How To Spot Credit Card Fraud Using Data Science and ML techniques

Credit card fraud detection has become increasingly sophisticated — leveraging high-quality data, advanced machine learning and analysis, and complex algorithms. Yet despite these advances, there are still gaps in our ability to detect fraud at an early stage, thus protecting your business from both the costs associated with false positives and the potential impact on your brand reputation and bottom line.

The FBI estimates that $7 billion worth of credit card data is stolen every year. The problem is getting worse: According to a recent study from IBM, more than 1 million cases of credit card fraud were reported in the US in 2018 alone. Crazy right?

This article will explore an overview of credit card fraud detection and ML techniques to detect fraud patterns in credit card data using machine learning algorithms such as decision trees or sequence models.

What are the Challenges involved in fraud detection?

Accurately huge amounts of data are processed daily, and the model development must be quick enough to detect scams in time. It is very difficult to identify fraudulent transactions because of the imbalanced data, which means that 99.8% of all transactions are legitimate. Data accessibility because most of the data is private. Misclassified data is another significant problem because not all fraudulent transactions are discovered and reported. The fraudsters utilized adaptive methods against the model.

How to Tackle These problems?

The used model must be quick and straightforward in order to identify the anomaly and classify the purchase as fraudulent as soon as possible. Imbalance can be corrected by accurately applying some techniques. The dimensionality of the data can be lowered to secure the user's privacy. It is necessary to use a more reliable source that verifies the data, at the very least, when training the model. When the fraudster adapts to it, we can create a new model that is ready to use by making minor adjustments to the existing one.

Requirements For Credit Card Fraud detection

Several essential criteria must be met in order to operate an AI-driven approach for Credit Card Fraud Analytics. These will guarantee that the model achieves the highest detection score possible. Amounts of Data It takes a lot of internal historical data to train high-quality ML models. Because the effectiveness of a machine learning model's training phase relies on the quality of the inputs, it would be daunting to apply one if there were insufficient previous cases of both fraudulent and legitimate transactions.

Check out the popular machine learning course in Bangalore to learn about the latest AIML tools and techniques used in the real world. Quality of Data Models can be biased depending on the nature and quality of the historical data. This implies that if the platform administrators did not gather and organize the data neatly or even mixed the data of fraudulent transactions with the information of normal ones, that is likely to lead to a significant bias in the model's results. Integrity of Factors There is a very good chance that fraud detection will benefit both your customers and your company if you have sufficient, well-structured, unbiased data and if your business logic and the machine learning model are well-matched.

Credit Card Fraud Detection Using Machine Learning Techniques

There are majorly two categories of advanced credit card fraud identification methods:

Unsupervised ML technique - PCA, LOF, One-class SVM, and Isolation Forest. Supervised. ML techniques like Random Forest, KNN, and Decision Trees ( XGBoost and LightGBM).

Unsupervised Machine Learning: The credit card fraud detection dataset can be used to group data points by similarities without manual labeling using unsupervised ML techniques, which use unlabeled data to find patterns and dependencies.

PCA — Exploratory data analysis, which reveals the internal structure of the data and explains its variations, can be carried out using PCA (Principal Component Analysis). One of the most widely used methods for detecting anomalies is PCA.

When analyzing credit card transactions, PCA looks for correlations between features, such as time, location, and amount spent, and determines which values are responsible for the variability in the results. A smaller feature space known as principal components can be produced using these combined feature values.

SVM — Data outliers can be found using the one-class SVM (Support Vector Machine) classification algorithm. With this algorithm's aid, one can address unbalanced data problems, such as fraud detection.

One-class SVM is designed to train only on a significant number of valid transactions, and then by comparing each new data point to those transactions, to detect anomalies or novelties.

Isolation Forest — Isolation Forest (IF) is a decision tree-based anomaly detection technique. The core concept of IF is that it precisely detects anomalies rather than profiling the positive data points, differentiating it from other well-known outlier detection algorithms. Isolation Forests are mainly composed of Decision Trees. The first step in separating data points is choosing a split value between the minimum and maximum value of the feature being used randomly.

Supervised Machine Learning

Supervised ML techniques make use of labeled data samples, allowing the system to predict labels for previously unknown data. We describe Decision Trees, Random Forests, KNN, and Naive Bayes among supervised ML fraud detection techniques.

K-Nearest Neighbor — A classification algorithm called K-Nearest Neighbors constitutes similarities in multidimensional space in accordance with the distance between objects. Therefore, the nearest neighbors' class will be given to the data point. Larger datasets can be created faster using this method since it is resistant to noise and missing data points. Besides this, tuning the model takes a small amount of work from a developer, which is quite accurate.

X boost — A unique gradient-boosted Decision Trees algorithm, known as XGBoost (Extreme Gradient Boosting) and Light GBM (Gradient Boosting Machine), was developed for speed while also maximizing the efficacy of computing time and memory resources. This algorithm combines existing models with new models to correct any errors they may have introduced. Random Forest — An algorithm for classifying data called Random Forest uses numerous Decision Trees. Each tree contains nodes with conditions that specify the outcome based on the highest value.

Two main factors that the Random Forest algorithm for fraud detection and prevention possesses make it effective at making predictions. In the first, the rows and columns of data are randomly selected from the dataset and fitted into various Decision Trees.

Summary In this blog on credit card fraud detection, we looked at what might be considered "traditional" methods of fraud detection and how data science can improve upon them. Since the emergence of credit cards, technology has made fraud detection easier. With most people paying for goods and services with their credit cards more than ever, data scientists are challenged to make fraud detection simpler, more accurate, and quicker. As synthetic data becomes available, it is possible to build an automated model that will detect card fraud. For a more practical explanation of ML techniques, visit the data science course in Bangalore and master the ML techniques.