How Can Machine Learning Help in Fraud Detection

In 2018, fraud had an impact worth more than $3 trillion on the global economy.

The pandemic years of 2020 and 2021 saw online crimes increase at an unprecedented rate as businesses struggled to increase their client and customer base. Fraud and scams have grown in tandem with internet penetration over the last few years.

Cybersecurity firms and think tanks around the world are dedicating a huge amount of resources to fighting online fraud. Machine learning in fraud detection is a result of these concentrated efforts. Machine learning is now regarded as one of the most promising answers to online crimes and fraud.

Put simply, machine learning seeks to replicate human behavior by studying vast amounts of data.

Fed with historical data, machine learning algorithms can come up with risk rules and suggest actions based on user behavior. When used correctly, machine learning is already proving to be effective against online fraud.

In this article, we will go through the developments and challenges of machine learning in fraud detection.

How can machine learning help in fraud detection?

We’ll understand why machine learning is such a promising solution to fight cybercrime and also understand its limitations. Before that, let’s understand what is online fraud and what it entails.

If the present rate of growth in machine learning continues for the next decade, it can be the leading technology for fraud and abuse prevention and detection across sectors.

What is online fraud?

Online fraud is an umbrella term for different manipulative and deceptive tricks used by cybercriminals to steal money and information from businesses and individuals. Some of the most common examples of online fraud are:

  • Credit card chargeback scam
  • Phishing
  • Identity theft
  • Spoof websites
  • Fake lotteries and charities
  • Cryptocurrency scam

We can extend the list much more if we want to, but this gives a general idea of what online fraud means. Fraud is as old as money. People have historically used unfair and deceptive means to take money from others. Modern online scams use sophisticated social engineering and technologies to extort data and resources.

Which sectors are the most affected by online fraud?

The global e-commerce and banking sectors are the most vulnerable to online fraud. However, that doesn’t mean other sectors are safe. Social media platforms, for example, are always at risk of losing sensitive user data to hackers.

For the average consumer, e-commerce crimes are the most common. There are various e-commerce crimes, from creating spoof websites to stealing credit card data. Some of these methods use basic social engineering principles while others may require specialized snoopware.

The banking sector is also at risk since the growth of fintech has made banking and the internet inextricably linked.

How can machine learning help in fraud detection?

It’s possible to detect fraud with machine learning by training machine learning engines. In simple words, machine learning algorithms study historical data to identify patterns of fraud behavior. When the machine learning engine detects similar user behavior, it immediately sends a warning or blocks the user.

Machine learning engines generally become better with time. The more data you provide, the better the algorithms become. Machine learning is inherently more effective than humans in fraud detection because of its ability to store and learn from huge chunks of data.

The core principle of using machine learning in fraud detection is simple. The more data you feed, the more accurate the suggestions would be. However, machine learning doesn’t discount the role of humans in the process yet.

Human insight is still necessary to identify tricky situations. Machine learning makes it possible to filter and sort users based on their online behavior.

In other words, machine learning studies historical data to make predictions about the future. A trained machine-learning engine is much more efficient than humans in identifying specific behavior patterns linked to fraud.

Machine learning works 24/7

Another major benefit of machine learning systems is that they work 24/7. On top of being faster and more accurate than humans, machine learning provides complete security support around the clock. The ability to handle overload makes machine learning effective against online fraud.

Reacting to data breaches and thefts makes a huge difference in the overall ramifications of the cyberattack. Here again, machine learning proves to be more effective since it works 24/7 and can send alerts as and when it gets triggered. This is an often overlooked benefit of using machine learning in fraud detection.

Traditional fraud detection vs machine learning

Traditional fraud detection solutions have several key drawbacks.

  • It relies on human analysis and labor. The availability of relevant data science tools and experts is a problem for small to medium businesses.
  • Traditional fraud detection solutions are based on fixed rules. These rules are often not iterative and don’t improve with time.
  • Traditional fraud detection systems are prone to minor human errors. Even small errors can have a big effect on the performance of the fraud detection system.

Machine learning is preferred over traditional fraud detection systems for the above reasons.

How machine learning in fraud detection works

There are multiple steps through which machine learning is used for fraud detection. The first step is data collection. As we already mentioned, machine learning algorithms need copious amounts of data to learn patterns of fraudulent behavior.

Once the machine learning system is fed data, it segments the data into different categories. After segmenting the data, it extracts the key features from it. These key features relate to different behavior patterns associated with fraudsters.

In the next step, the machine learning system is trained to identify suspicious actions and flag them accordingly. It takes several factors into account, such as:

  • Customer’s location
  • Customer identity
  • Order
  • Network
  • Chosen payment method.

Once this stage is over, the machine learning system is ready to detect fraud.

Fraud detection machine learning systems will be different for different businesses. For example, an e-commerce company might look for certain patterns that a fintech company can ignore. Similarly, there would be common patterns of fintech fraud that financial companies cannot overlook.

The final machine learning model that a company receives is trained to identify behavioral patterns specific to its sector. Customizability is another advantage of machine learning over humans in fraud detection.

Types of machine learning models for fraud detection

There are two main types of machine learning models for fraud detection: supervised and unsupervised. Let’s find out more about the advantages and disadvantages of these models.

1. Supervised learning

Supervised learning is the more common machine learning model. It uses a binary system to label data good or bad. Labeling vast amounts of data makes supervised learning time and resource intensive. The drawback of supervised learning is that it’s based on historical data. If a certain type of fraud is not included in the historical dataset, the machine learning model will not be able to identify it.

2. Unsupervised learning

An unsupervised machine learning model continuously processes data and automatically labels it. Such models are dependent on historical data because they can simultaneously receive and label data. Unsupervised machine learning models are more adept at finding patterns and tracing suspicious behavior.

Apart from supervised and unsupervised learning, there are two other machine learning models. These are semi-supervised learning and reinforced learning.

3. Semi-supervised learning

Semi-supervised machine learning models lie between supervised and unsupervised learning models. It’s used when it’s impossible or excessively resource-intensive to label data accurately. Instead of labeling each item good or bad, semi-supervised learning stores data under broad parameters. Human intervention is frequently needed to make such models work accurately.

4. Reinforcement learning

Reinforcement learning enables computers to automatically identify desirable behavior in a given situation. In order to choose actions that reduce risks while maximizing rewards, it continuously learns from specific contexts. The model needs a reinforcement feedback signal to recognize behavioral patterns.

These are the four main models of machine learning in fraud detection. Each model is ideal for specific scenarios. At the same time, unsupervised learning models are more capable of self-sustenance than the other models.

Is machine learning the future of fraud detection?

If the present rate of growth in machine learning continues for the next decade, it can be the leading fraud detection technology across sectors. However, there are some challenges to using machine learning in fraud detection.

First, the availability of raw data continues to be a challenge for companies trying to adopt machine learning.

Second, it takes considerable time to create, train, and use a machine-learning model for fraud detection.

Many businesses and organizations are unwilling to dedicate that amount of time and resources to developing their machine-learning models.

While there are certain challenges to using machine learning in fraud detection, it’s more promising than the rest of the options. We can expect a trickle-down effect as more large companies start using machine learning for fraud detection. At present, small to medium businesses can access machine learning capabilities through third-party software only.

Machine Learning is the most promising of all tools to prevent fraud

The financial and e-commerce sectors are already using machine learning to prevent fraud and scams.

Going forward, we can expect more rapid adoption of machine learning in fraud detection. We hope this guide familiarizes you with the core ideas of using machine learning to detect fraud.

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Published on October 6, 2022 by Beatrice Stefanescu; modified on September 25, 2023. Filed under: , , , .

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