An Overview of Machine Learning in Fraud Detection

Online fraud has been rampant ever since online transactions became a thing. While being able to pay for products and services online is a noble digital milestone, industries, and consumers have been dealt a blow by fraud. Fraud has been prevalent in several industries like insurance, health, banking, and e-commerce. 

Coding, working with machine learning – illustrative photo. Image credit: Charles de Luvio via Unsplash, free license

Huge losses have happened in the process, with e-commerce losses resulting from online fraud estimated at $41 billion in 2022. To avoid suffering from the financial setbacks caused by fraud-related activities, small, medium, and big companies have resorted to technologically advanced techniques like machine learning to set up systems that can detect and possibly eliminate fraud altogether. 

Businesses can leverage machine learning development services to safeguard transactions and streamline other financial aspects of their daily operations. Machine learning is among the most advanced technological strides that have proved effective in combating fraud. Before delving into the role of machine learning in curbing online fraud, it is critical to comprehend different types of fraud. 

Different types of online fraud

These are the most popular types of online fraud:

1. Identity theft

This fraud occurs when someone gains unauthorized access to another individual’s private data, such as passwords and bank details to transact, file taxes, withdraw funds, or apply for credit. In most cases, victims of identity theft get wind of the activities too late.

2. Email phishing

Attackers send emails resembling legit addresses, which most victims click without a second thought, only to realize when their data has already been intercepted. 

3. Transaction fraud

Online shopping is slowly taking center stage but only with a few limitations, such as cybercriminals’ invasion. In transaction fraud, online shoppers are tricked by fraudsters into clicking on nonexistent offers with expiry dates, and their goose is cooked upon sharing their credit information. The fraudsters achieve this by creating fake sites similar to authentic ones to blindside the victims into not suspecting anything.

4. Tax scams

Fraudsters use the tax refund period to defraud taxpayers by posing as tax department officials. They communicate to their targets via SMS or email, asking them to submit their financial information to receive the refunds, while the primary intention is to get hold of their bank information. 

5. Lottery fraud

Another prevalent form of online fraud is lottery fraud. Cybercriminals send emails or messages to unsuspecting victims claiming they have won the lottery, urging them to share their credit card details to receive the money, 

Supervised and unsupervised machine learning in fraud detection

Machine learning uses different models to detect fraud, including supervised and unsupervised. 

Supervised Learning

This human-supervised learning model labels all data sets as good or bad. That way, all input data has a predicted outcome. The supervised learning model is unreliable in detecting fraud not labeled because it is designed to learn only input data. This model requires well-organized data for it to issue accurate results. 

Unsupervised learning

The unsupervised machine learning algorithm is built to detect behavior from unlabelled data clusters and does not require human supervision. The model analyses new data and learns patterns by itself, which helps detect fraudulent schemes before they unfold.

How machine learning works in detecting fraud

Machine learning works in the following simple steps;

• Data Input: for machine learning to take place, data is required. The data can be labeled or not depending on the type of machine learning. Also, the more data, the better.
• Features Extraction: machine learning proceeds to generate customer behavior. The generated features may include the customer’s address, device, shopping frequency, and location. 
• Algorithm Training: The algorithm uses consumer data to make fraud predictions.
• Model Creation: the final step is to create a model based on your business and consumer activities, not forgetting to carry out routine updates for new clients.

Benefits of machine learning in detecting and preventing fraud

Besides preventing profit loss, companies can benefit from fraud detection using machine learning in several ways, such as follows:

1. Machine learning is economical 

Since machine learning is fast, it can detect fraud amongst massive datasets in seconds, lifting the financial burden of using costly conventional means of fraud detection that also take longer. Remember that machine learning becomes more accurate and efficient the longer it operates.

2. Enhanced automation

Machine learning automation is critical in eliminating redundancy or repetitiveness associated with manual processes and comes in handy in detecting fraud. Automation allows real-time analysis, enabling users to prevent and detect fraud in milliseconds despite the data workload. On the other hand, the traditional approach is time-consuming and has a high rate of false positives.

3. Ongoing fraud detection

Machine learning is continuous, implying that businesses and organizations can count on consistent fraud detection. With time, machine learning systems can differentiate genuine and fake transactions.

4. Precise fraud detection

Security analysts can rely on machine learning for fast and accurate fraud detection compared to manual or rule-based approaches that can give false positives. 

Tips for selecting a machine learning fraud detection model

Choosing a reliable machine learning model for your business or organization may require you to consider several things. Pay attention to the following factors:

• Automation Leve: this requires you to consider the amount of data to be automated to reduce the number of manual tasks and eliminate errors. Hiring fraud analysts may not be necessary to ensure you train your models for the best outcome.
• Time and Cost-Effectiveness: different machine learning models have variable pricing, so you should settle on a model that is within your budget but with high levels of accuracy in detecting fraud.
• Protection Layers: it is essential to consider a model with several layers of protection, with each level showing different user behavior for easier detection.
• Customer Support: you may need answers or clarifications on some areas while utilizing machine learning software, showing why responsive customer support is critical.

Closing thoughts

There is no doubt that machine learning is aiding in detecting fraud. Do not wait to fall victim to online fraudsters to embrace machine learning because you may never recover from the financial setback. You can save a lot of money by investing in reliable machine-learning software to protect transactions and keep fraudsters at bay.