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]

license: ODC-By（开放数据 Commons 署名许可证，Open Data Commons Attribution License）
language:
- 英语
size_categories:
- 100万<记录数<1000万
---
# COFINFAD：哥伦比亚金融科技金融分析数据集（Colombian Fintech Financial Analytics Dataset）


COFINFAD（哥伦比亚金融科技金融分析数据集，Colombian Fintech Financial Analytics Dataset）是一份源自匿名哥伦比亚金融科技公司、涵盖近12个月交易与人口统计数据的数据集。本数据集旨在助力拉丁美洲金融科技领域的客户行为分析、客户流失预测及金融模式识别相关研究。

## 数据集说明

- **时间范围**：2023年1月4日至2023年12月29日
- **客户总量**：48,723位
- **交易总笔数**：3,159,157笔
- **变量总数**：57个
- **文件格式**：CSV（逗号分隔值，Comma Separated Values）
- **货币单位**：所有货币金额均以哥伦比亚比索（COP，Colombian Pesos）计价

## 文件列表

1. `customer_data.csv`：存储每位客户的人口统计、行为及衍生数据
2. `transactions_data.csv`：存储单笔交易记录

## 核心特征

- 客户人口统计信息（年龄、性别、所在地、收入层级等）
- 产品使用情况（储蓄账户、信用卡、个人贷款等）
- 交易历史（交易频率、交易规模、交易类型）
- 客户参与度指标（应用使用情况、功能使用多样性）
- 客户反馈与满意度评分
- 衍生指标（客户流失概率、客户终身价值）

