Case Studies: Finance Technology

Finance
Technology

The Challenge:

To create a model 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.

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