What is fraud, and why does data science find it interesting?

Fraud, which the Cambridge Dictionary defines as “the crime of acquiring money by deceiving people,” is as old as humanity itself since fraud can happen whenever two persons exchange commodities or engage in business. Because more and more people are using the internet for banking, shopping, making insurance claims, and other purposes, these companies are becoming targets of fraud on a completely new level. In e-commerce, fraud has grown to be a significant issue, and considerable effort is being put forward to identify and stop it. The methods used to detect fraud in the past have been rule-based. This implies that rigid guidelines must be manually defined in advance to flag a transaction as fraudulent. However, this rigid structure forces the seller’s fraud detection system into a constant arms race with fraudsters looking for ways to get around these restrictions. The contemporary solution is to make use of the enormous amounts of Big Data that can be gathered from past transactions and model it in a way that allows us to detect or foretell fraud in subsequent transactions. The apparent answer to this is Data Science and Machine Learning methods like Deep Neural Networks. I’m going to give an illustration of how data science methods can be used to spot fraud in turnover transactions right now. I will provide some explanations of the underlying workings of fraud analysis that are simple enough for laypeople to understand.

Reduction in dimensions

What machine learning techniques are appropriate for fraud analysis?

The field of machine learning is vast. It includes a broad range of methods and algorithms for classification, regression, clustering, and anomaly detection. For supervised and unsupervised learning, there are two basic kinds of algorithms that can be separated.

• Supervised learning:  is employed to forecast either the values of a response variable or the labels of a collection of predefined categories (classification tasks).

• Unsupervised learning is used to find clusters or outliers/anomalies in data sets; it doesn’t require pre-defined labels or response variables.

Detecting anomalies with deep learning Autoencoders

Both supervised and unsupervised learning tasks use neural networks. In unsupervised learning, autoencoder neural networks are used to detect anomalies; they employ backpropagation to develop an approximate identity function, where the output values equal the input.