## 潜在应用场景

本数据集可应用于多类研究场景，包括：
- 客户流失预测
- 客户细分
- 产品推荐系统
- 客户终身价值优化
- 金融普惠研究
- 应用参与度分析

## 数据字典

| 序号 | 变量名 | 定义 | CSV数据类型 |
|-----|---------------|------------|---------------|
| 1 | customer_id | 每位客户的唯一标识符 | 整数型（Integer） |
| 2 | age | 客户年龄（单位：年） | 整数型（Integer） |
| 3 | gender | 客户性别 | 字符串型（String） |
| 4 | location | 客户所在城市与州/省 | 字符串型（String） |
| 5 | income_bracket | 客户收入层级 | 字符串型（String） |
| 6 | occupation | 客户职业 | 字符串型（String） |
| 7 | education_level | 客户最高学历 | 字符串型（String） |
| 8 | marital_status | 客户婚姻状况 | 字符串型（String） |
| 9 | household_size | 客户家庭人口数 | 整数型（Integer） |
| 10 | acquisition_channel | 客户获客渠道（如自然流量、推荐引流） | 字符串型（String） |
| 11 | customer_segment | 基于客户行为划分的客户类别 | 字符串型（String） |
| 12 | savings_account | 客户是否拥有储蓄账户（是/否） | 布尔型（Boolean） |
| 13 | credit_card | 客户是否拥有信用卡（是/否） | 布尔型（Boolean） |
| 14 | personal_loan | 客户是否拥有个人贷款（是/否） | 布尔型（Boolean） |
| 15 | investment_account | 客户是否拥有投资账户（是/否） | 布尔型（Boolean） |
| 16 | insurance_product | 客户是否拥有保险产品（是/否） | 布尔型（Boolean） |
| 17 | active_products | 客户持有的活跃金融产品数量 | 整数型（Integer） |
| 18 | app_logins_frequency | 客户每月登录应用的次数 | 整数型（Integer） |
| 19 | feature_usage_diversity | 客户在应用中使用的独特功能数量 | 整数型（Integer） |
| 20 | bill_payment_user | 客户是否使用账单支付功能（是/否） | 布尔型（Boolean） |
| 21 | auto_savings_enabled | 客户是否启用自动储蓄功能（是/否） | 布尔型（Boolean） |
| 22 | credit_utilization_ratio | 已用信用额度与总信用额度的比值 | 浮点型（Float） |
| 23 | international_transactions | 客户发起的跨境交易笔数 | 整数型（Integer） |
| 24 | failed_transactions | 客户发起的失败交易笔数 | 整数型（Integer） |
| 25 | tx_count | 客户发起的交易总笔数 | 整数型（Integer） |
| 26 | avg_tx_value | 客户单笔交易的平均金额 | 浮点型（Float） |
| 27 | total_tx_volume | 客户所有交易的总金额 | 浮点型（Float） |
| 28 | first_tx | 客户首笔交易的日期 | 日期型（Date） |
| 29 | last_tx | 客户最近一笔交易的日期 | 日期型（Date） |
| 30 | base_satisfaction | 客户基础满意度评分 | 浮点型（Float） |
| 31 | tx_satisfaction | 基于交易历史的满意度评分 | 浮点型（Float） |
| 32 | product_satisfaction | 基于产品使用情况的满意度评分 | 浮点型（Float） |
| 33 | satisfaction_score | 客户整体满意度评分 | 整数型（Integer） |
| 34 | nps_score | 客户净推荐值（Net Promoter Score） | 整数型（Integer） |
| 35 | last_survey_date | 客户最后一次完成问卷的日期 | 日期型（Date） |
| 36 | support_tickets_count | 客户提交的客服工单总数 | 整数型（Integer） |
| 37 | resolved_tickets_ratio | 已解决工单占总工单的比例 | 浮点型（Float） |
| 38 | app_store_rating | 客户在应用商店给出的应用评分 | 浮点型（Float） |
| 39 | feedback_sentiment | 客户反馈的情感分析结果 | 字符串型（String） |
| 40 | feature_requests | 客户提出的功能需求 | 字符串型（String） |
| 41 | complaint_topics | 客户投诉的核心主题 | 字符串型（String） |
| 42 | clv_segment | 客户终身价值（Customer Lifetime Value）层级 | 字符串型（String） |
| 43 | monthly_transaction_count | 客户平均每月交易笔数 | 浮点型（Float） |
| 44 | average_transaction_value | 客户单笔交易的平均金额 | 浮点型（Float） |
| 45 | total_transaction_volume | 客户所有交易的总金额 | 浮点型（Float） |
| 46 | transaction_frequency | 客户每日平均交易笔数 | 浮点型（Float） |
| 47 | last_transaction_date | 客户最近一笔交易的日期 | 日期型（Date） |
| 48 | preferred_transaction_type | 客户最频繁使用的交易类型 | 字符串型（String） |
| 49 | first_transaction_date | 客户首笔交易的日期 | 日期型（Date） |
| 50 | weekend_transaction_ratio | 周末交易占总交易的比例 | 浮点型（Float） |
| 51 | avg_daily_transactions | 客户每日平均交易笔数 | 浮点型（Float） |
| 52 | customer_tenure | 客户作为平台用户的时长（单位：月） | 浮点型（Float） |
| 53 | churn_probability | 客户流失概率的预测值 | 浮点型（Float） |
| 54 | customer_lifetime_value | 客户为企业带来的预估总价值 | 浮点型（Float） |
| 55 | date | 单笔交易的日期 | 日期型（Date） |
| 56 | amount | 单笔交易的金额 | 浮点型（Float） |
| 57 | type | 单笔交易的类型 | 字符串型（String） |

## 数据预处理与伦理考量

本数据集已完成匿名化处理以保护客户隐私，分类变量已完成编码，且生成了多项衍生特征以简化分析流程。恳请研究人员负责任地使用本数据集，并严格遵守相关数据保护法规。

## 引用规范

若您在研究中使用本数据集，请按以下格式引用：

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

## 许可证

本数据集采用开放数据 Commons 署名许可证（ODC-By，Open Data Commons Attribution License）进行授权。

## 联系方式

若对本数据集有任何疑问或建议，请联系：[e-mail]