Reframing techniques we can use to adapt our models to new contexts — without retraining it.
Dealing with fraud is getting trickier by the day since scammers are constantly updating their methods. Companies need machine learning models that can keep up with these changes without needing to be completely retrained or redesigned. That’s where versatile models come in handy, especially when we reframe them to adapt to new situations.
In this article, we’ll explore how reframing versatile models can help us stay ahead of fraudsters and check out some examples of how it works in practice. We’ll also discuss how this approach is related to the Context-Aware Security and Privacy by Design (CASP-DM) framework.
The ultimate goal of this article is to serve as a menu linking to various hands-on articles demonstrating different reframing techniques. As more hands-on articles become available, this article will be updated to include them, creating a comprehensive resource for adapting versatile models to ever-evolving fraud scenarios.
So, let’s dive in and learn how to make our models more adaptable and efficient in detecting fraud!
What’s the magic of reframing? It’s all about fine-tuning adaptable models to perform better in new situations, without starting from scratch.
Let’s dive into some examples of how reframing can help enhance fraud detection:
1.Decision threshold adjustment (Context-aware threshold):
Adjusting the decision threshold based on contextual information can help the model adapt to different scenarios. For example, you can increase the threshold for transactions in high-risk periods or industries, making the model more conservative in flagging potential fraud cases in those contexts.
Combining the model’s predictions with domain-specific rules can help refine the decision-making process. Rules can be derived from expert input, prior knowledge about common fraud patterns or heuristic functions that capture similarity between new fraud cases and the new transaction data. For instance, if transactions above a certain rule have a higher risk of being fraudulent, you can add a rule to flag such transactions, even if the model’s prediction is below the threshold.
3.Combining multiple models’ outputs:
If you have multiple models trained on different aspects of the fraud detection problem, you can aggregate their outputs to make a final decision without retraining any of the models. This could involve techniques like majority voting, weighted voting, or rank aggregation.
Reframing the interpretation of model outputs can help in adapting to different scenarios. For example, instead of directly using the predicted fraud probability, you can compute the percentile rank of the prediction within the historical distribution of fraud probabilities. This allows for a more relative comparison of transactions and can help in identifying potentially fraudulent transactions in changing scenarios.
Incorporating variables that indicate specific seasons, days of the week, or holidays can help the model capture variations in fraud patterns related to time. For example, you can add a binary feature that indicates whether a transaction took place during a known high-risk period, like the holiday shopping season.
Adding variables that represent specific niches or market segments can help the model adapt to different realities. For instance, you can introduce a categorical feature representing the industry or sector, as some industries might be more prone to fraud.
Creating interaction features by combining existing features can help capture complex relationships between variables. For example, you can create a new feature that represents the interaction between transaction amount and a high-risk period indicator, which could help the model identify transactions with higher fraud risk during certain times.
These examples just scratch the surface of how reframing can be used to improve fraud detection versatile models. By doing so, we ensure their adaptability and effectiveness in the face of ever-evolving threats and changing environments.
Connecting the Dots with CASP-DM
The Context-Aware Security and Privacy by Design (CASP-DM) framework is all about making sure we take the context into account when building models to detect and prevent security and privacy risks. Reframing versatile models for fraud detection fits right in with CASP-DM since it’s all about adapting the model to the specific situation we’re dealing with. This way, we can do a better job of detecting fraud in all kinds of different scenarios.