Fraud Detection Using Machine Learning: Pros, Cons, and Use Cases

Machine learning for fraud detection

Fraud attacks have grown in sophistication. The concept behind using machine learning in fraud detection presupposes using algorithms that detect patterns in financial operations and decide whether a given transaction is fraudulent.

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With businesses moving online, fraud and abuse in online systems are on the rise. Preventing, detecting, and eliminating fraud are some of the most pressing current concerns across industries. PwC’s Global Economic Crime and Fraud Survey 2022 reports total losses of $42 billion as a result of fraud. Meanwhile, another study suggests that the global fraud detection and prevention market is expected to reach $92,3 billion by 2027, growing at a CAGR of 16,8% during 2021-2027.

So why, then, are companies turning to machine learning for fraud detection?

Machine learning for fraud detection is potentially the best way to overcome these pressures. With the increase in systems’ processing power and the progress in statistical modeling, ML-based fraud detection models can be built quickly and at scale.

Let’s take a closer look at the role of machine learning in fraud detection..

What are the benefits of machine learning fraud detection?

Before we delve into the details, let’s address the benefits of adopting machine learning for fraud detection. What should we expect?

Faster and more accurate detection

Machine learning can analyze current consumer patterns and transaction methods. It can evaluate these behaviors faster and more efficiently than any human analysis, allowing it to instantly spot deviations from typical behavior.

Large datasets yield better forecasts

Machine learning algorithms can handle enormous volumes of data, which results in better predictions. The more data a model is fed, the more it can learn and make better predictions. This way, machine learning enhances accuracy by removing human error in data recording or analysis from the equation.

Cost-effective detection technique

With ML, data can be analyzed in milliseconds, meaning that team members aren’t burdened with manual reviews and checks every time new data is received. This is great for firms that see seasonal highs and lows in traffic, checkouts or signups.

Check out the benefits of using ML for eCommerce described in our article

What are the disadvantages of fraud detection with machine learning?

Using machine learning for fraud detection allows you to detect irregular patterns in everyday transactions. However, as with any other technology, the fraud detection ML system has limitations.  Among these are positive errors, less control, and no human intelligence. Let’s take a closer look at each.

Positive errors

Machine learning models require a large amount of data if they are to be accurate. This data volume is fine for large enterprises, but for others it is a challenge to have enough data points to identify valid cause-and-effect correlations.

Without the necessary data, fraud detection machine learning algorithms may learn incorrect inferences and create false or irrelevant fraud evaluations.

Less control

Fraud detection machine learning models are employed to evaluate actions, behavior and activities. Initially, when the dataset is small, they are blind to data connections. As a result, the model may overlook a seemingly evident connection, such as a shared card between two accounts.

No human intelligence

It’s difficult to beat good old psychology when working out why a user’s activity is questionable. Even the most advanced technology cannot replace the expertise and judgment required to correctly filter and interpret data and evaluate the meaning of the risk score.

How does machine learning in fraud detection work?

Despite their proven effectiveness, ML-based fraud detection systems can be challenging to implement. Therefore, let us examine a few guidelines for streamlining their implementation while overcoming potential drawbacks.

Feeding data and extracting features

To detect fraud, a machine learning model must first collect data. The model analyzes all the data gathered and then segments it before extracting the required features from it.

Feature extraction will then be the next step. At this point, features describing good and fraudulent customer behavior are added. These features usually include, but are not limited to the following:

  • transaction value
  • product SKU
  • credit card type

Data relating to how the customers connect to the site could also be added:

  • VPN, proxy, or Tor usage
  • type of device
  • IP data

The list of investigated features can differ depending on the complexity of the fraud detection system.

Choosing a limit

When developing a fraud detection model, it is critical to establish a threshold. This threshold would determine the acceptance/rejection rate and the minimum requirements to trigger a response. It would thus represent a tradeoff between true positives (fraudsters blocked), false positives (genuine users blocked), and false negatives (fraudsters not blocked). The right balance largely depends on the risk level a business can absorb.

