Making Use of Machine Learning in Fraud Detection Strategy: Tips & Tricks

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Digital threats keep making us feel unsafe. Some cases, like the infamous “crocodile of Wall Street,” strike us with extravaganza and scale, while others keep a low profile but make it up with quantity. However, unlike in 2020, when a record of 791,790 internet crime complaints was filed in the US alone, a 2022 report has shown that the global fraud attempt rate had decreased by nearly 25%. 

 

It seems like cybercriminals have become more cautious of fraud prevention measures. Can this drop be attributed to machine learning in fraud detection? After all, it’s much more effective than humans in automating fraud detection and putting suspicious activity on hold. Keep reading to discover how ML helps with fraud detection, how it works, and what limitations it has.

Positive effects of machine learning on fraud detection

Machine learning has proven to be widely helpful in healthcare, fintech, e-commerce, and government for optimizing operations, automating processes, and streamlining workflows. With quality data and well-trained models, applying ML for digital threat prevention is the next step in data security. For example, it can be used to create a fraud detection model to enhance identity proofing or identify suspicious patterns.

 

To fight digital fraud, vendors usually resort to rule-based or ML-based approaches. In rule-based online fraud detection (OFD) systems, algorithms compare actions against manually created fraud scenarios that rely exclusively on historical data. Such systems are too obvious for fraudsters, less change-adaptive than their tactics, and rife with loopholes.

 

However, fraud detection machine learning algorithms process large real-time and historical datasets with many variables. They use this data to find implicit and explicit correlations between user behavior and the possibility of fraudulent actions. While the rule-based approach results in multiple user verification steps that disrupt the user experience (UX), using machine learning in fraud detection allows businesses to run anti-fraud assessments without harming the UX.

 

But that’s not all. ML-based fraud detection algorithms have immense potential to improve your business while providing more efficient fraud protection. Here are some of them:

 

  • Reduced operational costs. Thanks to automation and accuracy in the ML-based approach, experts have to review fewer results. This leads to optimized labor and resource expenses while maintaining high performance and better reliability.
  • More effective payment fraud detection. Contrary to static rule-based scenarios, ML fraud detection algorithms can adapt to new behaviors and learn new patterns from input data.
  • Fewer false-positive results. Honest users get frustrated when they are suspected of being cybercriminals. For instance, if the activity identified by the rule-bases system is labeled as suspicious, the system will signal the risk analyst to check the transaction, contact the client, or even immediately freeze the account. But the ML-based approach is more flexible and smarter than that.
  • Real-time fraud detection. ML algorithms deal with real-time data, which means they can detect fraudulent patterns in action and alert the authorities before any real damage is done.
  • Improved large dataset processing. Accurate processing and interpretation of large datasets bring faster and more reliable results. ML-powered OFD systems automatically process data, be it historical or real-time, so the possibility of errors decreases.
  • Tailored, industry-specific algorithms. ML algorithms use either their native or trusted consortium data for model training. But going for business-native data allows developing specific fraud algorithms that alter their performance according to the changes in data.

 

These benefits translate into an improved detection strategy, fewer false interventions, better know-your-customer (KYC) policies, and more business insights. But how exactly does machine learning in fraud detection help achieve these advantages?

How machine learning helps businesses fight fraud

Now that you’re familiar with the benefits of machine learning in fraud detection, it’s a good idea to get to know how ML works and what techniques and algorithms help to reap those benefits. We’ll start with the basics.

How machine learning works in fraud detection

Machine learning models operate with large datasets to detect patterns in transactions. If they seem legitimate, the system allows the transactions. But if the patterns look suspicious, the transactions may very well be rejected.

 

With training datasets, the algorithm is taught to distinguish fraudulent patterns and the probability of fraud. As a result, engineers get a fraud detection ML model that flags suspicious activity for further review. 

 

Here’s a very simplified diagram of the process:

An example of how a fraud detection algorithm works to identify insurance claim fraud. Source

ML algorithms

You’re probably wondering about those algorithms we talk about. Well, there are three ML categories that differ in algorithms.

