The Challenge:
To create a model for banks and financial institutions that could improve identity fraud detection in credit card users while maintaining variable explainability.
The Solution:
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 Outcome:
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.
Results
% Improvement in Fraud Detection Ability
