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Client Financial Health & Credit Score Data
Finance & Banking Analytics
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About
Credit risk data evaluates the probability of a financial loss when a borrower fails to repay a loan. This data product contains information for assessing bank customer credit risk, focusing on the likelihood that a lender may not receive owed principal and interest. It is designed for analysis that can help mitigate interruptions in cash flows and increased collection costs associated with loan defaults.
Columns
- Debt (Задолженность): The total outstanding debt amount owed by the client.
- Overdue Days (Просрочка, дни): The number of days a payment is overdue.
- Initial Limit (Первоначальный лимит): The initial credit limit assigned to the client.
- Birth Date (BIRTHDATE): The client's date of birth.
- Sex (SEX): The client's gender.
- Education (EDU): The client's level of education.
- Income (INCOME): The client's monthly or annual income.
- Loan Term (TERM): The duration of the loan agreement in months.
- Credit History Rating (Рейтинг кредитной истории): A rating reflecting the client's credit history.
- Living Area (LV_AREA): The geographical region where the client resides.
- Settlement Name (LV_SETTLEMENTNAME): The name of the city or town where the client lives.
- Industry Name (INDUSTRYNAME): The industry sector where the client is employed.
- Probability of Default (PDN): The estimated likelihood that the client will default.
- Client ID (CLIENTID): A unique identifier for the client.
- Scoring Mark (SCORINGMARK): A credit score assigned to the client.
- Underage Children Count (UNDERAGECHILDRENCOUNT): The number of underage children the client has.
- Velcom Scoring (VELCOMSCORING): A specific, possibly telecom-related, scoring metric.
- Family Status (FAMILYSTATUS): The client's marital or family status.
Distribution
The dataset is provided in a single CSV file named
bank_credit_scoring.csv with a size of 3.15 MB. It is structured with 18 columns and contains 18,400 valid records. The data is expected to be updated annually.Usage
This data is ideal for developing and validating credit risk models, analysing factors that contribute to loan defaults, and creating financial assessment tools. It can be used for training machine learning algorithms to predict the probability of default and for market segmentation analysis based on customer demographics and financial behaviour.
Coverage
The data covers a time range from 2024 to 2025. Geographically, it has a significant focus on clients residing in Minsk and the Minsk region. Demographically, the client birth dates range from March 1956 to April 2005. The dataset includes a gender distribution of approximately 61% male and 39% female clients. Some fields, such as
LV_AREA, SCORINGMARK, and VELCOMSCORING, contain missing values.License
CC0: Public Domain
Who Can Use It
- Data Scientists: For building predictive models to assess creditworthiness and default probability.
- Financial Analysts: To understand risk factors and trends in consumer lending.
- Academic Researchers: For studies on economic behaviour, credit scoring methodologies, and financial inclusion.
- Banking Professionals: To refine internal credit scoring systems and lending policies.
Dataset Name Suggestions
- Bank Customer Credit Risk Profiles 2024-2025
- Consumer Loan Default Prediction Data
- Client Financial Health & Credit Score Data
- European Bank Lending and Risk Analysis
- Credit Risk Assessment & Scoring Factors
Attributes
Original Data Source: Client Financial Health & Credit Score Data
QUALITY