Get the most out of Machine Learning
Overview
Machine learning algorithms identify the patterns and typical behaviors behind fraudulent transactions and help you catch them earlier with improved accuracy.
By processing hundreds (sometimes even thousands) of approved and declined transactions, our machine learning solution can discover connections you didn't even know were there.
Our machine learning models will create new human-readable rules (whitebox) or assign a probability score to each transaction (blackbox).
Both are available to you in SEON, and you can choose how you want to use them.
SEON's Machine Learning Tools
Our whitebox and blackbox solutions are a great addition to your team's fraud-fighting toolkit. Choose the one that best fits your needs, or use them in combination for maximum efficiency. The choice is yours.
Both can be automated and customized to your liking, so you can find a setup that's just right for you and your business.
| Whitebox | Blackbox | |
| Automatic retraining several times a day | Yes | Yes |
| Uncovers complex fraud patterns | Yes | Yes |
| Human-readable rules | Yes | No |
| Transparent decision-making | Yes | No |
| Automatic rule creation | Yes | No |
| Can be used in rules | No | Yes |
| Can affect fraud score | No | Yes |
| Can change transaction state | Yes | Yes |
| Fraud probability scoring | No | Yes |
| Keeps you in control | Yes | Yes |
| Fully automatable as needed | Yes | Yes |
| Available from Day 1 | No* | Yes |
* Whitebox machine learning requires account-specific training data to begin recommending rules: at least 1000 transactions with 100 in the DECLINE and 100 in the APPROVE state.
Whitebox Machine Learning
Our whitebox machine learning algorithms analyze your transaction history to discover fraud patterns and create rules suggestions. You can then implement its fully explainable rules to block certain user actions, such as suspicious logins, identity theft, or fraudulent transactions.
Blackbox Machine Learning
Similar to the whitebox model, blackbox trains itself on historical data to help you find fraud patterns that stay hidden from the human eye. The difference lies in transparency — while you can see how the algorithm reached a conclusion in a whitebox model, the way blackbox comes to conclusions is more nuanced. The model can make surprising connections and discover behaviors that would be hard to translate to a human reader.
Feedback Loops
At its heart, machine learning algorithms learn from good and bad examples. By default, our machine learning solutions learn from transaction states, but you can finetune their performance with our Label API. Create positive and negative transaction labels to give more accurate feedback and rule out false positives.