Project Description


Why Today’s Machine Learning Fraud Detection In Financial Institutions Falls Short

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:

  1. The new shift in banking fraud attacks
  2. The current limitations of machine learning techniques
  3. How an unsupervised machine learning approach can address these shortcomings

Download the Whitepaper