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Loan Approval Prediction Data
Finance & Banking Analytics
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About
This is a Loan Classification Dataset detailing customers’ financial and credit history metrics used in the decision process for accepting or rejecting loan applications. The primary purpose is to support the creation of predictive models that can assess borrower risk, helping institutions determine whether to accept a loan application based on the provided customer characteristics.
Columns
The data includes numerous variables describing the borrower’s status, loan details, and credit history:
- loan_amnt: The listed amount of the loan applied for by the borrower.
- annual_inc: The self-reported annual income provided by the borrower.
- emp_length: Employment length, typically ranging from 0 (less than one year) to 10 (ten or more years).
- home_ownership: The borrower’s home ownership status, which can be RENT, OWN, MORTGAGE, or OTHER.
- dti: Debt-to-Income ratio, calculated using the borrower’s total monthly debt payments divided by their self-reported monthly income.
- loan_status: The current status of the loan (e.g., accepted, rejected, charged off).
- fico_range_high / fico_range_low: The boundaries of the borrower’s FICO score range at loan origination.
- term: The number of payments on the loan, specified in months (either 36 or 60).
- acc_now_delinq: The count of accounts on which the borrower is currently delinquent.
- purpose: The category provided by the borrower for why the loan was requested.
Distribution
The dataset is typically distributed in a tabular format. While the full structure includes extensive features, source samples indicate two primary tables:
LoanStats (117 total rows and 2 columns) and RejectStats (9 total rows and 2 columns). The overall data is suitable for distribution in file formats such as CSV, with accompanying data dictionaries detailing the meaning of each variable.Usage
This data is ideally suited for developing credit risk models and performing financial analysis. Specific use cases include training supervised machine learning algorithms to predict loan acceptance status, calculating default probabilities based on borrower attributes, and conducting statistical analysis to identify key drivers influencing loan decisions. It aids financial institutions in automating and improving their lending strategies.
Coverage
The data covers borrower demographic information such as home ownership status and geographical information via the first three digits of the borrower's zip code and their state of residence. Financial history includes metrics reflecting credit inquiries (e.g.,
inq_last_6mths) and delinquency records spanning up to the last 24 months. Time-related features include employment length and the date the loan was funded (issue_d). It includes records for both individual and joint applications.License
CC0: Public Domain
Who Can Use It
- Data Scientists: For building predictive models for credit scoring and risk management.
- Banking and Insurance Professionals: For strategic risk assessment, process automation, and evaluating lending criteria.
- Academics and Researchers: For studying consumer finance trends, debt behaviour, and the factors affecting loan outcomes.
- Data Analysts: For generating reports on portfolio health and customer segment performance.
Dataset Name Suggestions
- Loan Approval Prediction Data
- Customer Financial Risk Assessment
- Credit History Decision Data
- Lender Acceptance Metrics
Attributes
Original Data Source: Loan Approval Prediction Data
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