Detecting and preventing fraud in financial companies, such as banks, insurance companies, and credit unions, is an important task in order to see a business grow. So far, in the previous chapter, we have seen how to use classical supervised machine learning models; now it's time to use other, unsupervised learning algorithms, such as autoencoders.
In this chapter, we will use a dataset having more than 284,807 instances of credit card use and for each transaction, where only 0.172% transactions are fraudulent. So, this is highly imbalanced data. And hence it would make sense to use autoencoders to pre-train a classification model and apply an anomaly detection technique to predict possible fraudulent transactions; that is, we expect our fraud cases to be anomalies within the whole dataset.
In summary, we will learn the following topics through this end-to-end project:
- Outlier and anomaly detection using outliers
- Using autoencoders...