**These datasets are for educational purposes only. Any misuse for illegal or unethical activities is strictly prohibited. All generated data is fictional and has no real-world validity.
Contains details of loans, including amount, interest rate, term, and status.
| Loan ID | Loan Amount | Interest Rate | Loan Term (Months) | Purpose | Origination Date | Loan Status | Monthly Payment | State |
|---|---|---|---|---|---|---|---|---|
| LN10235 | 15,000 | 9.5% | 60 | Debt Consolidation | 2023-06-15 | Current | 314.23 | CA |
Includes borrower demographics, income, credit score, and employment status.
| Borrower ID | Name | Age | Gender | Annual Income | Credit Score | Employment Status | Debt-to-Income Ratio (%) | Home Ownership | Marital Status | Dependents |
|---|---|---|---|---|---|---|---|---|---|---|
| BR56422 | Maria Lopez | 34 | Female | 85,000 | 725 | Full-time | 24.6 | Mortgage | Married | 2 |
Tracks loan repayments, including dates, amounts, and payment status.
| Loan ID | Repayment Date | Amount Paid | Remaining Balance | Payment Status | Fees Incurred |
|---|---|---|---|---|---|
| LN10235 | 2024-03-01 | 314.23 | 11,235.77 | On Time | 0.00 |
Records inquiries made on a borrower’s credit history by different lenders.
| Inquiry ID | Borrower ID | Inquiry Date | Lender Name | Credit Inquiry Purpose | Hard Inquiry |
|---|---|---|---|---|---|
| INQ4489 | BR56422 | 2023-05-22 | LendSure Capital | Personal Loan Application | Yes |
Includes risk evaluation metrics such as probability of default and credit utilization.
| Assessment ID | Loan ID | Borrower ID | Risk Score | Probability of Default (%) | Debt-to-Income Ratio (%) | Credit Utilization (%) | Employment Stability |
|---|---|---|---|---|---|---|---|
| RSK9082 | LN10235 | BR56422 | 82 | 3.2 | 24.6 | 45.8 | High |
Provides insights into loan funding, including investor participation and funding platforms.
| Funding ID | Loan ID | Funded Amount | Investors | Funding Date | Lender Platform |
|---|---|---|---|---|---|
| FND7753 | LN10235 | 15,000 | 12 | 2023-06-10 | LendCircle |
Monitors loan performance, including delinquencies, charge-offs, and payments.
| Performance ID | Loan ID | Charge-Off Date | Delinquency Count | Principal Paid | Interest Paid | Late Fees Paid |
|---|---|---|---|---|---|---|
| PRF2120 | LN10235 | -- | 0 | 3,764.50 | 948.65 | 0.00 |
Lists information on collateral used for securing loans.
| Collateral ID | Loan ID | Collateral Type | Collateral Value | Collateral Condition | Appraisal Date |
|---|---|---|---|---|---|
| CLT9881 | LN10235 | Vehicle | 18,000 | Good | 2023-06-01 |
Flags potential fraudulent loan activities based on various indicators.
| Transaction ID | Loan ID | Borrower ID | Fraudulent Indicator | Reason for Flagging | Date Flagged |
|---|---|---|---|---|---|
| TXF3091 | LN10988 | BR56910 | Yes | Inconsistent income documents | 2024-02-12 |
Lending Club-style datasets mimic the structure and features found in peer-to-peer lending platforms. They typically include borrower profiles, loan details, credit history, repayment status, interest rates, and risk indicators used to evaluate and predict loan performance.
Common features include:
Lending platforms use historical repayment data to build credit scoring models that assess the probability of loan default. Predictive models like logistic regression and random forests help determine approval and interest rates based on borrower risk.
By analyzing inconsistencies in application data, behavioral anomalies, and high-risk patterns, lenders can identify potential fraud. Machine learning models trained on loan datasets help flag suspicious applications before approval.
While many peer-to-peer loans are unsecured, collateral-based loans may include additional data such as asset type, estimated value, and lien status. This helps lenders assess the recovery value in case of default.
Yes. You can use the data to simulate lending scenarios, analyze risk-return profiles, calculate interest income, and assess portfolio performance over time—all within Excel or data science tools.
By exploring trends in repayment, default, loan purpose, and credit utilization, analysts can understand borrower risk profiles and optimize lending criteria for future applicants.
Absolutely. You can perform:
Yes. All datasets on this platform are synthetically generated and anonymized. They mimic real-world loan structures without exposing any personal or confidential borrower information.
Definitely. You can use them to train and test classification models that identify potential fraud based on unusual patterns in credit history, loan amount, or inconsistent personal details.
These datasets offer a real-world blend of financial, behavioral, and credit-related features, making them excellent for exploring classification, regression, portfolio optimization, and even natural language processing (for loan purposes).
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