Fraud Analytics for a safer Cyber World ft Supervised Learning
People say that the real world we live in is not safe anymore. But the reality is that we humans spend as much time in the virtual world as we do in our actual surroundings. And guess what? The virtual world is equally unsafe. From credit card fraud to identity thefts, neither is one’s money safe nor is their identity in this cyber world.
In a recent incident, hackers were using their skill to hack victim’s mobile phones to get access to their UPI passwords and were digging into their savings accounts in short intervals of time. While this is one such incident, the list of crimes is pretty long.
Despite the presence of cyber laws, it has been proven time and now that it is difficult to stop cybercriminals before the crime has been committed. This is where fraud analytics comes into the picture. Fraud analytics does not only help in detecting the fraud committer post the occurrence of an event but also helps in deploying a system at the roots itself which shall aid in predicting fraudulent behavior, hence reducing the possibility of occurrence of the crime to a great extent.
Though in the title we have confined this process of fraud detection to a single term, fraud analytics has several variations and uses several methods to perform this predictive analysis using machine learning.
Both supervised and unsupervised learning methods of machine learning are in use for this particular part of analytics. In this article, we will delve into some of the supervised learning methods that are most commonly used and are also highly efficient.
Firstly, let us give you a brief introduction to supervised learning before digging deeper.
“In simple words, Supervised Learning can be defined as the method of using labeled data to train machine learning models.”
In supervised learning, the models store the recognized patterns which are found from the training data and use this knowledge to make predictions when the real-time data is given as input.
In this approach, four specific supervised learning methods are used for fraud analytics, namely
1. Logistic Regression
2. Decision Trees
3. Random Forest
4. Neural Networks
Now, let us delve into each of these individually.
1. Fraud detection using Logistic Regression:
Logistic Regression is applied when a categorical decision has to be made on the input variable, in this case, to decide whether a particular event of occurrence is a normal or a fraudulent event.
The procedure that is adopted here is as follows:
a. Firstly, certain decision points are taken as parameters. These decision points are recognized by the model from the training and test data.
b. When the real-time data is given to the model as input, the model analyses it over the given set of decision points.
c. The output given out by the model is a value between 0 and 1, 0 being an indication that it is least probable and 1 that the input is likely to be fraudulent.
2. Fraud detection using Decision Trees
Decision Trees are used to classify a given data set based on a specified set of conditions, in this case, similar to the previous case decision points are used as the set of conditions.
In a decision tree, the decision points act a series of hierarchically connected tests that the input data has to go through to collectively calculate the extent to which the input data is fraudulent.
3. Fraud Detection using Random Forest:
In the Random Forest approach, multiple decision trees are used to process the bifurcated parts of the dataset to achieve results with higher accuracy.
The given input data set is bifurcated over the recognized set of parameters and passed through multiple decision trees.
4. Fraud Detection using Neural Networks:
Neural Networks are similar structures as that of the human brain. They consist of multiple layers of processing nodes where complex operations are performed on the input data to achieve the required output.
In fraud analytics, the inner nodes analyze the data over the decision points to give out the required output.
While these are four methods that support fraud detection, several other methods also exist that don’t fall under supervised learning.
Currently, several business organizations whether in the finance sector or in healthcare use methods as such to eliminate the occurrence of fraud.
What we believe is fraud analytics should be applied to scenarios that don’t pertain to businesses as well. Cybercrime is an issue faced by most individuals who have a virtual presence. Hence we would like to conclude by stating that fraud analytics should be integrated for every individual who is a part of the cyber world. And if someone does not have access to such a system, why not build one by themselves?
A Computer Science graduate by education and a content writer by profession. Currently fulfilling her zeal to write by putting pen to paper every time she comes across something that is interesting enough to let the world know