I was just reading an article from Forrester research about “Artificial Intelligence Is Transforming Fraud Management”. Interesting read until about half way through where the authors start talking about supervised and unsupervised learning. That’s when they lost a lot of credibility:
Supervised learning makes decisions directly. Several years ago, Bayesian models, neural networks, decision trees, random forests, and support vector machines were popular fraud management algorithms. (see endnote 8) But they can only handle moderate amounts of training data; fraud pros need more complex models to handle billions of training data points. Supervised learning algorithms are good for predicting whether a transaction is fraudulent or not."
Aside from the ambiguity of what it means for an algorithm to make ‘direct’ decisions, SML can only take limited amounts of training data? Have you seen our malware deep learners? In turn, if SML is good at predicting fraudulent transaction, what’s the problem with training data?
What do they say about unsupervised approaches?
Unsupervised learning discovers patterns. Fraud management pros employ unsupervised learning to discover anomalies across raw data sets and use self-organizing maps and hierarchical and multimodal clustering algorithms to detect swindlers. (see endnote 10) The downside of unsupervised learning is that it is usually not explainable. To overcome this, fraud pros often use locally interpretable, model-agnostic explanations to process results; to improve accuracy, they can also train supervised learning with labels discovered by unsupervised learning. Unsupervised learning models are good at visualizing patterns for human investigators.
And here it comes: “The downside of UML is that it is usually not explainable”. SML has that problem not UML. Please get the fundamentals right. Reading something like this makes me question pretty much the entire article on its accuracy. And as a nuance: “UML is not itself good at visualizing patterns. Some of the algorithms lend themselves to visualize the output. But there is more to turning a clustering algo into a good visual. I mention t-sne in one of my older blog posts. That algorithm actually follows an underlying visualization paradigm (projection of multiple dimensions into two or three dimensions).
Reading on in the article, it says:
As this use case requires exceptional performance and accuracy, supervised learning dominates.
I thought SML doesn’t scale? Turns out, it actually does quite well, not least because you can run a learner offline.