Which algorithm is used in fraud detection?

Which algorithm is used in fraud detection?

What machine learning method will uncover the fraud? 

Nowadays, machine learning is becoming increasingly essential in the field of fraud detection. So which machine learning method will be better for uncovering fraud? In my point of view, Boosting has better potential compared with deep learning.  

From a purely modeling perspective, the process of identifying fraud can be summarized as a classification problem. While combined with the application scenario, it becomes regarded as an anomaly detection problem. As part of it, fraud detection has corresponding problems of category imbalances, diverse forms of fraudulence situations, and so on.  

Due to the unbalanced dataset, deep learning may not be our first choice.

The deep learning model mainly refers to deep neural networks. By calculating the loss between predicted and true labels, the model trains to figure out the correct prediction about a given sample.

With an unbalanced dataset, the model will focus primarily on optimizing the majority class, while missing the underlying class. Even if the accuracy is high, its performance in the minority class may not be that good. As fraud samples usually make up only a small part of the entire dataset, DL is not a proper choice to uncover fraud. Although there are several approaches to solve this problem, like oversampling and adjusting loss functions, we prefer to explore other models that fit better with the fraud detection scenario.  

Ensemble learning is a great option for conquering the problem of the imbalanced dataset.

In particular, the boosting model performs best in extensive fraud detection experiments. Boosting is an ensemble meta-algorithm for primarily reducing bias and variances in supervised learning. During iteratively training, misclassified input data will gain a higher weight, while samples that are correctly classified lose weight.

Future weak learners will focus more on the misclassified part. In fraud detection applications, the fraud type always takes the minority of the dataset. This class trains less and is more likely to get the wrong prediction. Then the boosting model will pay more attention to the fraud samples until it achieves great performance on this unbalanced dataset. Moreover, as the boosting model has a composition of several weak learners, it also has good generalization ability and robustness to outliers. As a result, the boosting model becomes a good fit for fraud detection.  

In conclusion, considering the fitness with the anomalous problem, Boosting may have a better potential for  finding the intricate pattern of fraudulence than DL.  

Yiwen(Evelyn) Song 

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Which algorithm is used in fraud detection?