How To Use Machine Learning For Fraud Detection
Online shopping may be convenient for everyone, but it also comes with a risk. By selling goods and services online, there’s a risk that either of the two parties—buyer or seller— will scam the other. It is also true for many high school and college essay writing service platforms for students who need help with their assignments, homework projects, and coursework in general. With the rapid development of e-commerce, fraud activity has also evolved and is harder to detect. From offline bank scams to identity theft and money laundering schemes, fraudsters will take advantage of every weak spot they see in a business’s system.
Today, detecting and preventing fraud are major concerns for the e-commerce and banking industries. However, by applying machine learning to systems, one can eliminate and prevent such activity.
Machine learning (ML) is a branch of computer science that centers around data and algorithms, so machines could ‘learn’ the same way humans do. If done properly, machine learning can easily identify legitimate and fraudulent behavior. With that being said, read this guide on how machine learning is used for fraud detection.
- Allow For Data Entry
First, a machine learning model will have to collect certain data. Data entry is different for machine learning and humans. Normally, humans find it hard to grasp a huge amount of data in a short period of time. This task is easy for machine learning. The more data is entered into an ML model, the more it can learn and improve its accuracy.
- Let It Extract Features
Next, a machine learning model will extract features that usually include the customer’s information such as identity, location, and payment method. In addition, features that describe normal customer behavior and fraud behavior are entered. The added features may differ based on the detection system’s complexity.
- Initiate The Training Algorithm
Third, a training algorithm for the model is initiated. For a period of time, the model will follow a set of rules to determine whether an operation is legitimate or fraudulent. Two common algorithms that ML engineers use are supervised and unsupervised learning:
- Supervised Learning
In a supervised learning algorithm, the model learns on a provided dataset and answers. All information has to be labeled either good or bad. Then, the learning model predicts fraud activity based on the provided data. Common supervised learning algorithms include:
- Decision Trees – Algorithm that sets different rules to verify data at every step. To prevent fraud, the model identifies activity by introducing decision trees that describe legitimate customer behavior.
- Random Forest – Built upon decision trees, random forest calculates the average predictions of decision trees. To learn more, a trusted database made a post about random forest.
- Logistic Regression – Simple algorithm that predicts the probability of an event based on certain variables. Financial institutions use logistic regression to guard against phishing and credit card fraud.
- Unsupervised Learning
In an unsupervised learning algorithm, the model does not learn on labeled data. Instead, the model learns by processing and analyzing new data. The model will learn to notice patterns and distinguish whether they are legitimate or fraudulent activities. Unsupervised learning algorithms include:
- K-Means Clustering – A clustering algorithm that learns on unfamiliar datasets by classifying data that are similar to each other.
- Local Outlier Factor (LOF) – Similar to K-means, LOF clusters data and surveys its values.
- Isolation Trees – Algorithm that also relies on decision trees. Unlike random forest, it is unsupervised and follows different rules to predict behavior.
When the ML model finishes its training period, the model is ready for business. The model should be able to detect fraud accurately in real-time. Eventually, fraudsters will invent new schemes to commit financial fraud. So, to successfully detect further fraud activity, the ML model will have to be tested and upgraded from time to time.
Why Use Machine Learning In Fraud Detection?
Most companies used to rely on rule-based systems for fraud prevention. In a rule-based approach, the company identifies fraud activity by comparing them to the rules written by cybersecurity experts. To verify, each transaction goes through hundreds of tests. If any of these tests fail, the transaction may have to go through another set of verification tests. Although this approach may be secure, it is very difficult to detect complicated patterns that machine learning can identify.
Machine learning can detect patterns in financial transactions and determine whether it is legitimate or not. They can also process loads of information faster and detect patterns that can go unnoticed by humans. In this way, machine learning models are more effective than humans. The more data they process, the more accurate they become. Additionally, in the long run, more data means more fraud detection algorithms.
With all these in mind, machine learning models are more successful in terms of speed and accuracy. Also, it is more affordable since you won’t bother hiring a team of analysts.
Machine learning is a very effective tool that helps companies detect and prevent fraud. By replacing traditional rule-based systems with machine learning models, businesses can reduce losses from fraud operations. Also, it provides businesses with a more secure platform. Therefore, machine learning improves the speed and accuracy of fraud detection and reduces costs while increasing security.