What Is Blackbox Machine Learning?
In general terms, blackbox machine learning refers to machine learning models that give you a result or reach a decision without explaining or showing how they did so. The internal processes used and the various weighted factors remain unknown.
In other words, there is a lack of transparency in this technology. A blackbox model means no human – not even the programmers and admins of the machine or algorithm – knows or understands how the output was reached.
Only the algorithm itself is aware of exactly how the decisions were made.
What Is Blackbox Machine Learning in Fraud Prevention?
Blackbox machine learning in fraud prevention gives you a fraud score without telling or showing you how that score was reached. The user only finds out the result of these complex calculations.
So, data goes in, and a result – a risk score – comes out, but we don’t know what happens in between. Therefore, we cannot adjust or tweak these internal processes either.
In a sense, this means we are at the mercy of the algorithm, since we are not aware of what informs the resulting risk score.
Discover how SEON’s whitebox machine learning system offers powerful and transparent rule suggestions so you can take your fraud prevention to the next level.
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How Does Blackbox Machine Learning Work?
- In its simplest form, machine learning involves feeding huge amounts of example data into an algorithm and letting it self-learn.
- Following this “training”, the machine learning model is ready to be given real-world data, process them and give us a result – in fraud detection, a risk score.
- All the while, as it receives more and more information, it scales its algorithm, methods and knowledge with this new data.
But how exactly is it doing so? What role does each bit of information play in the process that gave us this result?
A blackbox system is not able to tell us. In other words, just like we can’t see inside a box painted black, neither do we know how each blackbox machine learning model works.
An alternative way to consider this is to look at the features and breakdown of how blackbox machine learning works at SEON, in our open documentation, as an example.
Why Is Blackbox Machine Learning Important?
Despite its inherent lack of transparency, blackbox machine learning is very popular – and can be useful in certain settings. In fact, the vast majority of machine learning models in use in 2021 and beyond are blackbox.
Blackbox machine learning makes use of technology such as:
- big data
- string similarity
- deep learning
- neural networks
With blackbox machine learning, we can answer the question “What is the risk score of X?” but we are never able to answer the question “Why does the system think this is an accurate risk score for X?”
If we could answer this “why”, the system would automatically be classed as a whitebox machine learning system instead.
How Does Blackbox Machine Learning Help Fight Fraud?
As a fraud-fighting tool, blackbox machine learning can help us figure out complex connections and factors.
- It relies on complex classifications based on probability, but there is no transparency, which you get with a whitebox solution.
- It allows us to process more information than humanly possible, and do so in a fast way.
- It is better than whitebox at catching new, unique and sophisticated fraud attempts.
However, it also has shortcomings.
In fraud detection, using an exclusively blackbox-based ML platform means that you, as its user, are not fully aware of its inner machinations – so you cannot know whether it works for you and your needs. Neither will you be able to change the parameters and decision trees it uses in order to reach its decisions.
Depending on your requirements, it is considered advantageous to choose whitebox solutions where possible – or even a combination of the two.
Why Choose Blackbox Over Whitebox?
In fraud prevention and detection, blackbox machine learning delivers certain advantages:
- Speed: The system can process vast amounts of data and deliver faster results than humans – as well as faster results than a whitebox system.
- Big data utilization: Large datasets lend themselves well to blackbox systems.
- Works unsupervised: There’s no need for humans to tweak or approve the inner workings of the system and the way decisions are reached.
- Can identify new patterns: Blackbox models are more likely to predict and hone in on new and unique or sophisticated fraud attempts.
That said, the best answer is that there are instances where blackbox is better, and others where a whitebox approach is ideal.
Is there a benefit in having the algorithms and calculations be transparent (known) or are they acceptable being opaque, only giving you a score result without disclosing exactly how it has been reached?
The blackbox model means that you can see the input and you can see the output, but you don’t know what happens in-between. Simply put, blackbox is unexplainable machine learning (while whitebox is explainable machine learning).
When speed is of the essence and accuracy is secondary, a blackbox solution may be recommended. Whitebox solutions, on the other hand, opt for quality (and precision) over quantity.
Of course, all machine learning allows us to reduce time spent manually reviewing information – and each method has its use.
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