating credit scoring models, automating loan approval workflows, and analyzing lending risk factors across diverse applicant segments.

28 cols

approval_status
loan_amount
loan_type
loan_term_months
+24

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approval_status: approved
loan_amount: 18500.5
loan_type: personal

Fraud & AML

Financial Transaction Fraud Score

Real-time financial transaction records enriched with device, geo-location

This dataset provides granular, real-time financial transaction records enriched with device, geo-location, and behavioral features, along with model-generated fraud risk scores. It is ideal for training, evaluating, and deploying machine learning models for fraud detection, and supports operational monitoring and regulatory reporting. The comprehensive schema enables deep analysis of transaction patterns and risk factors across accounts, devices, and geographies.

23 cols

transaction_datetime
transaction_amount
transaction_type
previous_transaction_count_24h
+19

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transaction_amount: 24.99
transaction_type: purchase
previous_transaction_count_24h: 3

Credit & lending

Credit Default Early Warning

Customer loan histories, payment behaviors, and annotated default events

This dataset provides a comprehensive view of customer loan histories, payment behaviors, and annotated default events, enabling financial institutions to proactively identify and manage potential credit risks. It includes detailed customer profiles, loan attributes, payment records, and risk indicators, making it ideal for predictive analytics and early warning systems.

30 cols

default_flag
default_reason
credit_score
default_date
+26

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default_flag: false
credit_score: 743
marital_status: single

Expenses & invoicing

Finance Expense Approval Workflow

Business expense approval workflows

This dataset provides a detailed record of business expense approval workflows, capturing every step from submission to audit review. It includes employee, expense, approval, and vendor details, enabling robust compliance monitoring, audit trails, and process automation for finance teams. Ideal for organizations seeking transparency and control over expense management.

30 cols

expense_type
expense_amount
approval_status
approver_name
+26

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expense_type: travel
expense_amount: 1250.54
approval_status: approved

Markets & investing

Finance Investment Portfolio Returns

Absolute and annualized returns, risk-adjusted performance indicators

This dataset provides detailed investment portfolio return metrics, including absolute and annualized returns, risk-adjusted performance indicators, and benchmark comparisons for funds and financial advisors. It is ideal for performance benchmarking, risk analysis, and regulatory reporting in the finance industry.

20 cols

portfolio_name
return_absolute
return_annualized
benchmark_return
+16

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portfolio_name: Global Growth Equity
return_absolute: 14.25
return_annualized: 13.5

Banking & transactions

Finance Personal Savings Patterns

Personal savings patterns, combining customer demographics, income

This dataset provides a detailed view of personal savings patterns, combining customer demographics, income, and account activity with monthly savings breakdowns and product preferences. Financial institutions can leverage this data to design tailored savings products, predict customer retention, and identify at-risk customers for targeted engagement.

27 cols

monthly_savings_pattern
preferred_savings_products
account_type
account_status
+23

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account_type: regular
account_status: active
average_monthly_balance: 7420.49

Expenses & invoicingTop 10 most opened

Finance Expense Categorization Dataset

Transaction dates, amounts, categories, payment methods, merchant details

This dataset contains detailed, structured expense records including transaction dates, amounts, categories, payment methods, merchant details, and location information. It is ideal for personal finance management, business accounting, machine learning classification, and budgeting applications, providing granular insights into spending patterns and expense tracking.

20 cols

category
is_business_expense
amount
currency
+16

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category: Food
is_business_expense: true
amount: 53.25

Payments & messaging

Finance Payment Timing Optimization

Payment delays, overdue status, and payment methods

This dataset provides granular records of customer payment timing for invoices, including payment delays, overdue status, and payment methods. It enables financial institutions and commercial organizations to optimize accounts receivable processes, forecast cash flow, and identify patterns in customer payment behavior for improved risk management and operational efficiency.

22 cols

payment_amount
payment_method
payment_status
payment_date
+18

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payment_amount: 725.5
payment_method: credit_card
payment_status: paid

Credit & lending

Finance Peer-to-Peer Lending Data

Loan terms, borrower and investor attributes, credit scores

This dataset provides detailed, transaction-level records from a peer-to-peer lending platform, including loan terms, borrower and investor attributes, credit scores, repayment history, and loan performance indicators. It is ideal for credit risk modeling, investor analytics, and financial platform optimization, supporting both operational and research applications in alternative lending.