 

  • Supervised learning. In this category, the algorithms used for supervised learning are regression-based. They operate with historical data and perform as precisely as the training datasets allow them to. The input information obtains good/bad labels and is used for predictive analytics. Decision Trees, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes, various Lineare/Ridge, and Logistic Regressions are good examples of these algorithms.
  • Unsupervised learning. In unsupervised types, the algorithms use clustering to detect outliers or anomalies in cases with little to no transaction data. Contrary to supervised ones, these models find hidden structures and use not-so-related information to create patterns. The ML models continuously process and analyze the new data to modify their models based on the results. Algorithms used: Linear Vector Quantization (LVQ), K-Means, Histogram-Based Outlier Scoring (HBOS), one-class SVM, and more.
  • Reinforcement learning. Reinforcement algorithms detect the ideal behavior depending on the given context automatically. The environment and context are the major data sources for the algorithm to find the least risky and most rewarding actions within this context. Deep neural network (NN) hierarchical reinforcement is one of these algorithms.

Source

 

Data scientists use both supervised and unsupervised learning algorithms to teach models to detect fraudulent activity. Here are the most common ones:

 

  • Logistic regression: detects the probability of the event based on variables (phishing or credit card fraud)
  • Decision trees: applies various rules to split the data and verify it at each step (distinguishes legitimate behavior patterns from fraudulent ones)
  • Random forest: uses a set of decision trees to train a model with new random data to avoid overfitting
  • Neural network: detects the non-linear relationship between the data points
  • K-means: a cluster-based algorithm that groups the near-standing data points to evaluate feature distribution (detects malicious and abnormal activity)
  • One-class SVM: a context-based algorithm that identifies rarely occurring fraudulent activity and automatically flags it as suspicious
  • Local outlier factor (LOF): a cluster-based algorithm that evaluates the density of values and helps detect the outliers that can point to suspicious activity

 

In addition to those, ML engineers use face and speech recognition algorithms (voice stress analysis, computer vision, face reconstruction) and sentiment/feeling analysis (SentiCircles). 

Use cases of machine learning in fraud detection

One algorithm won’t be effective in beating fraud, which is why the ML engineers have to assess the desired business outcomes and functionality before choosing the right mix of technologies. Do you want to see some examples? Let’s take a look at some cases from e-commerce, gaming, and financial industries where ML algorithms were used to detect fraud.

Anti-fraud e-commerce systems 

With the COVID-19 pandemic, digital commerce is experiencing an unfortunate boom in fraud. Cybercriminals often guise themselves as online merchants to collect clients’ credit card information. Well, one French startup decided to add an anti-fraud system to prevent clients from shopping at scammy websites and show better, more reliable alternatives.

 

The extension’s algorithm analyzes the website’s content and URL data and marks it as green, orange, or red based on the findings. It provides customers with a full report on why it’s better to avoid shopping at this website (absence of security protocols, no CMS, suspicious ads, etc.) and offers trustworthy e-commerce alternatives right away. 

Fraud-prevention modules for the gaming industry

According to TransUnion, digital fraud rose to 52.2% between 2019 and 2021, with leisure, financial services, and gaming facing the largest increase in cybercrime. So our client, an established game production company, decided it needed system components to detect and prevent fraud and analyze large amounts of data while increasing monetization. 

 

Our data scientists used the historical preprocessed data that helped us identify the cluster of active users and analyze their gaming behavior and transaction-related activity. This allowed us to design and train the algorithm to detect suspicious patterns within in-game purchases. We could also identify when people used fake cards or created fake accounts and teams. When detected, they were sent to the database to train the model further. 

 

As a result, the client received an algorithm-powered module that helped the business deal with fraudsters. Plus, in 12 months, the client improved profits with the most promising groups thanks to the AI-powered module that analyzed in-game purchase sets and provided quality data for targeted marketing campaigns.

Machine learning in fraud detection for a cryptocurrency platform

Being the world’s sixth-largest cryptocurrency platform with over eight million customers, Luno needed a robust solution that would detect fraud while protecting honest customers. 

 

Initially, they started with a third-party fraud detection tool, but due to its low efficiency, the Luno tech team decided to develop an in-house system. They used Amazon SageMaker which supported ML frameworks and worked well with other Amazon Web Services (AWS) tools the company has already adopted. 

