5 ways to deal with online fraud detection using machine learning

At the moment, we can observe the following trend. The number of transactions made on the Internet is growing day by day, which means that businesses and ordinary users leave a huge number of traces that scammers can track.

Moreover, there is only one way to prevent online fraud with the highest level of security guarantees. This is the use of artificial intelligence and machine learning in banking and e-commerce. In this article, we will explain the potential of fraud detection using machine learning.

What types of internet fraud are the most common?

The virtual space gives fraudsters a lot of space to implement various schemes. Moreover, each anti-fraudulent protection tool is perceived by them as a new challenge, in response to which it is necessary to come up with an even more clever scheme. Below we list the standard online fraud methods that cover a huge number of individual and specific cases, making both private users and businesses and financial institutions victims of this illegal activity.

• Business fraud: Business fraud covers a huge number of cases. For example, coupon and promotional code fraud, chargeback abuse, clone site creation, and so on.
• Credit card fraud: Credit card fraud is one of the most widespread types of fraud in banking followed by identity theft. Within the framework of this scheme, fraudsters inject private financial information and use a credit card for their own purposes. Very often this happens with the consent of the deceived user since the fraudsters pose as bank employees.
• Internet auction fraud: This scheme involves the creation of an online auction and the sale of lots with payment in advance. Of course, buyers don’t receive any items, and their credit card credentials may be used in credit card fraud schemes. 
• Investment schemes: Fraudulent investment schemes often imply the absence of the asset itself for investment, or it may be a completely virtual fraudulent scheme. Cryptocurrency scams are a popular type of investment scam lately.
• Nigerian letter fraud: This is a popular alleged investment scheme in which scammers ask users to help with the withdrawal of money from Nigeria to another country, promising huge rewards in return, but the user must pay a forward transfer fee. After receiving this commission, the scammers disappear or continue asking the user to pay a little more due to “unforeseen problems with the withdrawal of money.” This is one of the oldest schemes that appeared at the dawn of the development of email, but it continues to work, although already on a smaller scale – only $700,000 are stolen per year.
• non-delivery of merchandise. As part of this scheme, fraudsters either intercept goods ordered by buyers from legal sellers and resell them on the black market, or create clone sites in order to force users to buy an imaginary product at a low price, leave their financial data, but of course, no product will be delivered to them.

How does online fraud occur?

As you can see, online scams can happen in a variety of ways. Very often, the user voluntarily transmits his private data or commits a fraudulent transaction. Of course, hackers are also on the alert – they are working to break into the databases of major sites and financial institutions, and gain access to all information, including passwords and credit card credentials.

For this reason, online fraud detection using machine learning is becoming a must-follow practice for businesses that want to protect both their customers and their reputation. Next, we will discuss the potential of machine learning in banking for fraud detection and prevention.

How does machine learning detect fraud?

If we try to explain the essence of machine learning in banking in simple words, then it is easier than it might seem. Any bank has a huge amount of information about its customers, and this information is not limited to the bank account number. Banks are also aware of what IP address we use, what goods we buy, what amounts we credit to our cards, and withdraw from an ATM.

All this information is a huge array, which, however, can be analyzed in the context of each specific user. Thus, we get user insights, behavior patterns that are typical for each of the bank’s clients. A machine learning model observes this behavior and analyzes patterns in order to find anomalies – for example, using a different IP address.

As soon as an anomaly is detected, the system signals a possible fraud and then authorized bank employees deal with each specific case. The new data obtained during the investigation of the case can be used to further train the artificial intelligence model in banking.

Online fraud detection using machine learning – 5 ways to deal

According to Fraud Detection using Machine Learning and Deep Learning research, “Frauds are known to be dynamic and have no patterns, hence they are not easy to identify. Fraudsters use recent technological advancements to their advantage. They somehow bypass security checks, leading to the loss of millions of dollars. Analyzing and detecting unusual activities using data mining techniques is one way of tracing fraudulent transactions.”

However, there are five essential techniques for online fraud detection using machine learning that help with data analysis and making credible predictions. 

1. Email phishing fraud detection model: Phishing emails are often used to compromise bank employees. In this case, artificial intelligence in banking helps detect phishing emails by analyzing a number of signs and notifies employees of a fraudulent attempt before they open the email or use a malicious link. 
2. Identity theft detection model: This model helps the bank to detect identity theft of their customers through behavioral analysis and the detection of abnormal patterns of behavior.
3. A model for credit card fraud detection using machine learning: In this case, machine learning in banking analyzes information about credit card transactions and also identifies possible anomalies.
4. ID document forgery detection model: With this model, it is possible to distinguish a fake document from a real one through the technology of image and security symbols of the document recognition.
5. Deep learning: The deep learning model is able to analyze the smallest details, plus learn on its own and take the most invisible anomalies into account. 

Artificial intelligence in banking  – Case studies

Leading banks are already implementing online fraud detection using machine learning. Here are some examples.

JPMorgan	This bank employs machine learning for document analysis.
Wells Fargo	Wells Fargo bets on better customer interactions with the help of a smart chatbot.
Bank of America	Bank of America also uses a chatbot to improve customer experience. Their chatbot analyses the users’ financial habits and provides them with instant notifications and smart advice.
City Bank	City Bank utilizes credit card fraud detection using machine learning, plus their system may help with money laundering attempts identification.

Machine learning in banking – Other use cases

As you can see, the practical possibilities of artificial intelligence in banking do not end with fraud detection and prevention. Data analysis is also useful for developing personalized marketing strategies, analyzing investment risks, making loan decisions, and combating money laundering and terrorist financing.

Thus, machine learning in banking is becoming a versatile technology that can effectively help solve the most pressing problems and improve business processes.