Fraud Detection Using Machine Learning.

According to PwC's Global Economic Crime and Fraud Survey, 47% of the respondents had experienced fraud in the past 24 months.

On average, companies have experienced six incidents of fraud in the last 24 months.

Fraud has been a menace to society for so long, longer than you can ever imagine. It can be dated as far back as the year 300 BC, and ever since then it has been on the increase.

In a time like this where most business transactions are carried out online, more and more businesses are at the risk of being defrauded, which is why now more than ever, these fraudulent activities need to be detected, monitored and controlled.

Fraud Detection.

Fraud detection refers to a set of activities that is used to detect, monitor and prevent fraudulent activities before they can take place.

Fraud detection is prevalent in prominent eCommerce stores, banks, insurance companies, credit card companies and other businesses with strong online presence.

How Does Fraud Detection Work?

Frauds are usually perpetrated in patterns. These patterns are detected by machine learning, behavior monitoring and statistical analysis. 

When the system identifies these fraudulent patterns, it is able to intercept the activity before they take place.

For the above to work, the system would have to be fed with similar patterns i.e instances of already perpetrated frauds so that it is able to identify such patterns when they reoccur.

Let us examine a simple Fraud Detection method that reveals in clearer context how abnormalities are detected by models that have been trained. For the purpose of this project, we would use a type of Machine Learning called “UNSUPERVISED LEARNING”, which would be explained better after the exploration.

To begin we would first examine the dataset we would be addressing, loading it into the model’s data frame as shown below

Having established that the dataset is good for the purpose of our examination, we would proceed to the analysis phase to better understand the regular pattern, which would help to detect when there are any anomalies.

Then we would also see from the dataset that there are fraud cases that have been loaded, which would be the focus of examining the anomalies from the regular other entries in the same dataset.

We would then proceed to use Unsupervised Machine Learning technique to train our model.

The above Unsupervised Learning has helped to train the model as well as revealing the anomalies and differences between the fraudulent datasets and the non-fraudulent ones.

Then we could deduce the analysis shown below on the percentage of valid vs fraudulent transactions.

Conclusively, with the above illustration, if another dataset is fed into the model, it would be able to perform the same explorational analysis and deduce which activities are fraudulent, based on how it had been trained.

How Can Fraud Be Detected Using Machine Learning?

Machine Learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data.

With machine learning, a model is built based on sample data. Due to this programming, the model is able to make certain prognosis and decisions without human intervention.

Fraud Detection using Machine Learning uses a machine learning model with an already studied data set, to train the model such that it is able to identify fraud patterns.

What's more? Such models are usually self-learning so that they are able to adapt to new, unknown patterns.

These machine learning models can come in different types:

• Supervised Learning.

• Unsupervised Learning.

• Semi-supervised Learning.

• Reinforcement Learning.

Supervised Learning.

Most practical machine learning models utilize supervised Learning.

In supervised Learning, there are two variables: input variable(Y) and output variable (Z). After inputting these variables, an algorithm is used to learn the mapping function from the input variable to the output variable. All input information is tagged good or bad.

It is called supervised learning because the learning process is supervised by a teacher. When the algorithm makes predictions based on the information, the teacher corrects it.

Despite being commonly used, its major limitation is that it can't identify any other information aside from the ones it was trained with.

Unsupervised Learning.

The goal in unsupervised learning is to train a model with a set of vetted data, such that the model is able to learn more about the data and make future predictions.

In unsupervised Learning, a teacher is not involved. The algorithms are left to make the decisions.

Semi-supervised Learning.

Just as the name implies, it sits between supervised and unsupervised learning. It works perfectly for cases where some of the information is labeled and some are unlabelled.

It is cost effective, and this is because apart from being time consuming, labeling of data is also expensive.

Reinforcement Learning.

This type of machine learning is based on maximizing rewards, yes rewards!

Developers devise a method to reward desired action and punish undesired ones.

In order to receive these rewards, the trained agents (models) tries to carry out the desired action and avoid the undesired ones.

I like to liken this to a dog that will do everything to please its owner, just to get a treat.

Final Thoughts.

According to InfoSecurity magazine, 40 sectors lost £15.6 trillion of expenditure to fraud globally in 2018. Fraud is ravaging the economy, costing businesses and individuals a fortune.

Machine Learning has proven to be a very effective tool in curbing these malicious activities, making the world a better place.