Machine Learning: higher accuracy at lower cost
Machine Learning (ML) is the most efficient way for a business to predict which of its transactions are likely to result in chargebacks. This efficiency means substantially lower costs and higher accuracy than any other approach to fraud prediction and detection.
However to get these results Machine Learning requires a great deal of data. Online businesses generate a lot of data by the nature of their operations. This makes them very well suited to this approach and, generally speaking, the more data a merchant has the more accurate the result.
Online businesses also have experience of chargebacks in the past. This provides a very useful training set to train the algorithms and models. Most, if not all online businesses should be able to see an extremely positive impact from a machine learning strategy when it comes to fraud.
Sending data to an external party is not always an easy decision to make especially when by its nature, a great deal of that data is sensitive. There is a degree of trust involved. In order to earn that trust a vendor should be held to certain standards:
- To treat the data with the utmost security
- To use the data with the utmost efficiency
- To provide results with the utmost accuracy
- To explain how those results were reached
- To enable those results to be improved by expert input
This paper explores the real-world experience of taking an ML approach, how it works, how the results are optimised and how a a fraud team interacts with an ML fraud detection system