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Credit Card Fraud

Enhance existing credit card fraud practices by leveraging machine learning alongside business rules.

The goal of this adapt and apply solution is to enable investigator teams to understand how Dataiku can be used to leverage established insights and rules for credit card fraud detection modeling within a robust and full-featured data science platform, while easily incorporating new machine learning approaches and ensuring real-time alerts management. More details on the specifics of the solution can be found on the knowledge base.

Business Overview

The total value of fraudulent transactions using cards issued within SEPA and acquired worldwide amounted to €1.80 billion in 2018¹, an increase of 13% year on year. This trend calls for continuous fraud monitoring and vigilance, both to limit banks P&L impact but also to enhance customer trust

Fraud detection rules are complex and well-established though often are based on business rules only. Enhancing set-ups with machine learning integration opens opportunities for increased efficiency in better detecting fraudulent behaviors and maximizing focus.

Providing a unified space for teams to manage business rules alongside machine learning approaches, and allowing for sandbox experimentation and enterprise-grade productionization, ensures the gains from machine learning are realized, without losing established success through existing approaches.

Highlights

  • Unifying business and ML rules ensures that existing approaches are leveraged to their full potential while adding incremental value via ML.
  • Business-friendly explainable AI allows for rapid and intelligent review of machine learning models, ensuring all teams are confident and contribute expertise effectively.
  • Integrated model re-evaluation and streamlined redeployment allow for models to be quickly retrained while retaining full control over production decisions.
  • Integrated alerts monitoring and powerful case management connectivity allows for effective alert assessment and assignment