At this point, it might also be helpful to distinguish between the different machine learning models and algorithms for fraud detection. These include:

  • Supervised learning. In a supervised learning model all input information has to be classified as good or bad. A supervised learning model is based on predictive data analysis and is only as accurate as the training set provided. A significant drawback of the supervised model is that it cannot detect fraud that was not included in the historical data set from which it learned.
  • Unsupervised learning. An unsupervised learning model detects anomalous behavior in cases of scarce or unavailable transaction data. An unsupervised learning model continuously processes and analyzes new data and updates its models based on the findings. It learns to identify patterns and decide whether they are parts of legitimate or fraudulent ones. Deep learning in fraud detection is often associated with unsupervised learning algorithms.
  • Semi-supervised learning. Mostly used for cases where labeling information is either impossible or too expensive and requires human expertise. A semi-supervised algorithm for fraud detection in deep learning stores data about key group parameters even when group membership of the unlabeled data is unknown. It does so on the assumption that the discovered patterns can still be valuable.
  • Reinforcement learning. Allows machines to automatically detect ideal behavior within a specified context. It constantly learns from the environment to find actions that minimize risks and maximize rewards. A reinforcement feedback signal is required for the model to learn its behavior.

Testing on historical data

The next step involves creating a confusion matrix based on previous transactions over the selected time frame.

In machine learning, a confusion matrix, or error matrix, is a table layout that allows visualization of the performance of an algorithm. This allows, for example, for calculation accuracy over a specific date range.

This gives fraud managers complete control over their risk strategy, allowing them to decrease, monitor and test the results.

Meet CheckNFT – an BI-driven and ML-based service for analyzing non-fungible tokens

3 use cases of fraud detection with machine learning

Let’s take a look at how fraud detection and machine learning are being used in real-life scenarios.

Rental sites

Like all online businesses, rental platforms are prone to attacks aimed at stealing credit cards — which is why applying machine learning for fraud detection is common practice. A good real-life example is Airbnb, which uses ML models that have been trained on past examples of confirmed good and confirmed fraudulent behavior.

In some situations, Airbnb blocks actions outright, but in most cases they allow the user to satisfy an additional verification called a friction. A friction is anything that blocks a fraudster yet is easy for a ‘good’ user to get through.

The company trains the chargeback model using positive (fraud) and negative (non-fraud) examples from previous bookings to forecast the likelihood that a booking is fraudulent.

Video games

Video games present unique challenges when it comes to detecting fraud. An example is account sharing, which is common in games, leading to erratic and unpredictable spending that looks like fraud.

However, models that are trained using machine learning algorithms help solve this problem. For example, Amazon’s Fraud Detection Using Machine Learning solution helps developers run ML models that detect in-game fraud.

This solution enables automated transaction processing. The machine learning model detects potentially fraudulent activity and highlights it for further investigation. The solution also provides a dataset of credit card transactions contained in an Amazon S3 bucket, but it can be modified to include datasets for other sorts of fraudulent conduct too.

Anti-money laundering programs

Recent advances in machine learning are helping banks to significantly improve their anti-money laundering (AML) programs, particularly the transaction monitoring component of these programs.

According to McKinsey, many financial institutions use rule- and scenario-based tools or basic statistical approaches for transaction monitoring. Industry red flags, basic statistical indicators and expert judgment are the chief influences on these rules and thresholds. However, the rules frequently fail to capture the latest trends in money-laundering conduct.

For its part, though, fraud detection machine learning creates sophisticated algorithms using more detailed, behavior-indicative data. These algorithms are also more adaptable and constantly improving over time.

In theory, banks can apply ML across the entire AML value chain, but combining ML with other advanced algorithms is where banks can reap one of the most immediate and significant benefits in their AML efforts.

Does your business need to create its own machine learning model for fraud detection?

The greater our society’s digitalization, the greater the impact and frequency of cybersecurity attacks, with fraudsters steadily expanding the complexity of their criminal operations.

Machine learning is currently the most promising, and revolutionary, technique for helping businesses prevent the fraudulent operations that cause them ever-increasing losses each year.

When it comes to the future of your business, it’s always best to use your own customer data, as it will prove the most accurate for detecting fraud among your future customers. In addition, it prevents the model from being influenced by patterns in unrelated industries, resulting in more precise forecasts and enhanced performance.

Closing thoughts

ML models are proving to be very effective in dealing with fraudulent activities.  They can save you millions of dollars. However, alongside implementing machine learning fraud detection there is an ever more urgent need for custom software development services that are harder for fraudsters to manipulate.

The good news is that you can offload any work concerned with machine learning to us. By building a 100% personalized model for each of our clients, our ML specialists know how to produce smart ML algorithms that achieve supreme precision and redefine data understanding.

This means you can relax while we build fraud-averse workflows, manage risks smoothly, remove the element of human error or other anomalies, and allocate assets in line with data-driven plans.

By keeping up to date with the best and the newest in industry, we leave nothing to chance. Do get in touch today!

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