News & Content: Member Blog

Using CD with Machine Learning Models to Tackle Fraud

Monday, April 16, 2018   (0 Comments)
Posted by: Gary Hotze
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Hany Elemary, Lead Consultant, Developer

Sarah LeBlanc, Developer - Software

ThoughtWorks

 

Credit card fraudsters are always changing their behavior, developing new tactics. For banks, the damage isn’t just financial; their reputations are also on the line. So how do banks stay ahead of the crooks? For many, detection algorithms are essential.


Given enough data, a supervised machine learning model can learn to detect fraud in new credit card applications. This model will give each application a score — typically between 0 and 1 — to indicate the likelihood that it’s fraudulent. The banks can then set a threshold for which they regard an application as fraudulent or not — typically that threshold will enable the bank to keep false positives and false negatives at a level it finds acceptable. 

False positives are the genuine applications that have been mistaken as fraud; false negatives are the fraudulent applications that are missed. Each false negative case has a direct financial impact on a bank as it corresponds to a financial loss. False positives are a little trickier to map to a financial figure since they represent the opportunity cost of losing a customer.

Given the impact fraudulent activity can have, it is vital to have an effective detection system. With fraudsters changing behavior constantly, a detection system is only effective if it can match this rate of change. This is the problem that we were helping with at one of our clients.

A lengthy process

 
For the past few years, we’ve worked with a global financial institution, on a variety of projects including one to help improve their credit card application fraud detection...

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