Unit8 Talks #7 – Fraud detection – A guide to building a financial transaction anomaly detector
Unit8 Talks #7 - On technology - Fraud detection - A guide to building a financial transaction anomaly detector
Many companies currently still rely on hard-coded, inflexible ways of detecting anomalies in their financial transactions, hence leading to lots of false positives or risking fraudulent transactions to be executed. How can anomalies be found in financial transactions when we don't know what indicators we are looking for in advance?
One possible solution to this is building a machine learning model that attempts to automate the approach. The model measures how easy individual data points can be separated from the rest of the data (i.e. using an isolation forest).
In this webinar we will explore how to build such a Machine Learning model, how to use its outputs, and how to create a complementary explanation model to interpret and validate these outputs..
Who should attend?
- Technical Executives, Data Scientists, Controlling/Risk teams
- Technical level L200
Why should you attend?
- A guide to machine learning driven fraud detection applicable across different industries - which can be reused to your needs
Get in touch info@unit8.co
More about Unit8 - https://unit8.co/
Unit8 on LinkedIn - https://www.linkedin.com/company/unit8.co
Unit8 on Twitter - https://twitter.com/unit8co
Unit8 on Instagram - https://www.instagram.com/unit8.co/
Unit8 on Medium - https://medium.com/unit8-machine-learning-publication
Unit8 Talks #7 – On technology – Fraud detection – A guide to building a financial transaction anomaly detector
Many companies currently still rely on hard-coded, inflexible ways of detecting anomalies in their financial transactions, hence leading to lots of false positives or risking fraudulent transactions to be executed. How can anomalies be found in financial transactions when we don’t know what indicators we are looking for in advance?
One possible solution to this is building a machine learning model that attempts to automate the approach. The model measures how easy individual data points can be separated from the rest of the data (i.e. using an isolation forest).
In this webinar we will explore how to build such a Machine Learning model, how to use its outputs, and how to create a complementary explanation model to interpret and validate these outputs..
Who should attend?
– Technical Executives, Data Scientists, Controlling/Risk teams
– Technical level L200
Why should you attend?
– A guide to machine learning driven fraud detection applicable across different industries – which can be reused to your needs
Get in touch info@unit8.co
More about Unit8 – https://unit8.co/
Unit8 on LinkedIn – https://www.linkedin.com/company/unit8.co
Unit8 on Twitter – https://twitter.com/unit8co
Unit8 on Instagram – https://www.instagram.com/unit8.co/
Unit8 on Medium – https://medium.com/unit8-machine-learning-publication
