Ethical Credit Card Fraud Detection
Budget: $30 – $250 USD
I need a complete fraud-detection solution for credit card transactions that marries conventional Machine Learning with more advanced Deep Learning, always keeping fairness and regulatory ethics front and centre, aims to design, implement, and deploy a robust fraud detection system using the publicly available dataset from Kaggle. I will provide a history of anonymised transactions; your job is to build, compare and document both approaches so I can demonstrate superior accuracy without unintentionally penalising any customer segment.
The study addresses the critical challenge of highly imbalanced financial data by developing accurate, scalable, and ethically responsible predictive models capable of identifying fraudulent transactions in near real-time.
The project will begin with comprehensive data preprocessing, including data cleaning, feature scaling, and transformation of anonymised variables. Advanced techniques such as class imbalance handling (e.g., SMOTE and undersampling) will be applied to improve model sensitivity to rare fraud cases. Exploratory data analysis will be conducted to understand transaction patterns and feature importance, forming the foundation for model selection and optimisation.
A range of machine learning and deep learning models will be implemented and compared, to decide on using the best appropriate model, including Logistic Regression, Random Forest, Gradient Boosting (e.g., XGBoost), and Artificial Neural Networks using frameworks such as TensorFlow or PyTorch. Model performance will be evaluated using appropriate metrics for imbalanced data, including precision, recall, F1-score, ROC-AUC, and confusion matrices. Hyperparameter tuning and cross-validation will be employed to achieve optimal performance, with a strong emphasis on minimising false negatives (missed fraud cases).
Beyond predictive performance, the project integrates principles of Fair and Ethical AI by assessing potential bias, transparency, and explainability using techniques such as SHAP and LIME. This ensures that the model aligns with responsible AI practices within financial services.
To demonstrate practical applicability, the final model will be deployed as a RESTful API using frameworks such as FastAPI or Flask, enabling real-time fraud prediction from incoming transaction data. The deployment pipeline will include model serialisation, API development, and testing, with optional cloud deployment (e.g., AWS or Heroku) for scalability. The project will be fully implemented in Python using Jupyter notebook with well-structured, reproducible code and version control.
The code should be written in Python using jupyter notebook environment, running perfectly, showing all the necessary outputs and the explanations and comments in the markdown cells.
If you’re comfortable moving between scikit-learn and TensorFlow/PyTorch and have experience testing models for bias, I’d like to hear how you would tackle this and roughly how long you’d need to complete it.
The study addresses the critical challenge of highly imbalanced financial data by developing accurate, scalable, and ethically responsible predictive models capable of identifying fraudulent transactions in near real-time.
The project will begin with comprehensive data preprocessing, including data cleaning, feature scaling, and transformation of anonymised variables. Advanced techniques such as class imbalance handling (e.g., SMOTE and undersampling) will be applied to improve model sensitivity to rare fraud cases. Exploratory data analysis will be conducted to understand transaction patterns and feature importance, forming the foundation for model selection and optimisation.
A range of machine learning and deep learning models will be implemented and compared, to decide on using the best appropriate model, including Logistic Regression, Random Forest, Gradient Boosting (e.g., XGBoost), and Artificial Neural Networks using frameworks such as TensorFlow or PyTorch. Model performance will be evaluated using appropriate metrics for imbalanced data, including precision, recall, F1-score, ROC-AUC, and confusion matrices. Hyperparameter tuning and cross-validation will be employed to achieve optimal performance, with a strong emphasis on minimising false negatives (missed fraud cases).
Beyond predictive performance, the project integrates principles of Fair and Ethical AI by assessing potential bias, transparency, and explainability using techniques such as SHAP and LIME. This ensures that the model aligns with responsible AI practices within financial services.
To demonstrate practical applicability, the final model will be deployed as a RESTful API using frameworks such as FastAPI or Flask, enabling real-time fraud prediction from incoming transaction data. The deployment pipeline will include model serialisation, API development, and testing, with optional cloud deployment (e.g., AWS or Heroku) for scalability. The project will be fully implemented in Python using Jupyter notebook with well-structured, reproducible code and version control.
The code should be written in Python using jupyter notebook environment, running perfectly, showing all the necessary outputs and the explanations and comments in the markdown cells.
If you’re comfortable moving between scikit-learn and TensorFlow/PyTorch and have experience testing models for bias, I’d like to hear how you would tackle this and roughly how long you’d need to complete it.