While machine learning has enhanced fraud detection systems, current approaches have downsides. Do any of these sound familiar?
- It’s virtually impossible to find unknown, novel fraud attacks
- A lot of manual effort is required to re-tune the models and perform feature engineering
- There’s always a significant gap between modeling data and production, which causes poor production performance
We’ve developed a guide that talks about how to address the weaknesses in today’s machine learning systems using an unsupervised machine learning approach.
Read this whitepaper to learn:
- The new shift in banking fraud attacks
- The current limitations of machine learning techniques
- How an unsupervised machine learning approach can address these shortcomings