---
license: cc-by-4.0
task_categories:
  - tabular-classification
  - tabular-regression
language:
  - en
tags:
  - financial-inclusion
  - fintech
  - africa
  - synthetic-data
  - sub-saharan-africa
  - credit-access
size_categories:
  - 10K<n<100K
---

# Credit Access Patterns in Africa

Synthetic dataset modeling credit access, approval, and repayment patterns across 15 Sub-Saharan African countries from 2018-2025.

## Dataset Description

This dataset simulates individual-level credit seeking behavior, application outcomes, and repayment patterns. It captures the spectrum of formal and informal credit sources, from commercial banks to family loans, and the barriers faced by those seeking credit.

### Key Statistics

| Metric | Value |
|--------|-------|
| Total Records | 15,000 |
| Countries | 15 |
| Time Period | 2018-2025 |
| Credit Need Rate | ~40% |
| Formal Credit Access | ~15% |
| Approval Rate (formal) | ~45% |
| Approval Rate (informal) | ~75% |
| Avg Loan Amount | $500-2,000 USD |
| Interest Rate Range | 5-120% APR |

### Coverage by Scenario

- `low_burden`: 4,000 records
- `moderate_burden`: 5,000 records
- `high_burden`: 6,000 records

## Column Descriptions

| Column | Type | Description |
|--------|------|-------------|
| country | string | One of 15 SSA countries |
| year | int | Year (2018-2025) |
| urban_rural | string | Urban or rural location |
| gender | string | Male or female |
| age | int | Age in years (18-70) |
| age_group | string | Age bracket |
| income_quintile | string | Income group (lowest to highest) |
| education | string | Education level |
| employment_status | string | Employment category |
| monthly_income_usd | float | Monthly income in USD |
| income_stability | int | Income stability score (1-10) |
| credit_history | string | Credit history quality |
| has_bank_account | int | Has bank account (0/1) |
| credit_score | int | Credit score (0-850) |
| credit_need | int | Needs credit (0/1) |
| preferred_credit_source | string | Preferred lender type |
| loan_amount_requested_usd | float | Requested loan amount |
| collateral_available | string | Collateral type |
| loan_purpose | string | Purpose of loan |
| approved | int | Loan approved (0/1) |
| loan_disbursed_usd | float | Disbursed loan amount |
| interest_rate | float | Annual interest rate |
| loan_term_months | int | Loan term in months |
| monthly_payment_usd | float | Monthly payment |
| repayment_status | string | Current repayment status |
| rejection_reason | string | Reason for rejection |
| scenario | string | Burden scenario label |

## Usage Example

```python
import pandas as pd

# Load the combined dataset
df = pd.read_csv("credit_access_combined.csv")

# Analyze credit sources
sources = df[df['credit_need'] == 1]['preferred_credit_source'].value_counts()

# Predict loan approval
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

seekers = df[df['credit_need'] == 1].copy()
features = ['age', 'credit_score', 'income_stability', 'has_bank_account', 'collateral_available']
X = pd.get_dummies(seekers[features], drop_first=True)
y = seekers['approved']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = RandomForestClassifier().fit(X_train, y_train)
print(f"Accuracy: {model.score(X_test, y_test):.2f}")
```

## Research Sources

- World Bank Global Findex Database 2025
- IMF Financial Access Survey 2024
- FSD Kenya credit market research 2024
- CGAP credit reporting research

## Citation

```bibtex
@dataset{credit_access_africa_2025,
  title={Credit Access Patterns in Africa},
  year={2025},
  note={Synthetic dataset based on World Bank Findex and IMF data}
}
```