/Credit-Card-Fraud-Detection

Credit-Card-Fraud-Detection

Project Definition

Objective:

  • Predict the probability of loan default based on an applicant's profile and financial data.
  • Provide actionable insights to financial institutions for making informed lending decisions.

Data Collection

Datasets:

Features:

  • Demographic Information: Age, gender, marital status, education, employment status.
  • Financial History: Number of previous loans, repayment history, current debts.
  • Credit Bureau Data: Credit scores, number of credit inquiries, public records.
  • Behavioral Data: Transaction history, account balances, spending patterns.

Data Preprocessing

Cleaning:

  • Handle missing values using methods like imputation or deletion.
  • Detect and address outliers using statistical techniques or domain knowledge.
  • Resolve data inconsistencies.

Normalization/Standardization:

  • Scale numerical features using standardization (Z-score) or normalization (Min-Max scaling).

Encoding:

  • Convert categorical variables using one-hot encoding for nominal features.
  • Use label encoding or target encoding for ordinal features.

Feature Engineering:

  • Create features like debt-to-income ratio, credit utilization rate, loan-to-value ratio.
  • Generate interaction features if they add value to the model.

Exploratory Data Analysis (EDA)

Visualization:

  • Use histograms, box plots, and scatter plots to understand feature distributions.
  • Create correlation matrices and heatmaps to identify relationships between features and the target variable.

Insights:

  • Identify key predictors of default.
  • Detect multicollinearity among features.

Model Development

Model Selection:

  • Experiment with baseline models like Logistic Regression and Decision Trees.
  • Explore advanced models like Random Forests, Gradient Boosting Machines, XGBoost, and Neural Networks.

Training:

  • Split the dataset into training and validation sets (e.g., 80/20 split).

Hyperparameter Tuning:

  • Use Grid Search or Random Search with cross-validation to find optimal hyperparameters.

Cross-Validation:

  • Apply k-fold cross-validation (e.g., k=5) to assess model robustness.

Model Evaluation

Metrics:

  • Evaluate models using AUC-ROC, Precision-Recall curves, F1 Score, and Confusion Matrix.

Interpretability:

  • Utilize SHAP or LIME for model interpretation.
  • Ensure fairness by checking for biases across different demographic groups.

Model Deployment

API Development:

  • Develop a RESTful API using Flask or FastAPI to serve the model.

Integration:

  • Integrate the API with front-end applications or existing financial systems for real-time scoring.

Documentation and Reporting

Documentation:

  • Document all steps including data sources, preprocessing methods, model development, and evaluation metrics.

Reporting:

  • Create visual reports using tools like Jupyter Notebook or dashboards using Tableau/PowerBI.
  • Present findings and model performance to stakeholders through detailed reports and presentations.

Future Enhancements

Continuous Learning:

  • Develop a pipeline for periodic model retraining with new data.

Advanced Techniques:

  • Explore ensemble methods to combine multiple models.
  • Investigate transfer learning for leveraging pre-trained models.
  • Incorporate alternative data sources, such as social media behavior or mobile phone usage patterns, if permissible.

Implementation Steps

Data Collection:

  • Download and inspect the Kaggle dataset.
  • Perform initial data cleaning (e.g., handle missing values).

Data Preprocessing and EDA:

  • Clean and preprocess the data thoroughly.
  • Conduct EDA to gain insights into data distribution and relationships.

Model Development:

  • Develop and train multiple models, starting with simple ones and progressing to complex ones.
  • Perform hyperparameter tuning to improve model performance.

Model Evaluation:

  • Evaluate models using appropriate metrics.
  • Use interpretability techniques to understand model predictions.

Model Deployment:

  • Develop an API to deploy the model.
  • Monitor the model in production to ensure it remains effective.

Documentation and Reporting:

  • Thoroughly document the entire project.
  • Create final reports and presentations for stakeholders.

Tools and Technologies

  • Programming Language: Python
  • Libraries: Pandas, NumPy, Scikit-learn, XGBoost, LightGBM, SHAP, LIME, Matplotlib, Seaborn
  • API Framework: Flask or FastAPI
  • Deployment: Docker, AWS/GCP/Azure for hosting

Expected Outcomes

  • A deployable credit risk scoring model.
  • Comprehensive analysis demonstrating the ability to tackle real-world fintech problems.
  • Detailed documentation and insightful reports for stakeholders or potential employers.