The advances in ML-AI have opened the doors to a plethora of smart and personalized methods of fraud detection. What once was an area that required ample amounts of manual labor, is now one of the many that have experienced the progress of machine learning. Notwithstanding, it is imperative to note that this technology (just like any other) is not perfect and susceptible to problems, and with enterprises and services offering predictive modeling solutions to detect fraudulent transactions, let’s take a step back and consider the possible challenges that may arise.
If you aren’t familiar with the applications of ML in fraud detection space, here’s Part 1 for your reference.
Class Imbalance
The majority of transactions are probably not fraudulent, which is great, but this brings up the most common problem in the field of fraud detection: imbalanced data.
The most critical component of a machine learning predictive model is the data, and an unbalanced dataset which in this case is one made up primarily of non-fraudulent records, may prevent a prediction system from effectively learning about the fraudulent transactions. As normal transactions vastly outnumber frauds, it is the minority class that is of importance. The lack of these significant frauds frequently hinders the classifier's ability to train, leading to the bias towards the majority class because classic learning algorithms are trained to maximize the overall accuracy and such high score accuracy may mislead about the performance of the learning model. In such circumstances, obtaining more data would seem to be the obvious remedy; however, in reality, doing so is either expensive, time-consuming, or nearly impossible.
Most of the research works have used data level techniques to handle class imbalance. Data level methods modify the data by undersampling or oversampling to balance the class frequencies. However, undersampling can cause the loss of valuable information from the data and oversampling can cause overfitting of the learning model due to the addition of redundant samples in the dataset. Thus, by altering the dataset, the important information like hidden patterns may get lost.
Try penalized models!
Penalized learning algorithms imposes an additional cost on the model for making classification mistakes on the minority class during training. These penalties can bias the model to pay more attention to the minority class. Often the handling of class penalties or weights are specialized to the learning algorithm but it is also possible to have generic frameworks for penalized models. Using penalization is desirable if you are locked into a specific algorithm and are unable to resample or you’re getting poor results.
Innovations in Fraud
Adaptive and innovative fraudulent behavior is another hurdle to be crossed. The profiles of both fraud and normal behavior are always changing and evolving, necessitating continuous relearning of the new patterns. Additionally, it's possible that the system will pick up on previous fraudulent transactions that were misinterpreted as legitimate. Because new frauds can be made to look like everyday transactions, even one mistake can cause all comparable cases to go unnoticed.
Ensemble modelling uses several models for a single job, like fraud detection, to address the constantly changing fraudulent behaviors. Ensembling with classic machine learning, deep learning, and linear models can capture various fraud patterns to maximize outputs. The rationale for ensemble modeling is as straightforward as is the concept effective - why not combine the predictive capability of each of those distinct models into a composite to generate even more accurate predictions, as opposed to developing many machine learning models and selecting the one that appears to be the most accurate? Seasoned data scientists use this powerful technique to enhance the number of predictors, features, and variables for foretelling data-driven outcomes.
The two most common methods for ensemble modeling are called bagging and boosting. Bagging reduces the variance of machine learning models while creating more bias; boosting increases the variance of such models while decreasing bias. Both are instrumental in implementing effective and efficient ensemble modeling as well as for assisting businesses in maximizing the results of conventional machine learning.
Creating multiple distinct models from essentially the same dataset constitutes bagging. Building these models in parallel with one another enables data scientists to effect two advantages. They’re able to rapidly produce them while constructing them from various subsets of the same dataset.
Boosting combines a set of weak learners into a strong learner to minimize training errors. In boosting, a random sample of data is selected, fitted with a model and then trained sequentially—that is, each model tries to compensate for the weaknesses of its predecessor. With each iteration, the weak rules from each individual classifier are combined to form one, strong prediction rule.
Model Explainability
Sure, the final prediction in fraud detection is imperative but what to make of the reason that led the system to such conclusions?
Banks may utilize an automated method for classifying fraud to identify suspicious circumstances. Following an alarm, bank staff will likely investigate the circumstances that caused the system to detect a potential fraud, and an expert will make the ultimate determination. If a model cannot explain the reason, it could never be used by the authority or for legal purposes.
Model Explainability is a broad concept of analyzing and understanding the results provided by ML models. It is most often used in the context of “black-box” models, for which it is difficult to demonstrate, how did the model arrive at a specific decision.
Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and potential biases. It helps characterize model accuracy, fairness, transparency and outcomes in AI-powered decision making.
Many different tools allow us to analyze the black-box models, and interestingly each of them looks from a slightly different angle. Some of these techniques focus on a global explanation — a holistic view of the main factors that influence the predictions of this model like an overview of the Feature Importance of the model. Other tools focus on generating a local explanation, which means focusing on a specific prediction made by the model.
Here's an amazing excerpt on XAI tools and applications to help you along the way.
When used successfully, machine learning removes heavy burden of data analysis from your fraud detection team. The results help the team with investigation, insights and reporting.
Machine learning doesn’t replace the experts, but gives them the ability to reduce the time spent on manual reviews and data analysis. This means analysts can focus on the most urgent cases and assess alerts faster with more accuracy, and also reduce the number of genuine customers declined.