Context
In the gaming industry, detecting bonus abuse fraud is crucial to maintaining the integrity of incentive programs and minimizing financial losses. The existing manual processes for identifying fraudulent activities were time-consuming and prone to errors. To address this, a machine learning model was developed, achieving over 95% accuracy in detecting monthly bonus abuse fraud cases. This deployment automated the fraud detection process, significantly enhancing operational efficiency and empowering stakeholders with data-driven insights.
Requirements
High Accuracy in Fraud Detection:
Develop a machine learning model that achieves high accuracy in identifying fraudulent activities.
Ensure the model can handle monthly detection of bonus abuse fraud cases.
Automation of Manual Processes:
Automate the previously manual processes to enhance operational efficiency and reduce the potential for errors.
Model Explainability:
Implement advanced techniques to explain the model's predictions.
Empower stakeholders to understand and trust the model's outputs.
Feature Enhancement:
Identify and incorporate new features to improve the model's predictive capabilities.
Develop a business case for the inclusion of these new features.
Threshold Optimization:
Develop a customized mathematical formula to determine ideal thresholds for flagging fraudulent customers.
Account for monetary impact, potential losses, and business-specific metrics in the threshold determination.
Data-Driven Insights:
Provide insights to guide business decisions regarding fraud detection strategies.
Conduct comprehensive analyses to evaluate the impact of existing and additional variables on fraud detection accuracy and business outcomes.
Approach
Requirement Analysis:
Engaging with stakeholders to understand the specific challenges and needs in detecting bonus abuse fraud.
Identifying key features and functionalities required for the machine learning model.
Data Collection and Preprocessing:
Collecting relevant data on bonus transactions and customer behaviors.
Preprocessing the data to ensure quality and suitability for model training.
Model Development:
Developing a machine learning model to detect fraudulent activities with high accuracy.
Training and testing the model to achieve over 95% accuracy in fraud detection.
Model Explainability:
Implementing advanced techniques, such as SHAP (SHapley Additive exPlanations), to explain the model's predictions.
Providing stakeholders with clear insights into why certain customers were flagged as fraudulent.
Feature Enhancement:
Conducting a business case analysis to identify and incorporate two new features that enhance the model's predictive capabilities.
Demonstrating the impact of these features on improving fraud detection accuracy.
Threshold Optimization:
Developing a customized mathematical formula to determine the ideal thresholds for flagging customers as fraudulent or legitimate.
Integrating business-specific metrics, such as financial implications and risk tolerance, into the threshold determination process.
Data-Driven Insights:
Conducting comprehensive analyses to evaluate the impact of existing and additional variables on fraud detection accuracy and business outcomes.
Providing data-driven insights to guide business decisions and optimize fraud detection strategies.
Technologies Used
Machine Learning Models:
Languages: Python
Libraries/Frameworks: Scikit-learn, TensorFlow
Purpose: Utilized various machine learning algorithms for developing the fraud detection model. Achieved high accuracy through extensive training and testing.
SHAP (SHapley Additive exPlanations):
Languages: Python
Libraries: SHAP
Purpose: Implemented for advanced model explainability to provide clear insights into model predictions. Empowered stakeholders to understand and trust the model's outputs.
Customized Mathematical Formulas:
Languages: Python
Libraries: NumPy, pandas
Purpose: Developed to determine ideal thresholds for flagging fraudulent customers. Integrated business-specific metrics to account for monetary impact and potential losses.
Data Analytics Tools:
Languages: Python, SQL
Libraries/Frameworks: Pandas, NumPy, Matplotlib, Seaborn
Purpose: Utilized for conducting comprehensive analyses to evaluate the impact of variables on fraud detection accuracy and business outcomes. Provided data-driven insights to guide business decisions.
Data Preprocessing Techniques:
Languages: Python
Libraries: Pandas, NumPy
Purpose: Employed to ensure data quality and suitability for model training. Included data cleaning, normalization, and feature engineering.