How Machine Learning is Utilized in Fraud Detection for Casinos

The world we live in is highly influenced by the internet and technology. Most of the activities we carry out daily have some form of connection to technology. As a result, criminals have also ported to the internet to perpetrate their malicious actions. This set of criminals, also known as cybercriminals, consistently look for loopholes in software and websites to carry out illegal activites.

Every industry has been affected by cybercrime in some form in the last decade. Research by The Association of Fraud Examiners has shown that companies lose 1/20 of their revenues to hackers.

One of the industries highly targeted by hackers is the casino industry. Even when casinos only existed in Las Vegas and expensive resorts, criminals attempted different ways to weasel money from them. Now that online casinos are on the rise, iGaming fraud has become more convenient for hackers.

Nevertheless, the use of big data has been found to combat privacy breaches efficiently. Casinos listed on casinovator.com have begun to leverage machine learning models to model the behavior of cybercriminals. This article will explore the processes used to prevent fraud in online casinos.

Steps in Facilitating Machine Learning to Prevent Casino Fraud

• Understanding the Problem

The problem with fraud analysis in most casinos is that they hire fraud analysts that manually go through players' activity to determine if the player is fraudulent. Data scientists try to automate this process by leveraging machine learning and AI to go through the same amount of data in a considerably shorter period.

The AI is built using machine learning tools to alert the analysts when a player is performing a suspicious activity. The goal is to enhance the analysts' efficiency in a casino and expedite their workflow.

The first step is to create a dataset with a target column or dependent variable displaying 'Fraud' or 'Not Fraud' for the player. Although the dataset is highly likely to be imbalanced since only a small percentage of players in a casino are fraudulent, balancing techniques for classification models could be used later on.

• Data Wrangling and Exploratory Data Analysis

To free the dataset from noise, data wrangling techniques can be utilized. First, the rows with players with very little activity and a 'Not Fraud' label can be dropped. That'll make the dataset more balanced between 'Fraud' and 'Not Fraud' labels.

The key columns that should be evaluated during exploratory data analysis to predict whether a player is a fraudster or not should include gaming patterns, demographics, payment method, and location.

Since Pearson's correlation can't be performed using categorical variables like these, an exploratory analysis should center around visualizations.

• Model Building and Evaluation

This problem is essentially one that can be solved with classification models under supervised machine learning. The best classification models to detect fraud include Logistic Regression, Light Gradient Boosting, Random Forest, and Decision Tree.

A train-test split can first be performed on the dataset to ascertain how well the model will perform on real-world data. As a plus, the K-fold cross-validation technique can be used to test different training data on various validation datasets.

These classification models can be utilized to find an eventual model that gives the best Precision, Recall, and F1 evaluations. Hyperparameter tuning can also be performed to increase the Accuracy metric of the models.

• Prescriptive Modeling  

As a machine learning engineer, your job is not only to create a model but to inform the fraud analysts of the jargon that your program is churning out. Hence, you need to give short descriptions of why the model is flagging down a player as fraudulent.

This will help the casino analysts better explain when a player gets restricted from gaming. As a plus, you can display the probabilities of a player being a fraud or not.

Conclusion

For decades, casinos have faced the scourge of criminals who try to steal money from them. With machine learning and big data, fraudsters can be stopped from inflicting damage on casinos.

You need to understand the problem and utilize data wrangling techniques on the dataset that identifies fraudsters and non-fraudsters. Furthermore, a machine learning model needs to be built using classification in supervised learning to predict the fraudulent activity and explain to casino analysts why the player is restricted from gaming.