Context and Objective
I’ve created a feature table for the banking and financial sector, using SQL queries to collect and structure the data needed to train a machine learning model capable of predicting future customer behaviors. The project focuses on creating a denormalized table that gathers various financial and behavioral indicators, derived from customer transactions and owned accounts.
The main goal is to generate a feature table that can be used to train machine learning models capable of:
- Predicting customer behavior (purchase of new products, account closures, etc.)
- Reducing churn rate by identifying at-risk customers
- Improving financial risk management
- Personalizing offers based on customer habits
- Preventing fraud by detecting transaction anomalies
Implemented Solution
The project required analyzing and manipulating the banking database, composed of the following tables:
- Customer: personal information (e.g., age, customer ID)
- Account: details of owned accounts
- Account type: description of available account types
- Transaction type: transaction classification
- Transactions: record of customer financial operations
Key Steps:
- Table Integration: Tables were joined using JOIN operations to connect transactional data with customer demographic information
- Behavioral Indicators Calculation: Various aggregates were computed using SQL functions like SUM and COUNT to derive indicators such as:
- Total number of incoming and outgoing transactions
- Total transaction amount
- Number of owned accounts and breakdown by type
- Transaction indicators segmented by account type
- Denormalized Table Creation: The resulting dataset was designed to contain all necessary features, making it ready for machine learning model training
Results and Benefits
The feature table implementation has brought numerous advantages:
- Better predictive accuracy: Models trained with this table can predict customer actions with greater accuracy
- Churn rate reduction: By identifying at-risk customers, the marketing team can intervene with targeted offers
- Risk management optimization: Customer segmentation improves credit and risk prevention strategies
- Advanced offer personalization: Financial products can be recommended based on actual customer behaviors
- Security enhancement: Transaction anomalies can signal potential fraud
Conclusion and Impact
This project has created a solid foundation for predictive customer analysis in the banking and financial sector. I’m already evaluating the integration of new variables and metrics to further improve the dataset quality and support more advanced machine learning models.