33 cols

loan_amount
funded_amount
interest_rate
loan_term_months
+29

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loan_amount: 8500.25
funded_amount: 8500.25
interest_rate: 8.75

Credit & lending

Finance Mortgage Application Features

Applicant demographics, financials, property details, and application status

This dataset provides a detailed, structured view of mortgage application features, including applicant demographics, financials, property details, and application status. It is ideal for banks and insurtech startups seeking to build AI/ML models for risk assessment, approval automation, and portfolio analytics. The schema is designed for high data integrity and regulatory compliance.

37 cols

applicant_ssn
co_applicant_present
co_applicant_annual_income
co_applicant_credit_score
+33

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applicant_ssn: 529-34-1832
co_applicant_present: true
co_applicant_annual_income: 102000

Payments & messaging

Finance Payment Method Preference

Insights into customer payment method preferences, usage frequency

This dataset provides detailed insights into customer payment method preferences, usage frequency, and transaction values across various demographic and business segments. It enables financial service providers to analyze trends, optimize product offerings, and tailor marketing strategies based on customer behavior and segment characteristics.

11 cols

payment_method
is_primary_method
segment
frequency
+7

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payment_method: Mobile Wallet
is_primary_method: true
segment: Gen Z

Credit & lending

Finance Loan Approval Modeling

Applicant demographics, financial profiles, loan request details

This dataset contains detailed records of loan applications, including applicant demographics, financial profiles, loan request details, approval outcomes, and risk indicators. It is ideal for building predictive models for loan approval, credit risk assessment, and applicant profiling in banking and fintech environments.

30 cols

loan_amount
loan_term_months
loan_purpose
loan_approval_status
+26

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loan_amount: 34000
loan_term_months: 72
loan_purpose: home

Credit & lending

Finance Credit Card Spend Analysis

Customer demographics, card details, merchant information

This dataset provides granular credit card transaction records, including customer demographics, card details, merchant information, and transaction metadata. It is ideal for banks and fintechs seeking to analyze spending patterns, segment customers, and model risk, enabling data-driven product design and market research.

22 cols

card_type
card_issuer_bank
transaction_datetime
transaction_amount
+18

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card_type: Visa
card_issuer_bank: Chase
transaction_amount: 120.5

Banking & transactions

Financial Transaction Pattern Analysis

Financial transaction records enriched with customer segmentation

This dataset provides granular financial transaction records enriched with customer segmentation, merchant categorization, and risk scoring. It enables banks and fintechs to analyze spending patterns, detect fraud, and optimize product offerings for targeted customer profiles. The dataset is ideal for risk management, customer analytics, and personalized financial services.

16 cols

transaction_amount
transaction_type
transaction_date
currency
+12

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transaction_amount: 11245.85
transaction_type: Purchase
currency: USD

Payments & messaging

Supplier Invoice Payment Timelines

Submission, due, and payment dates, amounts, and payment statuses

This dataset provides detailed, chronological records of supplier invoices, including submission, due, and payment dates, amounts, and payment statuses. It enables comprehensive analysis of cash flow, payment compliance, and supplier management, supporting audit, financial planning, and process optimization. The flat structure makes it ideal for integration with BI tools and compliance reporting systems.

19 cols

supplier_name
invoice_number
invoice_amount
payment_amount
+15

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supplier_name: Northland Foods Inc.
invoice_number: NF-20240101-001
invoice_amount: 495.25

Banking & transactions

Cardholder Dispute Resolution Cases

Transaction details, dispute reasons, status progression

This dataset provides detailed, synthetic records of cardholder dispute cases, including transaction details, dispute reasons, status progression, and resolution outcomes. It is designed to support the development and validation of fraud detection and compliance models, as well as operational analytics for financial institutions. All sensitive fields are masked or synthetic to ensure privacy while retaining analytical value.

26 cols

dispute_reason_code
dispute_status
resolution_outcome
dispute_date
+22

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dispute_reason_code: DUPLICATE_CHARGE
dispute_status: RESOLVED
resolution_outcome: CARDHOLDER_REFUNDED

Real codes. Never real customers.

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

Code systemDatasets using it, of 248

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 248

currency99
transaction_id62
customer_id53
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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