Fraud Detection Using Data Science
In response to the ever-emerging fraud threats scientists from different fields and industries try to develop appropriate countermeasures; however, criminals always stay one step ahead. According to the Security Magazine’s data, 47% of all credit card fraud happens in the U.S. How to identify and prevent fraud like this? By using big data analytics tools and techniques and today we will show how it can be done.
How to Detect Frauds: Methods and Techniques
Let’s start with considering basic techniques that allow to detect fraud in the financial sector using classification methods. We will learn algorithms that help to perform threat prevention by labeling legit and fraudulent transactions correspondingly. Taking into account that fraud detection is a task which involves structuring threats we feel obliged to shed some light on a few approaches here.
Bayes Rule-Based Methodology
A Naive Bayes principle consists of two main statements: the first one means that all entry points are equally important and the second statement is based on the assumption that all elements are fully independent. The “independence” means that the known value of one element can say nothing about any other one which is not always true in reality. While calculating the probability of a fraud, the corresponding formula has to be applicable to both legitimate and fraudulent classes.
A decision tree is a method that uses one of several existing algorithms for threat prediction:
- Iterative Dichotomiser 3 (ID3);
- Successor of ID3;
- Classification and Regression Tree.
The decision tree consists of nodes that form its initial structure. Topmost nodes are called the root nodes, leaf nodes are class labels, non-leaf nodes represent element tests and brunch leafs correspond to test results. This method helps represent the fraud probability calculation in a structured way.
K-Nearest Neighbors Algorithm (KNN)
KNN is a threat detection algorithm that classifies uncategorized instances taking into account their closest neighbors. This algorithm means that all uncategorized instances are categorized based on calculation of the distance between these instances. In this algorithm, instances form a model, while decision tree means that those instances form a tree at first and then a tree represents the calculation model.
Support Vector Machines
A method of support vector machines was created in 1992 for dealing with the binary classification tasks. Later, it was improved and enhanced to solve nonlinear regression tasks. The Support Vector Machines approach utilizes a nonlinear mapping in order to turn the initial data into a multidimensional feature space. It is based on the structural risk minimization principle and its task is to detect the most probable threat in the feature space.
All methods mentioned above can form a set of performance metrics that will help analyze security threats in a more efficient way.
Metrics that can be used in fraud predictive analysis:
- Balanced classification rate;
- Confusion matrix;
- Matthews correlation coefficient;
- False positive rate;
However, just knowing methodologies that help to detect security threats, is not enough for effective fraud detection. That’s why it’s time to talk about how to implement the machine learning analytics. In any company.
How to Implement Big Data Analytics to Detect Frauds
Implementation of big data analytics especially in financial, insurance, and healthcare organizations is highly important. However, companies often face various obstacles regarding the use of different tools and data mining systems. Here are several steps that will help implement the efficient fraud detection analytics in your company.
Fraud Management Team
Companies usually do not hire professionals for solving fraud detection problems. Truth to be told, this team can become your saving node that will timely detect fraudulent actions or data breach attempts. A separate team of fraud detection specialists will definitely pay off once a massive data breach is prevented.
Ensure that all databases are integrated in the systems of your company and regularly check if your stored data is clean. Review whether an undesired software is installed or unrecognized files are in the storage. The data cleanliness is the key to the digital security.
Fraud Detection Tool
It is hard to imagine an effective fraud detection without a corresponding tools installed. It is up to the top management team either the organization is capable to build a fraud detection tool for the network analysis or it should buy one.
Fraud Detection: Case Studies
To let readers better understand how various fraud detection methods can help avoid security threats, we have a few real case studies. Reviewing these precedents will allow you to implement a more effective strategy of fraud prevention.
Several security experts have implemented an efficient Big Data-based concept to prevent terrorism in Abu Dhabi. Their system checks all the data that flows into governmental databases and then apply a statistical data model to detect any terrorist or cyber crime activities.
The European Government has developed a POLE data model that stores and record incidents related to the terrorism. This system also uses text mining and can detect fraud in another country by reviewing Twitter posts using a specific recognizing algorithm.
Online Fraud Detection
The U.S. based big data company called EMC uses machine learning to prevent online fraud. Their system has managed to detect 500,000 attacks in eight years. The system uses 20 specific factors, which applied to any transaction flowing through this system and creates a notification in case risk features have detected fraud.
Fraud detection plays a crucial role for saving finances, important data, and even human lives. Corresponding tools will help timely detect cyber criminals or even terrorists in every place we can even imagine. Now, you see that many organizations already use different techniques and methods for fraud detection and it helps them avoid tremendously negative consequences of criminal threats. That is why it’s important using artificial intelligence methods to develop and implement an effective strategy that will protect your company from any threat.