We Value Your Privacy

We use technology such as cookies on our website, and through our partners, to personalize content and ads, provide social media features, and analyse our traffic. To find out more, read our privacy policy and Cookie Policy. Please also see our Terms and Conditions of Use. By accepting these terms you agree to your information being processed by Inbox Insight, its Partners or future partners, that you are over 18, and may receive relevant communications through this website, phone, email and digital marketing. For more information on how we process your data, or to opt out, please read our privacy policy. Our policies and partners are subject to change so please check back regularly to stay up to date with our terms of use and processing.

Accept Terms Settings
x

Please Sign-In to Access this Report

To access other reports on the platform please sign in with your username and password, or register for a free account to get unlimited access and insight customized for you.

Report Dataiku Fraud and Anomaly Detection in Banking

Fraud and Anomaly Detection in Banking

A Step-by-Step Guide to Incorporating Machine Learning Into Models

Establishing a firm understanding of what data science, machine learning, and AI technologies can bring to fraud detection and other anomaly detection use cases in banking today is a first step to making headway in these areas. Combining this knowledge with more overarching AI project best practices, including operationalization and data democratization, will ensure that banks stay ahead of the curve.

Report Snap Shot

  • A broader overview of the role of anomaly detection in banking (beyond fraud) and ways to integrate the process into existing workflows.
  • Code samples for a simple machine learning-based fraud detection model, along with ways to customize and improve it.
  • Use case examples from innovative banking organizations.

Related Content