Blackbox Machine Learning

What Is Blackbox Machine Learning?

Blackbox machine learning refers to models that produce results without revealing their decision-making processes. In these models, the internal mechanisms and weighted factors influencing the output remain hidden or inaccessible.

In other words, only the algorithm itself is aware of exactly how the decisions were made. No human — not even the programmers and admins of the machine or algorithm — can directly interpret or trace the exact reasoning behind the output.

Blackbox models are valuable in fraud prevention because they can detect complex, subtle patterns in large datasets that simpler, more transparent models might miss.

What Is Blackbox Machine Learning in Fraud Prevention?

In fraud prevention, blackbox models are often used because they can identify complex, non-linear patterns that traditional models might miss. The algorithm will assign a fraud probability score to transactions without showing how it reached this conclusion. 

A blackbox algorithm can provide benefits such as:

1. Higher detection accuracy: Blackbox models can analyze vast amounts of transactional data and behavioral patterns, spotting anomalies indicative of fraud that may not be visible in simpler models.
2. Adaptability: These models can learn from new data continuously, adapting to emerging fraud tactics and new types of fraud, often more effectively than manually designed rules.
3. Reduced false positives: By identifying nuanced patterns, blackbox models can more accurately distinguish between legitimate and fraudulent transactions, reducing false positives that inconvenience customers and add investigation costs.

However, the lack of transparency also poses challenges:

• Lack of interpretability: Because it’s hard to see “inside” the model, decision-makers may struggle to understand why a model flagged a transaction as fraudulent. This lack of transparency is a particular concern in highly regulated industries, where justifications are often required for adverse actions.
• Regulatory compliance: Some jurisdictions require explainable AI for decisions that impact individuals, especially in financial services. Blackbox models can make it hard to comply with these regulations.
• Bias and fairness: Without insights into how the model makes decisions, there’s a risk of inadvertently encoding biases, leading to unfair or discriminatory outcomes.

How Does Blackbox Machine Learning Work?

As a first step, the machine learning algorithm needs to be trained in a large data set. After this training is complete, the model is ready to process real-world data and return a result. In fraud prevention, this result will be a risk probability score.

All the while, as the model receives more and more information, it scales its algorithm, methods and knowledge with this new data.In SEON, blackbox machine learning is used to calculate a fraud probability score that is separate from the regular fraud score. You can learn more about this in detail in our documentation.

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.

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.

Fight Fraud with Machine Learning

In fraud detection, relying solely on a blackbox machine learning platform means you won’t have a clear understanding of how it operates internally. This lack of transparency can make it more difficult to assess whether it effectively meets your specific needs. Additionally, you won’t have the ability to adjust parameters or modify the decision trees that inform its judgments.

In contrast, opting for a whitebox solution can be highly beneficial. Whitebox models provide transparency and control, allowing you to see how decisions are made and to adjust the model according to your specific requirements. This enables you to respond quickly to emerging fraud patterns and ensures that your approach remains fair and compliant with regulations.

In many instances, a combination of both types of models can offer the best balance between accuracy and insight.

Luckily, in SEON you can find the best of both worlds. Our blackbox model is actually explainable AI, which means that upon request, you can identify and understand key correlations that determine the final probability score.