Applying Machine Learning to Financial Payments

This presentation discusses how Machine Learning can be applied to Payments to respond rapidly to known and emerging patterns of fraud, and to detect patterns of fraud that may not otherwise be identified.  It will cover techniques that have been used and are emerging in fraud detection including rule-based techniques, supervised learning and unsupervised learning. The presentation includes a demonstration using TensorFlow to detect fraud. This will illustrate the process of preparing training and test data, learning and then applying the model to generate potential fraud events.  We’ll also explore potential issues including data bias and mitigating approaches.  

Objective of the talk:

Develop an understanding of how machine learning can be implemented to detect fraud in payments, gain knowledge on how Machine Learning can detect patterns that indicate fraudulent transactions, learn how data is prepared to train an artificial Neural Network  

Required audience experience:

Basic familiarity with machine learning is helpful, but not required


Tamsin Crossland

Senior Architect @iconsolutions

Tamsin is a Senior Architect who is leading research on artificial intelligence for a FinTech payments specialist. Having started working on Expert Systems, Tamsin has 30 years experience of IT, primarily in the finance sector.

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