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• Client : Bank
• Category : Ai & ML
• Date : 08/01/2020

Challenge
Mitigating fraud is a top priority for banks. fraudsters are becoming more creative and technologically savvy—they’re also using advanced technologies like machine learning—and new schemes to defraud banks are evolving rapidly.

Approach
Analytic approach employs a “champion/ challenger” methodology. With this approach, deep learning systems compare models in real time to determine which one is most effective. Each challenger processes data in real time, learning as it goes which traits are more likely to indicate fraud. If a process dips below a certain threshold, the model is fed more data, such as the geo-location of customers or recent ATM transactions. 

Results
When a challenger outperforms other challengers, it transforms into a champion, giving the other models a roadmap to successful fraud detection