---
license: apache-2.0
task_categories:
- tabular-classification
- tabular-regression
tags:
- nigeria
- mobile-money
- fraud-detection
- churn-prediction
- digital-payments
- fintech
- financial-inclusion
language:
- en
size_categories:
- 1M<n<10M
---

# Nigerian Mobile Money Wallet Transactions

**Dataset Type**: Banking & Finance - Digital Payments  
**Version**: 1.0  
**License**: Apache 2.0  
**Language**: English  
**Geography**: Nigeria

---

## Dataset Description

Synthetic Nigerian mobile money wallet transactions with fraud and churn labels. Covers major providers (OPay, PalmPay, MTN MoMo, Airtel Money) with realistic P2P, airtime, bill payment, and cash-in/out patterns.

**Use Cases**:
- Mobile money fraud detection
- Customer churn prediction
- Financial inclusion analysis
- Agent network optimization
- Digital wallet product development

---

## Dataset Statistics

- **Rows**: 4,000,000 (pilot: 10,000)
- **Columns**: 13
- **Time Range**: 2024-01-01 to 2024-06-30 (6 months)
- **Fraud Prevalence**: 1.5%
- **Churn Prevalence**: 6%

---

## Schema

| Column | Type | Description |
|--------|------|-------------|
| `transaction_id` | string | Unique transaction identifier (UUID) |
| `wallet_id` | string | Wallet identifier (WLT-########) |
| `timestamp` | datetime | Transaction timestamp (Africa/Lagos) |
| `transaction_type` | category | `p2p_send`, `p2p_receive`, `cashin`, `cashout`, `airtime`, `data`, `billpay` |
| `amount_ngn` | float | Transaction amount in Naira |
| `fee_ngn` | float | Transaction fee |
| `balance_after_ngn` | float | Wallet balance after transaction |
| `agent_id` | string | Agent ID (for cashin/cashout) |
| `channel` | category | `ussd`, `app`, `agent`, `web` |
| `device_os` | category | `android`, `feature_phone`, `ios` |
| `kyc_tier` | category | `tier1`, `tier2`, `tier3` |
| `fraud_flag` | bool | **LABEL**: True if fraudulent |
| `churn_30d` | bool | **LABEL**: True if churned in 30 days |

---

## Label Distribution

### Fraud Flag
- **Positive**: 1.5%
- **Negative**: 98.5%

**Fraud Drivers**:
- **High Cashout Ratio** (cashout/cashin >2): 12x risk
- **New Wallet** (<7 days old): 5x risk
- **High Velocity** (>20 txns/24h): 15x risk
- **Night Hours**: 3x risk
- **Round Amounts** (₦50k, ₦100k): 4x risk

### Churn (30 days)
- **Positive**: 6%
- **Negative**: 94%

**Churn Drivers**:
- **Dormant** (14+ days no activity): 8x risk
- **Low Diversity** (only airtime): 3x risk
- **Failed Transactions** (>3 failures): 4x risk
- **Tier 1 Only** (never upgraded): 2.5x risk
- **No Bill Payments**: 2x risk

---

## Data Distributions

### Transaction Types
| Type | Percentage | Description |
|------|------------|-------------|
| Airtime | 25% | Mobile phone top-up |
| P2P Send | 20% | Send money to another wallet |
| P2P Receive | 20% | Receive money from another wallet |
| Bill Payment | 15% | Utilities, cable TV, internet |
| Cash-in | 10% | Deposit to wallet |
| Cash-out | 8% | Withdraw from wallet |
| Data Bundle | 2% | Internet data purchase |

### Channels
| Channel | Percentage | Description |
|---------|------------|-------------|
| USSD | 50% | *99# codes (no smartphone needed) |
| App | 35% | Smartphone apps |
| Agent | 13% | Through banking agents |
| Web | 2% | Web portal |

