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Introduction to Fraud Detection Algorithms in Machine Learning

For years, fraud has been a major issue in sectors like banking, medical, insurance, and many others. Due to the increase in online transactions through different payment options, such as credit/debit cards, PhonePe, Gpay, Paytm, etc., fraudulent activities have also increased. Moreover, fraudsters or criminals have become very skilled in finding escapes so that they can loot more. Since no system is perfect and there is always a loophole them, it has become a challenging task to make a secure system for authentication and preventing customers from fraud. So, Fraud detection algorithms are very useful for preventing frauds.

Here comes Machine Learning which can be used for creating a fraud detection algorithm that helps in solving these real-world problems.

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Manual Review and Transaction Rules

Nowadays, Machine Learning in Artificial Intelligence resolves most of the issues that human beings find difficult to deal with. Previously, industries were using a rule-based approach for fraud detection. But due to the popularity and acceptance of Artificial Intelligence Tutorial, especially by students and Machine Learning in every industry vertical, organizations have moved from the ruled-based fraud detection to ML-based solutions.

Now, we will look at the rule-based fraud detection system and ML-based systems.

Rule-based Approach or Traditional Approach in Fraud Detection Algorithms

In the rule-based approach, the algorithms are written by fraud analysts. They are based on strict rules. If any changes have to be made for detecting a new fraud, then they are done manually either by making those changes in the already existing algorithms or by creating new algorithms. In this approach, with the increase in the number of customers and the data, human effort also increases. So, the rule-based approach is time-consuming and costly. Another drawback of this approach is that it is more likely to have false positives. This is an error condition where an output of a test specifies the existence of a particular condition that does not even exist. The output of a transaction depends upon the rules and guidelines made for training the algorithm for non-fraudulent transactions. So, for a fixed risk threshold, if a transaction is rejected where it should not be, it will generate a condition of high rates of false positives. This false-positive condition will result in losing genuine customers.

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ML-based Fraud Detection Algorithms

In the rule-based approach, the algorithms cannot recognize the hidden patterns. Since they are based on strict rules, they cannot predict fraud by going beyond these rules. But in real world, fraudsters are very skilled and can adopt new techniques every time to commit a crime. Therefore, there is a need for a system that can analyze patterns in data and predict and respond to new situations for which it is not trained or explicitly programmed.

Hence, we use Machine Learning for detecting fraud. Here, a machine tries to learn by itself and becomes better by experience. Also, it is an efficient way of detecting fraud because of its fast computing. It does not even require the guidance of a fraud analyst. It helps in reducing false positives for transactions as the patterns are detected by an automated system for streaming transactions that are in huge volume.

Now, we will look at the two most commonly used Machine Learning models for detecting fraud in transactions.

Supervised Learning Used in Fraud Detection Algorithms

Supervised Learning models are trained on tagged outputs. If a transaction occurs, it is tagged as either ‘fraud’ or ‘non-fraud.’ Large amounts of such tagged data are fed into the supervised learning model in order to train it in such a way that it gives a valid output. Also, the accuracy of the model’s output depends on how well-organized your data is.

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Unsupervised Learning Used in Fraud Detection Algorithm

Unsupervised learning models are built to detect unusual behavior in transactions which is not detected previously. Unsupervised learning models involve self-learning that helps in finding hidden patterns in transactions. In this type, the model tries to learn by itself, analyzes the available data, and tries to find the similarities and dissimilarities between the occurrences of transactions. This helps in detecting fraudulent activities.

So, both these models, supervised and unsupervised, can be used independently or in combination for detecting anomalies in transactions.

Need for the Fraud Detection Machine Learning Algorithms

Human beings always search for methods, tools, or techniques that reduce the human effort for performing a certain task efficiently. In Machine Learning, algorithms are designed in such a way that they try to learn by themselves using past experience. After learning from the past experience, the algorithms become quite capable of reacting and responding to conditions for which they are not explicitly programmed. So, Machine Learning helps a lot when it comes to fraud detection. It tries to identify hidden patterns that help in detecting a fraud which is not been previously recognized. Also, its computation is fast as compared to the traditional rule-based approaches.

Why do we use Machine Learning in Fraud Detection?

Here are some factors for why Machine Learning techniques are so popular and widely used in industries for detecting frauds:

Speed: Machine Learning is widely used because of its fast computation. It analyzes and processes data and extracts new patterns from it within no time. For human beings to evaluate the data, it will take a lot of time and evaluation time will increase with the amount of data.
Scalability: As more and more data is fed into the Machine Learning-based model, the model becomes more accurate and effective in prediction.
Efficiency: Machine Learning algorithms perform the redundant task of data analysis and try to find hidden patterns repetitively. Their efficiency is better in giving results in comparison with manual efforts. It avoids the occurrence of false positives which counts for its efficiency.

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How does a Machine Learning system work for Fraud Detection?

The below picture shows the basic structure of the working of fraud detection algorithms using Machine Learning:

Feeding Data: First, the data is fed into the model. The accuracy of the model depends on the amount of data on which it is trained, more data better the model performs.

For detecting frauds specific to a particular business, you need to input more and more amounts of data into your model. This will train your model in such a way that it detects fraud activities specific to your business perfectly.

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