To create a model for banks and financial institutions that could improve identity fraud detection in credit card users while maintaining variable explainability.
Factored implemented an experimentation pipeline to detect the best model given a set of variables. A deep analysis of variables was established, which improved model accuracy.
The detection rate for cases of identity fraud improved by 7.5%, which saved our client time and money as they no longer had to spend on or carry out additional processes.
Tech Tools & Skills Applied:
We used a variety of skills and tech stack tools including TensorFlow, Python, LGBM, XGBoost, Hyperopt, Deep Learning, Fraud Detection, Forecasting, and Model Interpretability.
% Improvement in Fraud Detection Ability