### Device OS
| OS | Percentage |
|----|------------|
| Android | 70% |
| Feature Phone | 25% |
| iOS | 5% |

### KYC Tiers
| Tier | Daily Limit | Percentage |
|------|-------------|------------|
| Tier 1 | ₦50,000 | 60% |
| Tier 2 | ₦200,000 | 35% |
| Tier 3 | ₦5,000,000 | 5% |

---

## Mobile Money Providers

### Market Share
| Provider | Share | Features |
|----------|-------|----------|
| **OPay** | 35% | Cashback rewards (1%) |
| **PalmPay** | 30% | Referral bonuses (₦500) |
| **MTN MoMo** | 20% | Network advantage (linked to MTN) |
| **Airtel Money** | 10% | Airtel network integration |
| **9PSB** | 5% | 9mobile payment service |

---

## Nigerian Context

### Usage Patterns
✅ **Airtime Dominant**: Small frequent airtime purchases (₦200-₦1,500)  
✅ **Family Transfers**: 65% send to 3-5 family members regularly  
✅ **Agent Preference**: Urban 75% cashout via agents, Rural 45%  
✅ **USSD Heavy**: 50% still use USSD (*99#) over apps  
✅ **Bill Payments**: DSTV, GOtv, EKEDC, IKEDC, internet

### Financial Inclusion
- **Unbanked Access**: Mobile money reaches 30M+ unbanked Nigerians
- **Rural Penetration**: Feature phone support enables rural access
- **Low Barriers**: Tier 1 KYC requires only phone number
- **Micro-transactions**: Enables ₦50-₦500 transactions

### Transaction Patterns
- **Morning Peak**: 9-12am (before work)
- **Evening Peak**: 6-8pm (after work, family transfers)
- **Weekend Dip**: -20% on Sundays
- **Month-End Surge**: +50% last week of month (salary days)

---

## Files

```
mobile_money/
├── README.md
├── nigerian_mobile_money_pilot.parquet (10k, 0.62 MB)
├── nigerian_mobile_money.parquet (4M, ~320 MB) - Coming Soon
├── nigerian_mobile_money.csv (4M, ~640 MB) - Coming Soon
└── mobile_money_sample.csv (100 rows)
```

---

## Usage Example

```python
import pandas as pd
from sklearn.ensemble import RandomForestClassifier

# Load data
df = pd.read_parquet('nigerian_mobile_money.parquet')

# Fraud detection features
df['is_cashout'] = (df['transaction_type'] == 'cashout').astype(int)
df['is_night'] = ((df['timestamp'].dt.hour >= 22) | 
                  (df['timestamp'].dt.hour <= 6)).astype(int)
df['is_round'] = df['amount_ngn'].isin([50000, 100000, 200000]).astype(int)
df['hour'] = df['timestamp'].dt.hour

fraud_features = ['amount_ngn', 'fee_ngn', 'is_cashout', 
                   'is_night', 'is_round', 'hour']

X_fraud = df[fraud_features]
y_fraud = df['fraud_flag']

# Train fraud model
fraud_model = RandomForestClassifier(n_estimators=100)
fraud_model.fit(X_fraud, y_fraud)

# Churn prediction features
df['balance_low'] = (df['balance_after_ngn'] < 1000).astype(int)
df['is_tier1'] = (df['kyc_tier'] == 'tier1').astype(int)
df['only_airtime'] = (df['transaction_type'] == 'airtime').astype(int)

churn_features = ['balance_low', 'is_tier1', 'only_airtime', 'amount_ngn']

X_churn = df[churn_features]
y_churn = df['churn_30d']

# Train churn model
churn_model = RandomForestClassifier(n_estimators=100)
churn_model.fit(X_churn, y_churn)
```

---

## Citation

```bibtex
@dataset{nigerian_mobile_money_2025,
  author = {Electric Sheep Africa},
  title = {Nigerian Mobile Money Wallet Transactions Dataset},
  year = {2025},
  publisher = {Hugging Face}
}
```

---

**Last Updated**: 2025-10-19  
**Status**: ✅ Pilot Validated