Longsssss/cofinfad

---
license: odc-by
language:
- en
size_categories:
- 1M<n<10M
---
# COFINFAD: Colombian Fintech Financial Analytics Dataset


COFINFAD (Colombian Fintech Financial Analytics Dataset) is a dataset containing almost 12 months of transactional and demographic data from an anonymous Colombian fintech company. This dataset is designed to facilitate research in customer behavior analysis, churn prediction, and financial pattern recognition in the Latin American fintech sector.

## Dataset Description

- **Time Period**: January 4, 2023 to December 29, 2023
- **Number of Customers**: 48,723
- **Number of Transactions**: 3,159,157
- **Number of Variables**: 57
- **File Format**: CSV (Comma Separated Values)
- **Currency**: All monetary values are in Colombian Pesos (COP)

## Files

1. `customer_data.csv`: Contains demographic, behavioral, and derived data for each customer
2. `transactions_data.csv`: Contains individual transaction records

## Key Features

- Customer demographics (age, gender, location, income bracket, etc.)
- Product usage (savings account, credit card, personal loan, etc.)
- Transaction history (frequency, volume, types)
- Customer engagement metrics (app usage, feature diversity)
- Customer feedback and satisfaction scores
- Derived metrics (churn probability, customer lifetime value)

## Potential Applications

This dataset can be used for various research purposes, including:
- Customer churn prediction
- Customer segmentation
- Product recommendation systems
- Customer lifetime value optimization
- Financial inclusion studies
- App engagement analysis

## Data Dictionary

| No. | Variable Name | Definition | CSV Data Type |
|-----|---------------|------------|---------------|
| 1 | customer_id | Unique identifier for each customer | Integer |
| 2 | age | Customer's age in years | Integer |
| 3 | gender | Customer's gender | String |
| 4 | location | Customer's city and state | String |
| 5 | income_bracket | Customer's income category | String |
| 6 | occupation | Customer's job or profession | String |
| 7 | education_level | Highest level of education attained by the customer | String |
| 8 | marital_status | Customer's marital status | String |
| 9 | household_size | Number of people in the customer's household | Integer |
| 10 | acquisition_channel | How the customer was acquired (e.g., organic, referral) | String |
| 11 | customer_segment | Category assigned to the customer based on their behavior | String |
| 12 | savings_account | Whether the customer has a savings account (True/False) | Boolean |
| 13 | credit_card | Whether the customer has a credit card (True/False) | Boolean |
| 14 | personal_loan | Whether the customer has a personal loan (True/False) | Boolean |
| 15 | investment_account | Whether the customer has an investment account (True/False) | Boolean |
| 16 | insurance_product | Whether the customer has an insurance product (True/False) | Boolean |
| 17 | active_products | Number of active financial products the customer has | Integer |
| 18 | app_logins_frequency | Number of times the customer logs into the app per month | Integer |
| 19 | feature_usage_diversity | Number of unique features used by the customer in the app | Integer |
| 20 | bill_payment_user | Whether the customer uses bill payment feature (True/False) | Boolean |
| 21 | auto_savings_enabled | Whether the customer has enabled auto-savings feature (True/False) | Boolean |
| 22 | credit_utilization_ratio | Ratio of credit used to credit available | Float |
| 23 | international_transactions | Number of international transactions made by the customer | Integer |
| 24 | failed_transactions | Number of failed transactions for the customer | Integer |
| 25 | tx_count | Total number of transactions made by the customer | Integer |
| 26 | avg_tx_value | Average value of customer's transactions | Float |
| 27 | total_tx_volume | Total value of all transactions made by the customer | Float |
| 28 | first_tx | Date of the customer's first transaction | Date |
| 29 | last_tx | Date of the customer's most recent transaction | Date |
| 30 | base_satisfaction | Base satisfaction score for the customer | Float |
| 31 | tx_satisfaction | Satisfaction score based on transaction history | Float |
| 32 | product_satisfaction | Satisfaction score based on product usage | Float |
| 33 | satisfaction_score | Overall customer satisfaction score | Integer |
| 34 | nps_score | Net Promoter Score for the customer | Integer |
| 35 | last_survey_date | Date when the customer last completed a survey | Date |
| 36 | support_tickets_count | Number of support tickets opened by the customer | Integer |
| 37 | resolved_tickets_ratio | Ratio of resolved support tickets to total tickets | Float |
| 38 | app_store_rating | Customer's rating of the app in the app store | Float |
| 39 | feedback_sentiment | Sentiment analysis of customer's feedback | String |
| 40 | feature_requests | Features requested by the customer | String |
| 41 | complaint_topics | Main topics of customer's complaints | String |
| 42 | clv_segment | Customer Lifetime Value segment | String |
| 43 | monthly_transaction_count | Average number of transactions per month | Float |
| 44 | average_transaction_value | Average value of customer's transactions | Float |
| 45 | total_transaction_volume | Total value of all transactions made by the customer | Float |
| 46 | transaction_frequency | Number of transactions per day | Float |
| 47 | last_transaction_date | Date of the customer's most recent transaction | Date |
| 48 | preferred_transaction_type | Most frequent type of transaction for the customer | String |
| 49 | first_transaction_date | Date of the customer's first transaction | Date |
| 50 | weekend_transaction_ratio | Ratio of transactions made on weekends | Float |
| 51 | avg_daily_transactions | Average number of transactions per day | Float |
| 52 | customer_tenure | Length of time as a customer in months | Float |
| 53 | churn_probability | Predicted probability of customer churn | Float |
| 54 | customer_lifetime_value | Estimated total value of the customer to the business | Float |
| 55 | date | Date of a specific transaction | Date |
| 56 | amount | Amount of a specific transaction | Float |
| 57 | type | Type of a specific transaction | String |

## Data Preprocessing and Ethical Considerations

The data has been anonymized to protect customer privacy. Categorical variables have been encoded, and several derived features have been created to facilitate analysis. Researchers are encouraged to handle this data responsibly and in compliance with relevant data protection regulations.

## Citation

If you use this dataset in your research, please cite it as follows:

```
Muñoz Guerrero, L. E., Ceballos, Y. F., & Trejos Rojas, L. D. (2024). COFINFAD: Colombian Fintech Financial Analytics Dataset [Dataset]. Hugging Face. https://huggingface.co/datasets/luisdavidtrejosrojas/cofinfad
```

## License

This dataset is licensed under the Open Data Commons Attribution License (ODC-By).

## Contact

For any questions or feedback regarding this dataset, please contact [e-mail]