Datasets in this collection
Finance Expense Categorization Dataset, Stock Portfolio Allocation Dataset, SME Lending Decision Dataset, Loan Default Probability Dataset, Bank Customer Segmentation Dataset, Digital Wallet Transaction Dataset, Loan Approval Decision Dataset, Loan Default Prediction Dataset, Banking Transaction Categorization Dataset, Banking Transaction Graphs Dataset, Payment Fraud Detection Dataset, Finance Peer-to-Peer Lending Data
Finance20 cols · 200 rows
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.
| merchant_name | amount | category |
|---|
| The Grove Bistro | 53.25 | Food |
| Pacific Utilities | 0 | Other |
| Netflix Australia | 19.99 | Entertainment |
currencyis_business_expensereceipt_available+14 more
Finance14 cols · 200 rows
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.
| investor_profile | asset_class_equity_pct | asset_class_fixed_income_pct |
|---|
| conservative | 12 | 81 |
| aggressive | 74 | 4 |
| balanced | 47 | 35 |
account_idallocation_dateasset_class_cash_pct+8 more
Finance32 cols · 200 rows
This dataset provides a comprehensive view of SME loan applications, featuring detailed business profiles, financial metrics, application specifics, and lending decisions. It enables robust credit risk modeling, operational analytics, and benchmarking for financial institutions serving small and medium enterprises.
| business_name | business_type | collateral_type |
|---|
| Greenfield Manufacturing Co | Corporation | Property |
| Sunrise Retail Group | LLC | Inventory |
| TechNova Solutions | Corporation | None |
application_idbusiness_idindustry_sector+26 more
Finance24 cols · 200 rows
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.
| employment_status | marital_status | residential_status |
|---|
| employed | married | mortgage |
| employed | single | rent |
| self-employed | single | rent |
applicant_idloan_idapplication_date+18 more
Finance28 cols · 200 rows
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.
| first_name | last_name | account_type |
|---|
| Alex | Jones | checking |
| Meera | Singh | student |
| Riley | Brown | savings |
customer_idgenderdate_of_birth+22 more
Finance18 cols · 200 rows
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.
| transaction_type | status | merchant_name |
|---|
| top-up | completed | – |
| transfer | pending | – |
| merchant-payment | completed | Tesco Express |
transaction_iduser_idtransaction_datetime+12 more
Finance28 cols · 200 rows
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.
| approval_status | loan_type | applicant_name |
|---|
| approved | personal | Jessica Lee |
| pending | student | Minho Park |
| approved | mortgage | Ricardo Alvarez |
application_idapplicant_idapplication_date+22 more
Finance25 cols · 200 rows
This dataset provides detailed borrower profiles and comprehensive loan performance records, including payment history and default status, to support advanced risk modeling and mitigation strategies. It is ideal for predictive analytics, credit scoring, and financial risk assessment in lending environments.
| borrower_name | borrower_employment_status | loan_status |
|---|
| Jacob Thompson | employed | active |
| Priya Iyer | employed | closed |
| Lucas Brown | retired | delinquent |
loan_idborrower_idborrower_dob+19 more
Finance18 cols · 200 rows
This dataset provides detailed, labeled banking transaction records, including transaction amounts, merchant details, categories, and recurrence information. It is ideal for developing and benchmarking expense tracking, personal finance management, and automated categorization tools. The dataset enables granular financial analysis and supports machine learning applications for transaction classification.
| merchant_name | category | subcategory |
|---|
| Whole Foods Market | Groceries | Supermarkets |
| ACME Payroll | Salary | – |
| Uber | Transportation | Taxi/Rideshare |
transaction_idaccount_idtransaction_date+12 more
Finance16 cols · 200 rows
This dataset provides detailed, interconnected banking transaction records, capturing sender and receiver relationships, transaction metadata, and anomaly flags. Designed for network analytics, it enables advanced anti-money laundering (AML) detection, fraud analysis, and financial behavior modeling by representing transactions as a directed graph. The flat structure ensures easy integration with machine learning and graph analytics tools.
| currency | transaction_type | transaction_status |
|---|
| USD | payment | completed |
| EUR | transfer | pending |
| USD | payment | completed |
transaction_idtimestampamount+10 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
Finance33 cols · 200 rows
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.
| loan_status | borrower_employment_status | investor_type |
|---|
| active | employed | individual |
| completed | retired | institutional |
| active | student | individual |
loan_idborrower_idinvestor_id+27 more