 

The system first learned to identify fraudulent behavior based on 47 data points indicating log-in locations, device, navigation data, and more. After a year of training and tuning, the six-person team developed a full-scale automation tool from the initial model. And their results were great: the in-house solution scored 94% in performance, while the third-party solution got 80%.

 

ML fraud detection looks pretty promising, doesn’t it? But you should know about the challenges of implementing it before investing.

Challenges of implementing machine learning models 

While using machine learning in fraud detection can improve the company’s ability to detect and deter wrongdoings, the deployment of this technology has its challenges. We’ve collected the most common ones. 

Overfitting or underfitting Both conditions lead to a model’s poor ability to generalize and predict the results. If the model has too much data to learn from or was trained for too long, it becomes applicable in too many cases (overfitting). And if the model doesn’t have enough data to learn from, it cannot capture the underlying trend (underfitting).
Challenging identification of suitable ML methods The combination of methods and algorithms will heavily depend on the complexity and output of the trained model, quality and availability of data, given problem, model accuracy, timeframe, etc. The choice of the wrong method will result in developing an algorithm that would not provide the expected solution to the given problem. For example, if the vendor uses supervised learning methods only, the algorithm will only detect the expected fraud patterns, leaving plenty of other potentially dangerous cases unattended. 
Dimensionality issue When a model has to consider too many features, the probability of errors increases. That’s because, in this case, the model will require more data, and more data comes with more noise which decreases the model’s accuracy.
Lack of quality training data Not all transactional data is suitable for training fraud detection models. If you want the model to perform well and provide accurate predictions, it should operate with up-to-date, relevant, structured, well-labeled data. Since the required amount of real data in finance is hard to get due to NDA and privacy protection policies, it will take time to retrieve synthetic data or augment the existing datasets. Plus, quality structuring and classifying data will take up extra time and costs.
System biases On the other hand, having all-quality or non-diversified data will lead to system biases as the chosen data will be considered the exemplary set, and everything that doesn’t fit will be overlooked. For example, if a face-recognition model is trained with the images of Caucasian males to define the sex of the fraudster, it will not consider the representatives of other races as the possible offenders. Neural networks, sophisticated data designs, and frequent model testing should be applied to combat the bias issue
Dominance of supervised learning In supervised learning, which is mostly classification-based, the expected problem answer is hidden in historical datasets, and the algorithm’s task is to find it in new data. The problem is that the data changes continuously, and not taking it into account will lead to creating an inaccurate model. If we use the supervised-only methods for fraud detection, the machine won’t be able to improve its algorithm.
Algorithms being considered “black boxes” In machine learning, the process between the input data and output result isn’t transparent. This limited understanding of the inner working processes leads to a limited ability to control them, deal with possible biases, and improve the algorithms further.
Lack of qualified teams A well-designed model with low bias levels and high accuracy can only be designed by skilled engineers, design architects, and data scientists that also have a profound understanding of the target market. In addition, they should use company-specific data to simulate the environment and test the model in a close-to-real-life infrastructure.

 

For now, machine learning isn’t an entirely flawless solution to help businesses detect cases of fraud. More than that, businesses need to understand that machine learning shouldn’t be the only fraud detection technology but rather an embedded part of a holistic anti-fraud system. Yet a carefully considered technology partnership can help you conquer ML and make the best of its algorithms.

The bottom line

As digital markets grow, they entail spikes of fraudulent activity, which cost companies millions in financial and reputational damage. And as fraud schemes become more sophisticated, so should the tools that help discover and prevent them. The use of machine learning in fraud detection helps create models that would complement the broader anti-fraud system and use new data to improve its performance automatically. 

However, as ML systems show limited performance in isolation, they require skilled engineers and data scientists to integrate them into online fraud detection systems. Unicsoft has the right talents to make it happen.

We focus on developing and integrating ML tools into OFD departments to give risk and fraud analysts insights into the ever-changing fraud patterns. Our solutions help detect suspicious activity accurately with fewer false positives while ensuring a smooth UX for trusted clients. Are you ready to improve the security of your business and shield your clients from fraud? Contact us today, and let’s talk about how Unicsoft can be of service.